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healed/canary_512_g4/args.json

@@ -0,0 +1,44 @@
+{
+  "student": "outputs/pruned/glean-0125inst-math-keep5",
+  "teacher": "allenai/OLMoE-1B-7B-0125-Instruct",
+  "dataset": "allenai/RLVR-MATH",
+  "dataset_sources": null,
+  "max_difficulty": null,
+  "teacher_device": "cuda:0",
+  "student_device": "cuda:1",
+  "lr": 3e-05,
+  "optimizer": "adamw8bit",
+  "weight_decay": 0.1,
+  "epochs": 4,
+  "prompts_per_step": 256,
+  "group_size": 4,
+  "rollout_batch": 64,
+  "micro_batch": 4,
+  "max_new_tokens": 512,
+  "max_prompt_len": 1024,
+  "warmup_steps": 5,
+  "max_grad_norm": 1.0,
+  "eval_every": 10,
+  "gsm8k_every": 5,
+  "gsm8k_n": 64,
+  "gsm8k_batch": 16,
+  "save_every": 25,
+  "out_dir": "outputs/healed/canary_512_g4",
+  "sweep": 5,
+  "wandb": false,
+  "wandb_project": "glean-heal",
+  "wandb_run_name": null,
+  "debug": false,
+  "resume_from": null,
+  "start_step": 0,
+  "no_grad_checkpointing": false,
+  "seed": 1223,
+  "rollout_engine": "vllm",
+  "vllm_gpu": "GPU-864c54df-0130-7780-e271-8a5551d1733f",
+  "vllm_port": 8377,
+  "vllm_refresh_every": 1,
+  "vllm_serve_bin": "vllm-plugin/.venv25/bin/python",
+  "vllm_gpu_mem_util": 0.85,
+  "vllm_refresh_mode": "reload",
+  "vllm_live_dir": "/dev/shm/glean_vllm_live"
+}

healed/canary_512_g4/train_log.jsonl

@@ -0,0 +1,7 @@
+{"step": 1, "epoch": 0, "reverse_kl": 0.30979594500512125, "tokens": 104839, "grad_norm": 4.75, "lr": 1.2e-05, "finish_rate": 0.461, "comp_len": 409.5, "t_rollout_s": 36.5, "t_step_s": 149.9, "t_refresh_s": 0.4, "mem_gb": 16.88}
+{"step": 1, "gsm8k_quick": 0.484375, "gsm8k_n": 64, "t_eval_s": 46.3}
+{"step": 2, "epoch": 0, "reverse_kl": 0.31237369027154915, "tokens": 97970, "grad_norm": 4.3125, "lr": 1.8e-05, "finish_rate": 0.586, "comp_len": 382.7, "t_rollout_s": 34.5, "t_step_s": 135.4, "t_refresh_s": 0.4, "mem_gb": 18.64}
+{"step": 3, "epoch": 0, "reverse_kl": 0.21176254702495886, "tokens": 100793, "grad_norm": 3.734375, "lr": 2.4e-05, "finish_rate": 0.52, "comp_len": 393.7, "t_rollout_s": 35.2, "t_step_s": 136.0, "t_refresh_s": 0.4, "mem_gb": 17.01}
+{"step": 4, "epoch": 0, "reverse_kl": 0.22271002861383504, "tokens": 81381, "grad_norm": 4.75, "lr": 3e-05, "finish_rate": 0.703, "comp_len": 317.9, "t_rollout_s": 30.9, "t_step_s": 126.4, "t_refresh_s": 0.4, "mem_gb": 16.89}
+{"step": 5, "epoch": 0, "reverse_kl": 0.19559442095853996, "tokens": 91708, "grad_norm": 2.578125, "lr": 3e-05, "finish_rate": 0.629, "comp_len": 358.2, "t_rollout_s": 33.8, "t_step_s": 120.9, "t_refresh_s": 0.0, "mem_gb": 17.05}
+{"step": 5, "gsm8k_quick": 0.453125, "gsm8k_n": 64, "t_eval_s": 33.7}

healed/canary_512_g4/vllm_server.log

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+(EngineCore pid=313432) WARNING 07-12 09:48:00 [layerwise.py:264] VllmVariableOlmoeMoE: Failed to load weights
+(EngineCore pid=313432) WARNING 07-12 09:48:00 [layerwise.py:264] VllmVariableOlmoeMoE: Failed to load weights
+(EngineCore pid=313432) WARNING 07-12 09:48:00 [layerwise.py:264] VllmVariableOlmoeMoE: Failed to load weights
+(EngineCore pid=313432) WARNING 07-12 09:48:00 [layerwise.py:264] VllmVariableOlmoeMoE: Failed to load weights
+(EngineCore pid=313432) WARNING 07-12 09:48:00 [layerwise.py:264] VllmVariableOlmoeMoE: Failed to load weights
+(EngineCore pid=313432) WARNING 07-12 09:48:00 [layerwise.py:264] VllmVariableOlmoeMoE: Failed to load weights
+(EngineCore pid=313432) WARNING 07-12 09:48:00 [layerwise.py:264] VllmVariableOlmoeMoE: Failed to load weights
+(EngineCore pid=313432) WARNING 07-12 09:48:00 [layerwise.py:264] VllmVariableOlmoeMoE: Failed to load weights
+(EngineCore pid=313432) WARNING 07-12 09:48:00 [layerwise.py:264] VllmVariableOlmoeMoE: Failed to load weights
+(EngineCore pid=313432) WARNING 07-12 09:48:00 [layerwise.py:264] VllmVariableOlmoeMoE: Failed to load weights
+(EngineCore pid=313432) WARNING 07-12 09:48:00 [layerwise.py:264] VllmVariableOlmoeMoE: Failed to load weights
+(EngineCore pid=313432) WARNING 07-12 09:48:00 [layerwise.py:264] VllmVariableOlmoeMoE: Failed to load weights
+(EngineCore pid=313432) WARNING 07-12 09:48:00 [layerwise.py:264] VllmVariableOlmoeMoE: Failed to load weights
+(EngineCore pid=313432) WARNING 07-12 09:48:00 [layerwise.py:264] VllmVariableOlmoeMoE: Failed to load weights
+(EngineCore pid=313432) WARNING 07-12 09:48:00 [layerwise.py:264] VllmVariableOlmoeMoE: Failed to load weights
+(EngineCore pid=313432) INFO 07-12 09:48:00 [gpu_model_runner.py:5506] Reloading and processing weights took 0.38 seconds
+(APIServer pid=313310) INFO:     127.0.0.1:59108 - "POST /collective_rpc HTTP/1.1" 200 OK
+(APIServer pid=313310) INFO 07-12 09:48:00 [api_router.py:38] Resetting prefix cache...
+(EngineCore pid=313432) INFO 07-12 09:48:00 [block_pool.py:685] Successfully reset prefix cache
+(APIServer pid=313310) INFO:     127.0.0.1:59112 - "POST /reset_prefix_cache HTTP/1.1" 200 OK
+(APIServer pid=313310) INFO 07-12 09:48:54 [loggers.py:273] Engine 000: Avg prompt throughput: 722.2 tokens/s, Avg generation throughput: 1657.4 tokens/s, Running: 255 reqs, Waiting: 0 reqs, GPU KV cache usage: 24.1%, Prefix cache hit rate: 95.3%
+(APIServer pid=313310) INFO 07-12 09:49:04 [loggers.py:273] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 3331.0 tokens/s, Running: 210 reqs, Waiting: 0 reqs, GPU KV cache usage: 49.4%, Prefix cache hit rate: 95.3%
+(APIServer pid=313310) INFO 07-12 09:49:14 [loggers.py:273] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 2774.3 tokens/s, Running: 160 reqs, Waiting: 0 reqs, GPU KV cache usage: 61.4%, Prefix cache hit rate: 95.3%
+(APIServer pid=313310) INFO:     127.0.0.1:34838 - "POST /v1/completions HTTP/1.1" 200 OK
+(APIServer pid=313310) INFO 07-12 09:49:24 [loggers.py:273] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 2032.8 tokens/s, Running: 0 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.0%, Prefix cache hit rate: 95.3%
+(APIServer pid=313310) INFO 07-12 09:49:34 [loggers.py:273] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 0 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.0%, Prefix cache hit rate: 95.3%
+(EngineCore pid=313432) INFO 07-12 09:51:04 [weight_utils.py:849] Filesystem type for checkpoints: TMPFS. Checkpoint size: 6.89 GiB. Available RAM: 81.16 GiB.
+(EngineCore pid=313432) 
+(EngineCore pid=313432) 
+(EngineCore pid=313432) 
+(EngineCore pid=313432) 
+(EngineCore pid=313432) 
+(EngineCore pid=313432) WARNING 07-12 09:51:04 [layerwise.py:264] RotaryEmbedding: Failed to load weights
+(EngineCore pid=313432) WARNING 07-12 09:51:04 [layerwise.py:264] VllmVariableOlmoeMoE: Failed to load weights
+(EngineCore pid=313432) WARNING 07-12 09:51:04 [layerwise.py:264] VllmVariableOlmoeMoE: Failed to load weights
+(EngineCore pid=313432) WARNING 07-12 09:51:04 [layerwise.py:264] VllmVariableOlmoeMoE: Failed to load weights
+(EngineCore pid=313432) WARNING 07-12 09:51:04 [layerwise.py:264] VllmVariableOlmoeMoE: Failed to load weights
+(EngineCore pid=313432) WARNING 07-12 09:51:04 [layerwise.py:264] VllmVariableOlmoeMoE: Failed to load weights
+(EngineCore pid=313432) WARNING 07-12 09:51:04 [layerwise.py:264] VllmVariableOlmoeMoE: Failed to load weights
+(EngineCore pid=313432) WARNING 07-12 09:51:04 [layerwise.py:264] VllmVariableOlmoeMoE: Failed to load weights
+(EngineCore pid=313432) WARNING 07-12 09:51:04 [layerwise.py:264] VllmVariableOlmoeMoE: Failed to load weights
+(EngineCore pid=313432) WARNING 07-12 09:51:04 [layerwise.py:264] VllmVariableOlmoeMoE: Failed to load weights
+(EngineCore pid=313432) WARNING 07-12 09:51:04 [layerwise.py:264] VllmVariableOlmoeMoE: Failed to load weights
+(EngineCore pid=313432) WARNING 07-12 09:51:04 [layerwise.py:264] VllmVariableOlmoeMoE: Failed to load weights
+(EngineCore pid=313432) WARNING 07-12 09:51:04 [layerwise.py:264] VllmVariableOlmoeMoE: Failed to load weights
+(EngineCore pid=313432) WARNING 07-12 09:51:04 [layerwise.py:264] VllmVariableOlmoeMoE: Failed to load weights
+(EngineCore pid=313432) WARNING 07-12 09:51:04 [layerwise.py:264] VllmVariableOlmoeMoE: Failed to load weights
+(EngineCore pid=313432) WARNING 07-12 09:51:04 [layerwise.py:264] VllmVariableOlmoeMoE: Failed to load weights
+(EngineCore pid=313432) WARNING 07-12 09:51:04 [layerwise.py:264] VllmVariableOlmoeMoE: Failed to load weights
+(EngineCore pid=313432) INFO 07-12 09:51:04 [gpu_model_runner.py:5506] Reloading and processing weights took 0.36 seconds
+(APIServer pid=313310) INFO:     127.0.0.1:34692 - "POST /collective_rpc HTTP/1.1" 200 OK
+(APIServer pid=313310) INFO 07-12 09:51:04 [api_router.py:38] Resetting prefix cache...
+(EngineCore pid=313432) INFO 07-12 09:51:04 [block_pool.py:685] Successfully reset prefix cache
+(APIServer pid=313310) INFO:     127.0.0.1:34700 - "POST /reset_prefix_cache HTTP/1.1" 200 OK
+(APIServer pid=313310) INFO 07-12 09:51:14 [loggers.py:273] Engine 000: Avg prompt throughput: 708.4 tokens/s, Avg generation throughput: 3214.8 tokens/s, Running: 236 reqs, Waiting: 0 reqs, GPU KV cache usage: 37.3%, Prefix cache hit rate: 95.4%
+(APIServer pid=313310) INFO 07-12 09:51:24 [loggers.py:273] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 3086.4 tokens/s, Running: 193 reqs, Waiting: 0 reqs, GPU KV cache usage: 57.4%, Prefix cache hit rate: 95.4%
+(APIServer pid=313310) INFO 07-12 09:51:34 [loggers.py:273] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 2572.9 tokens/s, Running: 147 reqs, Waiting: 0 reqs, GPU KV cache usage: 65.6%, Prefix cache hit rate: 95.4%
+(APIServer pid=313310) INFO:     127.0.0.1:34708 - "POST /v1/completions HTTP/1.1" 200 OK
+(APIServer pid=313310) INFO 07-12 09:51:44 [loggers.py:273] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 1204.1 tokens/s, Running: 0 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.0%, Prefix cache hit rate: 95.4%
+(APIServer pid=313310) INFO 07-12 09:51:54 [loggers.py:273] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 0 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.0%, Prefix cache hit rate: 95.4%
+(EngineCore pid=313432) INFO 07-12 09:53:20 [weight_utils.py:849] Filesystem type for checkpoints: TMPFS. Checkpoint size: 6.89 GiB. Available RAM: 80.95 GiB.
+(EngineCore pid=313432) 
+(EngineCore pid=313432) 
+(EngineCore pid=313432) 
+(EngineCore pid=313432) 
+(EngineCore pid=313432) 
+(EngineCore pid=313432) WARNING 07-12 09:53:20 [layerwise.py:264] RotaryEmbedding: Failed to load weights
+(EngineCore pid=313432) WARNING 07-12 09:53:20 [layerwise.py:264] VllmVariableOlmoeMoE: Failed to load weights
+(EngineCore pid=313432) WARNING 07-12 09:53:20 [layerwise.py:264] VllmVariableOlmoeMoE: Failed to load weights
+(EngineCore pid=313432) WARNING 07-12 09:53:20 [layerwise.py:264] VllmVariableOlmoeMoE: Failed to load weights
+(EngineCore pid=313432) WARNING 07-12 09:53:20 [layerwise.py:264] VllmVariableOlmoeMoE: Failed to load weights
+(EngineCore pid=313432) WARNING 07-12 09:53:20 [layerwise.py:264] VllmVariableOlmoeMoE: Failed to load weights
+(EngineCore pid=313432) WARNING 07-12 09:53:20 [layerwise.py:264] VllmVariableOlmoeMoE: Failed to load weights
+(EngineCore pid=313432) WARNING 07-12 09:53:20 [layerwise.py:264] VllmVariableOlmoeMoE: Failed to load weights
+(EngineCore pid=313432) WARNING 07-12 09:53:20 [layerwise.py:264] VllmVariableOlmoeMoE: Failed to load weights
+(EngineCore pid=313432) WARNING 07-12 09:53:20 [layerwise.py:264] VllmVariableOlmoeMoE: Failed to load weights
+(EngineCore pid=313432) WARNING 07-12 09:53:20 [layerwise.py:264] VllmVariableOlmoeMoE: Failed to load weights
+(EngineCore pid=313432) WARNING 07-12 09:53:20 [layerwise.py:264] VllmVariableOlmoeMoE: Failed to load weights
+(EngineCore pid=313432) WARNING 07-12 09:53:20 [layerwise.py:264] VllmVariableOlmoeMoE: Failed to load weights
+(EngineCore pid=313432) WARNING 07-12 09:53:20 [layerwise.py:264] VllmVariableOlmoeMoE: Failed to load weights
+(EngineCore pid=313432) WARNING 07-12 09:53:20 [layerwise.py:264] VllmVariableOlmoeMoE: Failed to load weights
+(EngineCore pid=313432) WARNING 07-12 09:53:20 [layerwise.py:264] VllmVariableOlmoeMoE: Failed to load weights
+(EngineCore pid=313432) WARNING 07-12 09:53:20 [layerwise.py:264] VllmVariableOlmoeMoE: Failed to load weights
+(EngineCore pid=313432) INFO 07-12 09:53:20 [gpu_model_runner.py:5506] Reloading and processing weights took 0.37 seconds
+(APIServer pid=313310) INFO:     127.0.0.1:46922 - "POST /collective_rpc HTTP/1.1" 200 OK
+(APIServer pid=313310) INFO 07-12 09:53:20 [api_router.py:38] Resetting prefix cache...
+(EngineCore pid=313432) INFO 07-12 09:53:20 [block_pool.py:685] Successfully reset prefix cache
+(APIServer pid=313310) INFO:     127.0.0.1:46934 - "POST /reset_prefix_cache HTTP/1.1" 200 OK
+(APIServer pid=313310) INFO 07-12 09:53:24 [loggers.py:273] Engine 000: Avg prompt throughput: 653.6 tokens/s, Avg generation throughput: 1152.1 tokens/s, Running: 253 reqs, Waiting: 0 reqs, GPU KV cache usage: 18.9%, Prefix cache hit rate: 95.7%
+(APIServer pid=313310) INFO 07-12 09:53:34 [loggers.py:273] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 3232.5 tokens/s, Running: 175 reqs, Waiting: 0 reqs, GPU KV cache usage: 38.9%, Prefix cache hit rate: 95.7%
+(APIServer pid=313310) INFO 07-12 09:53:44 [loggers.py:273] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 2418.4 tokens/s, Running: 112 reqs, Waiting: 0 reqs, GPU KV cache usage: 44.4%, Prefix cache hit rate: 95.7%
+(APIServer pid=313310) INFO:     127.0.0.1:46942 - "POST /v1/completions HTTP/1.1" 200 OK
+(APIServer pid=313310) INFO 07-12 09:53:54 [loggers.py:273] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 1334.4 tokens/s, Running: 0 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.0%, Prefix cache hit rate: 95.7%
+(APIServer pid=313310) INFO 07-12 09:54:04 [loggers.py:273] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 0 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.0%, Prefix cache hit rate: 95.7%
+(EngineCore pid=313432) INFO 07-12 09:55:26 [weight_utils.py:849] Filesystem type for checkpoints: TMPFS. Checkpoint size: 6.89 GiB. Available RAM: 80.88 GiB.
+(EngineCore pid=313432) 
+(EngineCore pid=313432) 
+(EngineCore pid=313432) 
+(EngineCore pid=313432) 
+(EngineCore pid=313432) 
+(EngineCore pid=313432) WARNING 07-12 09:55:27 [layerwise.py:264] RotaryEmbedding: Failed to load weights
+(EngineCore pid=313432) WARNING 07-12 09:55:27 [layerwise.py:264] VllmVariableOlmoeMoE: Failed to load weights
+(EngineCore pid=313432) WARNING 07-12 09:55:27 [layerwise.py:264] VllmVariableOlmoeMoE: Failed to load weights
+(EngineCore pid=313432) WARNING 07-12 09:55:27 [layerwise.py:264] VllmVariableOlmoeMoE: Failed to load weights
+(EngineCore pid=313432) WARNING 07-12 09:55:27 [layerwise.py:264] VllmVariableOlmoeMoE: Failed to load weights
+(EngineCore pid=313432) WARNING 07-12 09:55:27 [layerwise.py:264] VllmVariableOlmoeMoE: Failed to load weights
+(EngineCore pid=313432) WARNING 07-12 09:55:27 [layerwise.py:264] VllmVariableOlmoeMoE: Failed to load weights
+(EngineCore pid=313432) WARNING 07-12 09:55:27 [layerwise.py:264] VllmVariableOlmoeMoE: Failed to load weights
+(EngineCore pid=313432) WARNING 07-12 09:55:27 [layerwise.py:264] VllmVariableOlmoeMoE: Failed to load weights
+(EngineCore pid=313432) WARNING 07-12 09:55:27 [layerwise.py:264] VllmVariableOlmoeMoE: Failed to load weights
+(EngineCore pid=313432) WARNING 07-12 09:55:27 [layerwise.py:264] VllmVariableOlmoeMoE: Failed to load weights
+(EngineCore pid=313432) WARNING 07-12 09:55:27 [layerwise.py:264] VllmVariableOlmoeMoE: Failed to load weights
+(EngineCore pid=313432) WARNING 07-12 09:55:27 [layerwise.py:264] VllmVariableOlmoeMoE: Failed to load weights
+(EngineCore pid=313432) WARNING 07-12 09:55:27 [layerwise.py:264] VllmVariableOlmoeMoE: Failed to load weights
+(EngineCore pid=313432) WARNING 07-12 09:55:27 [layerwise.py:264] VllmVariableOlmoeMoE: Failed to load weights
+(EngineCore pid=313432) WARNING 07-12 09:55:27 [layerwise.py:264] VllmVariableOlmoeMoE: Failed to load weights
+(EngineCore pid=313432) WARNING 07-12 09:55:27 [layerwise.py:264] VllmVariableOlmoeMoE: Failed to load weights
+(EngineCore pid=313432) INFO 07-12 09:55:27 [gpu_model_runner.py:5506] Reloading and processing weights took 0.36 seconds
+(APIServer pid=313310) INFO:     127.0.0.1:60246 - "POST /collective_rpc HTTP/1.1" 200 OK
+(APIServer pid=313310) INFO 07-12 09:55:27 [api_router.py:38] Resetting prefix cache...
+(EngineCore pid=313432) INFO 07-12 09:55:27 [block_pool.py:685] Successfully reset prefix cache
+(APIServer pid=313310) INFO:     127.0.0.1:60248 - "POST /reset_prefix_cache HTTP/1.1" 200 OK
+(APIServer pid=313310) INFO 07-12 09:55:34 [loggers.py:273] Engine 000: Avg prompt throughput: 867.9 tokens/s, Avg generation throughput: 2331.0 tokens/s, Running: 238 reqs, Waiting: 0 reqs, GPU KV cache usage: 30.9%, Prefix cache hit rate: 94.6%
+(APIServer pid=313310) INFO 07-12 09:55:44 [loggers.py:273] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 3106.6 tokens/s, Running: 182 reqs, Waiting: 0 reqs, GPU KV cache usage: 49.8%, Prefix cache hit rate: 94.6%
+(APIServer pid=313310) INFO 07-12 09:55:54 [loggers.py:273] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 2462.0 tokens/s, Running: 127 reqs, Waiting: 0 reqs, GPU KV cache usage: 55.3%, Prefix cache hit rate: 94.6%
+(APIServer pid=313310) INFO:     127.0.0.1:60262 - "POST /v1/completions HTTP/1.1" 200 OK
+(APIServer pid=313310) INFO 07-12 09:56:04 [loggers.py:273] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 1270.7 tokens/s, Running: 0 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.0%, Prefix cache hit rate: 94.6%
+(APIServer pid=313310) INFO 07-12 09:56:14 [loggers.py:273] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 0 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.0%, Prefix cache hit rate: 94.6%
+(EngineCore pid=313432) INFO 07-12 09:58:17 [core.py:1214] [shutdown] EngineCore: trigger received signal=SIGTERM
+(APIServer pid=313310) INFO 07-12 09:58:17 [launcher.py:100] [shutdown] API server: shutdown triggered
+(APIServer pid=313310) INFO 07-12 09:58:17 [launcher.py:116] [shutdown] API server: stopping engine client mode=abort timeout=0s
+(EngineCore pid=313432) INFO 07-12 09:58:17 [core.py:1333] [shutdown] EngineCore: start mode=abort timeout=0s
+(EngineCore pid=313432) INFO 07-12 09:58:17 [core.py:1364] [shutdown] EngineCore: request processing complete; starting resource teardown
+(EngineCore pid=313432) INFO 07-12 09:58:17 [core.py:1227] [shutdown] EngineCore: exiting busy loop
+(APIServer pid=313310) INFO 07-12 09:58:17 [core_client.py:655] [shutdown] MPClient: start timeout=0s
+(APIServer pid=313310) INFO 07-12 09:58:17 [core_client.py:657] [shutdown] MPClient: stopping engine manager
+(APIServer pid=313310) WARNING 07-12 09:58:17 [utils.py:626] [shutdown] Process manager: force killing remaining processes count=1
+(APIServer pid=313310) INFO 07-12 09:58:17 [core_client.py:659] [shutdown] MPClient: engine manager stopped
+(APIServer pid=313310) INFO 07-12 09:58:17 [core_client.py:660] [shutdown] MPClient: cleaning up background resources
+(APIServer pid=313310) INFO 07-12 09:58:17 [core_client.py:662] [shutdown] MPClient: complete
+(APIServer pid=313310) INFO 07-12 09:58:17 [launcher.py:125] [shutdown] API server: engine client stopped
+(APIServer pid=313310) INFO 07-12 09:58:17 [launcher.py:128] [shutdown] API server: signalling HTTP server shutdown
+(APIServer pid=313310) INFO 07-12 09:58:17 [launcher.py:149] [shutdown] API server: shutting down FastAPI HTTP server
+(APIServer pid=313310) INFO:     Shutting down
+(APIServer pid=313310) INFO:     Waiting for application shutdown.
+(APIServer pid=313310) INFO:     Application shutdown complete.
+/home/henry/.local/share/uv/python/cpython-3.12.12-linux-x86_64-gnu/lib/python3.12/multiprocessing/resource_tracker.py:279: UserWarning: resource_tracker: There appear to be 1 leaked semaphore objects to clean up at shutdown
+  warnings.warn('resource_tracker: There appear to be %d '

healed/grid_general/gengrid.log

@@ -0,0 +1,160 @@
+2026-07-17T12:49:16-07:00 ###### GENERAL HEAL GRID START ######
+2026-07-17T12:49:16-07:00 === RUNG keep-25 on 3 GPUs (0 1 2), 9 cells ===
+2026-07-17T12:49:16-07:00 HEAL glean_keep25_s1224 on GPU 0
+2026-07-17T12:51:46-07:00 HEAL glean_keep25_s1225 on GPU 1
+2026-07-17T12:54:16-07:00 HEAL glean_keep25_s1226 on GPU 2
+2026-07-17T14:37:21-07:00 EVAL glean_keep25_s1225 on GPU 1 (port 8421)
+2026-07-17T14:38:49-07:00 EVAL glean_keep25_s1226 on GPU 2 (port 8422)
+2026-07-17T14:46:16-07:00 EVAL glean_keep25_s1224 on GPU 0 (port 8420)
+2026-07-17T14:54:32-07:00 glean_keep25_s1225 done -> outputs/evals/general_suite/healed/glean_keep25_s1225/student/results_2026-07-17T14-54-29.755565.json
+2026-07-17T14:54:32-07:00 HEAL reap_keep25_s1225 on GPU 1
+2026-07-17T14:54:47-07:00 glean_keep25_s1226 done -> outputs/evals/general_suite/healed/glean_keep25_s1226/student/results_2026-07-17T14-54-44.918246.json
+2026-07-17T14:54:47-07:00 HEAL reap_keep25_s1226 on GPU 2
+2026-07-17T15:01:02-07:00 glean_keep25_s1224 done -> outputs/evals/general_suite/healed/glean_keep25_s1224/student/results_2026-07-17T14-56-42.779609.json
+2026-07-17T15:01:02-07:00 HEAL reap_keep25_s1224 on GPU 0
+2026-07-17T17:15:39-07:00 EVAL reap_keep25_s1226 on GPU 2 (port 8422)
+2026-07-17T17:17:04-07:00 EVAL reap_keep25_s1225 on GPU 1 (port 8421)
+2026-07-17T17:27:41-07:00 reap_keep25_s1226 done -> outputs/evals/general_suite/healed/reap_keep25_s1226/student/results_2026-07-17T17-27-38.590201.json
+2026-07-17T17:27:41-07:00 HEAL uniform_keep25_s1226 on GPU 2
+2026-07-17T17:29:13-07:00 reap_keep25_s1225 done -> outputs/evals/general_suite/healed/reap_keep25_s1225/student/results_2026-07-17T17-29-11.051064.json
+2026-07-17T17:29:13-07:00 HEAL uniform_keep25_s1225 on GPU 1
+2026-07-17T17:31:30-07:00 EVAL reap_keep25_s1224 on GPU 0 (port 8420)
+2026-07-17T17:45:54-07:00 reap_keep25_s1224 done -> outputs/evals/general_suite/healed/reap_keep25_s1224/student/results_2026-07-17T17-45-52.249309.json
+2026-07-17T17:45:54-07:00 HEAL uniform_keep25_s1224 on GPU 0
+2026-07-17T19:01:19-07:00 EVAL uniform_keep25_s1226 on GPU 2 (port 8422)
+2026-07-17T19:03:56-07:00 EVAL uniform_keep25_s1225 on GPU 1 (port 8421)
+2026-07-17T19:16:25-07:00 uniform_keep25_s1226 done -> outputs/evals/general_suite/healed/uniform_keep25_s1226/student/results_2026-07-17T19-16-23.025416.json
+2026-07-17T19:16:25-07:00 lane GPU2 (rung 25) complete
+2026-07-17T19:19:15-07:00 uniform_keep25_s1225 done -> outputs/evals/general_suite/healed/uniform_keep25_s1225/student/results_2026-07-17T19-19-12.985404.json
+2026-07-17T19:19:15-07:00 lane GPU1 (rung 25) complete
+2026-07-17T19:34:39-07:00 EVAL uniform_keep25_s1224 on GPU 0 (port 8420)
+2026-07-18T07:28:17-07:00 ###### GENERAL HEAL GRID START ######
+2026-07-18T07:28:17-07:00 === RUNG keep-25 on 3 GPUs (0 1 2), 9 cells ===
+2026-07-18T07:28:17-07:00 glean_keep25_s1224 already evaled, skip
+2026-07-18T07:28:17-07:00 reap_keep25_s1224 already evaled, skip
+2026-07-18T07:28:17-07:00 uniform_keep25_s1224 already evaled, skip
+2026-07-18T07:28:17-07:00 lane GPU0 (rung 25) complete
+2026-07-18T07:30:47-07:00 glean_keep25_s1225 already evaled, skip
+2026-07-18T07:30:47-07:00 reap_keep25_s1225 already evaled, skip
+2026-07-18T07:30:47-07:00 uniform_keep25_s1225 already evaled, skip
+2026-07-18T07:30:47-07:00 lane GPU1 (rung 25) complete
+2026-07-18T07:33:17-07:00 glean_keep25_s1226 already evaled, skip
+2026-07-18T07:33:17-07:00 reap_keep25_s1226 already evaled, skip
+2026-07-18T07:33:17-07:00 uniform_keep25_s1226 already evaled, skip
+2026-07-18T07:33:17-07:00 lane GPU2 (rung 25) complete
+2026-07-18T07:35:47-07:00 === RUNG keep-25 COMPLETE ===
+2026-07-18T07:35:47-07:00 === RUNG keep-50 on 3 GPUs (0 1 2), 9 cells ===
+2026-07-18T07:35:47-07:00 HEAL glean_keep50_s1224 on GPU 0
+2026-07-18T07:38:17-07:00 HEAL glean_keep50_s1225 on GPU 1
+2026-07-18T07:40:47-07:00 HEAL glean_keep50_s1226 on GPU 2
+2026-07-18T09:48:07-07:00 EVAL glean_keep50_s1225 on GPU 1 (port 8421)
+2026-07-18T09:49:18-07:00 EVAL glean_keep50_s1226 on GPU 2 (port 8422)
+2026-07-18T10:02:01-07:00 EVAL glean_keep50_s1224 on GPU 0 (port 8420)
+2026-07-18T10:02:49-07:00 glean_keep50_s1225 done -> outputs/evals/general_suite/healed/glean_keep50_s1225/student/results_2026-07-18T09-58-04.824177.json
+2026-07-18T10:02:49-07:00 HEAL reap_keep50_s1225 on GPU 1
+2026-07-18T10:03:36-07:00 glean_keep50_s1226 done -> outputs/evals/general_suite/healed/glean_keep50_s1226/student/results_2026-07-18T09-59-02.269145.json
+2026-07-18T10:03:36-07:00 HEAL reap_keep50_s1226 on GPU 2
+2026-07-18T10:15:23-07:00 EVAL glean_keep50_s1224 step100 on GPU 0 (port 8420)
+2026-07-18T10:29:22-07:00 glean_keep50_s1224 done -> outputs/evals/general_suite/healed/glean_keep50_s1224/student/results_2026-07-18T10-11-54.989656.json
+2026-07-18T10:29:22-07:00 HEAL reap_keep50_s1224 on GPU 0
+2026-07-18T12:35:16-07:00 EVAL reap_keep50_s1226 on GPU 2 (port 8422)
+2026-07-18T12:36:25-07:00 EVAL reap_keep50_s1225 on GPU 1 (port 8421)
+2026-07-18T12:45:14-07:00 reap_keep50_s1226 done -> outputs/evals/general_suite/healed/reap_keep50_s1226/student/results_2026-07-18T12-41-23.273535.json
+2026-07-18T12:45:14-07:00 HEAL uniform_keep50_s1226 on GPU 2
+2026-07-18T12:46:34-07:00 reap_keep50_s1225 done -> outputs/evals/general_suite/healed/reap_keep50_s1225/student/results_2026-07-18T12-42-34.448591.json
+2026-07-18T12:46:34-07:00 HEAL uniform_keep50_s1225 on GPU 1
+2026-07-18T13:19:55-07:00 EVAL reap_keep50_s1224 on GPU 0 (port 8420)
+2026-07-18T13:32:15-07:00 EVAL reap_keep50_s1224 step100 on GPU 0 (port 8420)
+2026-07-18T13:44:07-07:00 reap_keep50_s1224 done -> outputs/evals/general_suite/healed/reap_keep50_s1224/student/results_2026-07-18T13-32-13.179265.json
+2026-07-18T13:44:07-07:00 HEAL uniform_keep50_s1224 on GPU 0
+2026-07-18T14:45:53-07:00 EVAL uniform_keep50_s1226 on GPU 2 (port 8422)
+2026-07-18T14:48:15-07:00 EVAL uniform_keep50_s1225 on GPU 1 (port 8421)
+2026-07-18T14:58:35-07:00 uniform_keep50_s1226 done -> outputs/evals/general_suite/healed/uniform_keep50_s1226/student/results_2026-07-18T14-58-32.761745.json
+2026-07-18T14:58:35-07:00 lane GPU2 (rung 50) complete
+2026-07-18T15:01:22-07:00 uniform_keep50_s1225 done -> outputs/evals/general_suite/healed/uniform_keep50_s1225/student/results_2026-07-18T14-57-45.707275.json
+2026-07-18T15:01:22-07:00 lane GPU1 (rung 50) complete
+2026-07-18T15:57:10-07:00 EVAL uniform_keep50_s1224 on GPU 0 (port 8420)
+2026-07-18T16:10:17-07:00 EVAL uniform_keep50_s1224 step100 on GPU 0 (port 8420)
+2026-07-18T16:23:54-07:00 uniform_keep50_s1224 done -> outputs/evals/general_suite/healed/uniform_keep50_s1224/student/results_2026-07-18T16-10-14.760456.json
+2026-07-18T16:23:54-07:00 lane GPU0 (rung 50) complete
+2026-07-18T16:23:54-07:00 === RUNG keep-50 COMPLETE ===
+2026-07-18T16:23:54-07:00 === RUNG keep-75 on 2 GPUs (0 1), 9 cells ===
+2026-07-18T16:23:54-07:00 HEAL glean_keep75_s1224 on GPU 0
+2026-07-18T16:26:24-07:00 HEAL glean_keep75_s1225 on GPU 1
+2026-07-18T18:56:00-07:00 EVAL glean_keep75_s1224 on GPU 0 (port 8420)
+2026-07-18T19:05:45-07:00 EVAL glean_keep75_s1225 on GPU 1 (port 8421)
+2026-07-18T19:09:27-07:00 EVAL glean_keep75_s1224 step100 on GPU 0 (port 8420)
+2026-07-18T19:19:41-07:00 glean_keep75_s1225 done -> outputs/evals/general_suite/healed/glean_keep75_s1225/student/results_2026-07-18T19-16-03.625490.json
+2026-07-18T19:19:41-07:00 HEAL reap_keep75_s1224 on GPU 1
+2026-07-18T19:23:11-07:00 glean_keep75_s1224 done -> outputs/evals/general_suite/healed/glean_keep75_s1224/student/results_2026-07-18T19-05-59.656656.json
+2026-07-18T19:23:11-07:00 HEAL glean_keep75_s1226 on GPU 0
+2026-07-18T22:05:21-07:00 EVAL reap_keep75_s1224 on GPU 1 (port 8421)
+2026-07-18T22:14:46-07:00 EVAL reap_keep75_s1224 step100 on GPU 1 (port 8421)
+2026-07-18T22:24:15-07:00 reap_keep75_s1224 done -> outputs/evals/general_suite/healed/reap_keep75_s1224/student/results_2026-07-18T22-11-41.709032.json
+2026-07-18T22:24:15-07:00 HEAL reap_keep75_s1226 on GPU 1
+2026-07-18T22:26:48-07:00 EVAL glean_keep75_s1226 on GPU 0 (port 8420)
+2026-07-18T22:40:19-07:00 glean_keep75_s1226 done -> outputs/evals/general_suite/healed/glean_keep75_s1226/student/results_2026-07-18T22-36-41.957915.json
+2026-07-18T22:40:19-07:00 HEAL reap_keep75_s1225 on GPU 0
+2026-07-19T00:19:17-07:00 reap_keep75_s1225 HEAL FAILED (no step150)
+2026-07-19T00:19:17-07:00 HEAL uniform_keep75_s1224 on GPU 0
+2026-07-19T01:12:55-07:00 EVAL reap_keep75_s1226 on GPU 1 (port 8421)
+2026-07-19T01:22:06-07:00 reap_keep75_s1226 done -> outputs/evals/general_suite/healed/reap_keep75_s1226/student/results_2026-07-19T01-22-04.019724.json
+2026-07-19T01:22:06-07:00 HEAL uniform_keep75_s1225 on GPU 1
+2026-07-19T01:49:38-07:00 uniform_keep75_s1225 HEAL FAILED (no step150)
+2026-07-19T01:49:38-07:00 lane GPU1 (rung 75) complete
+2026-07-19T02:45:32-07:00 EVAL uniform_keep75_s1224 on GPU 0 (port 8420)
+2026-07-19T02:58:55-07:00 EVAL uniform_keep75_s1224 step100 on GPU 0 (port 8420)
+2026-07-19T03:12:33-07:00 uniform_keep75_s1224 done -> outputs/evals/general_suite/healed/uniform_keep75_s1224/student/results_2026-07-19T02-58-53.335416.json
+2026-07-19T03:12:33-07:00 HEAL uniform_keep75_s1226 on GPU 0
+2026-07-19T05:37:29-07:00 EVAL uniform_keep75_s1226 on GPU 0 (port 8420)
+2026-07-19T05:51:02-07:00 uniform_keep75_s1226 done -> outputs/evals/general_suite/healed/uniform_keep75_s1226/student/results_2026-07-19T05-50-59.443956.json
+2026-07-19T05:51:02-07:00 lane GPU0 (rung 75) complete
+2026-07-19T05:51:02-07:00 === RUNG keep-75 COMPLETE ===
+2026-07-19T05:51:02-07:00 ###### GENERAL HEAL GRID COMPLETE ######
+2026-07-19T07:42:25-07:00 ###### GENERAL HEAL GRID START ######
+2026-07-19T07:42:25-07:00 === RUNG keep-25 on 3 GPUs (0 1 2), 9 cells ===
+2026-07-19T07:42:25-07:00 glean_keep25_s1224 already evaled, skip
+2026-07-19T07:42:25-07:00 reap_keep25_s1224 already evaled, skip
+2026-07-19T07:42:25-07:00 uniform_keep25_s1224 already evaled, skip
+2026-07-19T07:42:25-07:00 lane GPU0 (rung 25) complete
+2026-07-19T07:44:55-07:00 glean_keep25_s1225 already evaled, skip
+2026-07-19T07:44:55-07:00 reap_keep25_s1225 already evaled, skip
+2026-07-19T07:44:55-07:00 uniform_keep25_s1225 already evaled, skip
+2026-07-19T07:44:55-07:00 lane GPU1 (rung 25) complete
+2026-07-19T07:47:25-07:00 glean_keep25_s1226 already evaled, skip
+2026-07-19T07:47:25-07:00 reap_keep25_s1226 already evaled, skip
+2026-07-19T07:47:25-07:00 uniform_keep25_s1226 already evaled, skip
+2026-07-19T07:47:25-07:00 lane GPU2 (rung 25) complete
+2026-07-19T07:49:55-07:00 === RUNG keep-25 COMPLETE ===
+2026-07-19T07:49:55-07:00 === RUNG keep-50 on 3 GPUs (0 1 2), 9 cells ===
+2026-07-19T07:49:55-07:00 glean_keep50_s1224 already evaled, skip
+2026-07-19T07:49:55-07:00 reap_keep50_s1224 already evaled, skip
+2026-07-19T07:49:55-07:00 uniform_keep50_s1224 already evaled, skip
+2026-07-19T07:49:55-07:00 lane GPU0 (rung 50) complete
+2026-07-19T07:52:25-07:00 glean_keep50_s1225 already evaled, skip
+2026-07-19T07:52:25-07:00 reap_keep50_s1225 already evaled, skip
+2026-07-19T07:52:25-07:00 uniform_keep50_s1225 already evaled, skip
+2026-07-19T07:52:25-07:00 lane GPU1 (rung 50) complete
+2026-07-19T07:54:55-07:00 glean_keep50_s1226 already evaled, skip
+2026-07-19T07:54:55-07:00 reap_keep50_s1226 already evaled, skip
+2026-07-19T07:54:55-07:00 uniform_keep50_s1226 already evaled, skip
+2026-07-19T07:54:55-07:00 lane GPU2 (rung 50) complete
+2026-07-19T07:57:25-07:00 === RUNG keep-50 COMPLETE ===
+2026-07-19T07:57:25-07:00 === RUNG keep-75 on 2 GPUs (0 1), 9 cells ===
+2026-07-19T07:57:25-07:00 glean_keep75_s1224 already evaled, skip
+2026-07-19T07:57:25-07:00 glean_keep75_s1226 already evaled, skip
+2026-07-19T07:57:25-07:00 HEAL reap_keep75_s1225 on GPU 0
+2026-07-19T07:59:55-07:00 glean_keep75_s1225 already evaled, skip
+2026-07-19T07:59:55-07:00 reap_keep75_s1224 already evaled, skip
+2026-07-19T07:59:55-07:00 reap_keep75_s1226 already evaled, skip
+2026-07-19T07:59:55-07:00 HEAL uniform_keep75_s1225 on GPU 1
+2026-07-19T10:54:23-07:00 EVAL reap_keep75_s1225 on GPU 0 (port 8420)
+2026-07-19T11:04:17-07:00 reap_keep75_s1225 done -> outputs/evals/general_suite/healed/reap_keep75_s1225/student/results_2026-07-19T11-01-06.595488.json
+2026-07-19T11:04:17-07:00 uniform_keep75_s1224 already evaled, skip
+2026-07-19T11:04:17-07:00 uniform_keep75_s1226 already evaled, skip
+2026-07-19T11:04:17-07:00 lane GPU0 (rung 75) complete
+2026-07-19T11:52:31-07:00 EVAL uniform_keep75_s1225 on GPU 1 (port 8421)
+2026-07-19T12:05:59-07:00 uniform_keep75_s1225 done -> outputs/evals/general_suite/healed/uniform_keep75_s1225/student/results_2026-07-19T12-05-54.836521.json
+2026-07-19T12:05:59-07:00 lane GPU1 (rung 75) complete
+2026-07-19T12:05:59-07:00 === RUNG keep-75 COMPLETE ===
+2026-07-19T12:05:59-07:00 ###### GENERAL HEAL GRID COMPLETE ######

healed/grid_general/glean_keep25_s1224.console.log

@@ -0,0 +1,213 @@
+/home/henry/Documents/PythonProjects/variable-reap/.venv/lib/python3.12/site-packages/megablocks/grouped_gemm_util.py:10: UserWarning: Grouped GEMM not available.
+  warnings.warn('Grouped GEMM not available.')
+wandb: [wandb.login()] Loaded credentials for https://api.wandb.ai from /home/henry/.netrc.
+wandb: Currently logged in as: hbfreed to https://api.wandb.ai. Use `wandb login --relogin` to force relogin
+wandb: setting up run lpq2f0lo
+wandb: Tracking run with wandb version 0.28.0
+wandb: Run data is saved locally in outputs/healed/grid_general/glean_keep25_s1224/wandb/run-20260717_124922-lpq2f0lo
+wandb: Run `wandb offline` to turn off syncing.
+wandb: Syncing run glean_keep25_s1224
+wandb: ⭐️ View project at https://wandb.ai/hbfreed/glean-general-grid
+wandb: 🚀 View run at https://wandb.ai/hbfreed/glean-general-grid/runs/lpq2f0lo
+58360 cached top-128 chat trajectories / 22,295,631 unique tokens | 185 steps/epoch | 150 total steps | student params 2.09B | teacher overlap=False
+{"step": 1, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.230232952358822, "tokens": 120000, "cumulative_loss_tokens": 120000, "grad_norm": 18.125, "lr": 6e-06, "finish_rate": 0.869, "comp_len": 463.3, "t_data_s": 0.2, "t_rollout_s": 0.0, "t_step_s": 41.8, "frames": {"chat": 259}, "mem_gb": 9.93}
+The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.
+[eval step 1] sample: 'The rank of the matrix is 4.\n\n\n\nExplanation:\nThe matrix contains four distinct integers: 12, -16, 4, and -8. Each of these integers is different. Therefore, the rank of the matrix is 4.\n\n\n\n\n\n\n\n\n\n\n\n\n'
+{"step": 2, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.7555008484855295, "tokens": 120000, "cumulative_loss_tokens": 240000, "grad_norm": 23.5, "lr": 9e-06, "finish_rate": 0.997, "comp_len": 344.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 43.7, "frames": {"chat": 348}, "mem_gb": 9.59}
+{"step": 3, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.5307058254199724, "tokens": 120000, "cumulative_loss_tokens": 360000, "grad_norm": 13.375, "lr": 1.2e-05, "finish_rate": 0.997, "comp_len": 324.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 46.0, "frames": {"chat": 370}, "mem_gb": 9.74}
+{"step": 4, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.7830725133789083, "tokens": 120000, "cumulative_loss_tokens": 480000, "grad_norm": 8.1875, "lr": 1.5e-05, "finish_rate": 0.852, "comp_len": 491.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 33.0, "frames": {"chat": 244}, "mem_gb": 9.98}
+{"step": 5, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.1955481514299908, "tokens": 120000, "cumulative_loss_tokens": 600000, "grad_norm": 7.28125, "lr": 1.8e-05, "finish_rate": 0.953, "comp_len": 375.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 41.8, "frames": {"chat": 320}, "mem_gb": 9.9}
+{"step": 6, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.3044716434742014, "tokens": 120000, "cumulative_loss_tokens": 720000, "grad_norm": 5.71875, "lr": 2.1e-05, "finish_rate": 0.997, "comp_len": 317.5, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 45.6, "frames": {"chat": 378}, "mem_gb": 9.49}
+{"step": 7, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.452664390789966, "tokens": 120000, "cumulative_loss_tokens": 840000, "grad_norm": 4.84375, "lr": 2.4e-05, "finish_rate": 0.997, "comp_len": 312.5, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 47.0, "frames": {"chat": 384}, "mem_gb": 9.99}
+{"step": 8, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.2374622819642227, "tokens": 120000, "cumulative_loss_tokens": 960000, "grad_norm": 4.0625, "lr": 2.7000000000000002e-05, "finish_rate": 0.997, "comp_len": 370.4, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 43.0, "frames": {"chat": 324}, "mem_gb": 9.84}
+{"step": 9, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.5717071391055981, "tokens": 120000, "cumulative_loss_tokens": 1080000, "grad_norm": 2.796875, "lr": 3e-05, "finish_rate": 0.891, "comp_len": 502.1, "t_data_s": 0.1, "t_rollout_s": 0.0, "t_step_s": 34.6, "frames": {"chat": 239}, "mem_gb": 10.0}
+{"step": 10, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.1515966722741724, "tokens": 120000, "cumulative_loss_tokens": 1200000, "grad_norm": 3.546875, "lr": 3e-05, "finish_rate": 0.961, "comp_len": 392.2, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 39.3, "frames": {"chat": 306}, "mem_gb": 9.74}
+[eval step 10] sample: 'To compute the rank of a 4x4 matrix, we need to determine the number of linearly independent rows or columns in the matrix. The rank of a matrix is the number of linearly independent rows or columns.\n'
+{"step": 11, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.1663353991595407, "tokens": 120000, "cumulative_loss_tokens": 1320000, "grad_norm": 2.84375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 364.7, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 43.6, "frames": {"chat": 329}, "mem_gb": 9.78}
+{"step": 12, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.5732789917799334, "tokens": 120000, "cumulative_loss_tokens": 1440000, "grad_norm": 1.34375, "lr": 3e-05, "finish_rate": 0.882, "comp_len": 472.4, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 34.9, "frames": {"chat": 254}, "mem_gb": 10.01}
+{"step": 13, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.9218004519566894, "tokens": 120000, "cumulative_loss_tokens": 1560000, "grad_norm": 1.984375, "lr": 3e-05, "finish_rate": 0.974, "comp_len": 439.6, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 37.9, "frames": {"chat": 273}, "mem_gb": 9.98}
+{"step": 14, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.3943379741191864, "tokens": 120000, "cumulative_loss_tokens": 1680000, "grad_norm": 0.9921875, "lr": 3e-05, "finish_rate": 0.873, "comp_len": 508.5, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 32.2, "frames": {"chat": 236}, "mem_gb": 9.9}
+{"step": 15, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.1741832443614801, "tokens": 120000, "cumulative_loss_tokens": 1800000, "grad_norm": 4.4375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 315.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 46.1, "frames": {"chat": 381}, "mem_gb": 9.64}
+{"step": 16, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.4743189287646363, "tokens": 120000, "cumulative_loss_tokens": 1920000, "grad_norm": 0.99609375, "lr": 3e-05, "finish_rate": 0.925, "comp_len": 451.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 34.6, "frames": {"chat": 266}, "mem_gb": 9.83}
+{"step": 17, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.7200682055724164, "tokens": 120000, "cumulative_loss_tokens": 2040000, "grad_norm": 1.03125, "lr": 3e-05, "finish_rate": 0.961, "comp_len": 389.6, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 41.4, "frames": {"chat": 308}, "mem_gb": 9.82}
+{"step": 18, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.5685026399575174, "tokens": 120000, "cumulative_loss_tokens": 2160000, "grad_norm": 0.921875, "lr": 3e-05, "finish_rate": 0.853, "comp_len": 439.6, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 37.7, "frames": {"chat": 273}, "mem_gb": 10.0}
+{"step": 19, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.965660633458073, "tokens": 120000, "cumulative_loss_tokens": 2280000, "grad_norm": 1.203125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 326.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 46.1, "frames": {"chat": 368}, "mem_gb": 9.63}
+{"step": 20, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.1254668449434142, "tokens": 120000, "cumulative_loss_tokens": 2400000, "grad_norm": 1.2109375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 354.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 47.0, "frames": {"chat": 339}, "mem_gb": 9.88}
+[eval step 20] sample: 'To determine the rank of a 4x4 matrix, we need to find the maximum number of linearly independent rows or columns in the given matrix. This is done by performing row reduction (or elimination) operati'
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+[eval step 30] sample: 'To determine the rank of a 4x4 matrix, we need to find the maximum number of linearly independent rows or columns in the matrix. This involves performing row reduction (or elimination) operations to r'
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+[eval step 40] sample: "To compute the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. \n\nLet's represent the matrix as follows:\n\\[\nA = \\begin{pmatrix}\n12 & -16"
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+[eval step 50] sample: 'To compute the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns. For a 4x4 matrix, we can use various methods, including row reduction (Gaussian elimin'
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+[eval step 60] sample: "To determine the rank of a matrix, we need to find the maximum number of linearly independent rows or columns. \n\nLet's start by converting the matrix into row-echelon form (REF) using Gaussian elimina"
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+{"step": 70, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.58340040049615, "tokens": 120000, "cumulative_loss_tokens": 8400000, "grad_norm": 0.69140625, "lr": 3e-05, "finish_rate": 0.973, "comp_len": 402.7, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 44.7, "frames": {"chat": 298}, "mem_gb": 9.73}
+[eval step 70] sample: "To compute the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. \n\nLet's start by writing down the given 4x4 matrix:\n\n\\[\n\\begin{bmatrix}\n"
+{"step": 71, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.4847553086879974, "tokens": 120000, "cumulative_loss_tokens": 8520000, "grad_norm": 0.62109375, "lr": 3e-05, "finish_rate": 0.917, "comp_len": 454.5, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 39.5, "frames": {"chat": 264}, "mem_gb": 9.95}
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+{"step": 79, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.3909381251984586, "tokens": 120000, "cumulative_loss_tokens": 9480000, "grad_norm": 0.59765625, "lr": 3e-05, "finish_rate": 0.915, "comp_len": 424.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 40.8, "frames": {"chat": 283}, "mem_gb": 9.86}
+{"step": 80, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.7662618677861989, "tokens": 120000, "cumulative_loss_tokens": 9600000, "grad_norm": 0.79296875, "lr": 3e-05, "finish_rate": 0.998, "comp_len": 299.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 53.2, "frames": {"chat": 401}, "mem_gb": 9.61}
+[eval step 80] sample: 'To determine the rank of a matrix, we need to find the maximum number of linearly independent rows or columns. A matrix is said to be of rank \\( n \\) if it has \\( n \\) linearly independent rows or col'
+{"step": 81, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.5011390567759052, "tokens": 120000, "cumulative_loss_tokens": 9720000, "grad_norm": 0.66796875, "lr": 3e-05, "finish_rate": 0.965, "comp_len": 416.7, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 43.1, "frames": {"chat": 288}, "mem_gb": 9.81}
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+{"step": 83, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.858884850747635, "tokens": 120000, "cumulative_loss_tokens": 9960000, "grad_norm": 0.84765625, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 357.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 48.7, "frames": {"chat": 336}, "mem_gb": 9.74}
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+{"step": 86, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.714498685978353, "tokens": 120000, "cumulative_loss_tokens": 10320000, "grad_norm": 0.765625, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 357.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 51.5, "frames": {"chat": 336}, "mem_gb": 9.75}
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+{"step": 88, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.41439470696250597, "tokens": 120000, "cumulative_loss_tokens": 10560000, "grad_norm": 0.5859375, "lr": 3e-05, "finish_rate": 0.968, "comp_len": 427.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 41.0, "frames": {"chat": 281}, "mem_gb": 9.82}
+{"step": 89, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.5404754282862569, "tokens": 120000, "cumulative_loss_tokens": 10680000, "grad_norm": 0.66796875, "lr": 3e-05, "finish_rate": 0.977, "comp_len": 401.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 44.9, "frames": {"chat": 299}, "mem_gb": 9.69}
+{"step": 90, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.6631877648853386, "tokens": 120000, "cumulative_loss_tokens": 10800000, "grad_norm": 0.734375, "lr": 3e-05, "finish_rate": 0.966, "comp_len": 365.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 47.5, "frames": {"chat": 328}, "mem_gb": 9.88}
+[eval step 90] sample: "To determine the rank of a matrix, we need to find the maximum number of linearly independent rows or columns. Let's break down the problem into smaller steps and solve it using Python and SymPy.\n\n###"
+{"step": 91, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.5152765134019777, "tokens": 120000, "cumulative_loss_tokens": 10920000, "grad_norm": 0.64453125, "lr": 3e-05, "finish_rate": 0.919, "comp_len": 442.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 41.3, "frames": {"chat": 271}, "mem_gb": 9.98}
+{"step": 92, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.5397345383686324, "tokens": 120000, "cumulative_loss_tokens": 11040000, "grad_norm": 0.671875, "lr": 3e-05, "finish_rate": 0.874, "comp_len": 446.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 45.9, "frames": {"chat": 269}, "mem_gb": 10.08}
+{"step": 93, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.8142568668286005, "tokens": 120000, "cumulative_loss_tokens": 11160000, "grad_norm": 0.79296875, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 324.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 55.6, "frames": {"chat": 370}, "mem_gb": 9.63}
+{"step": 94, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.8570075749141475, "tokens": 120000, "cumulative_loss_tokens": 11280000, "grad_norm": 0.8359375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 340.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 51.3, "frames": {"chat": 352}, "mem_gb": 9.69}
+{"step": 95, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.7482497710308681, "tokens": 120000, "cumulative_loss_tokens": 11400000, "grad_norm": 0.8125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 363.6, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 51.9, "frames": {"chat": 330}, "mem_gb": 9.89}
+{"step": 96, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.6091186967118022, "tokens": 120000, "cumulative_loss_tokens": 11520000, "grad_norm": 0.72265625, "lr": 3e-05, "finish_rate": 0.991, "comp_len": 359.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 50.0, "frames": {"chat": 334}, "mem_gb": 9.62}
+{"step": 97, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.6094944001651058, "tokens": 120000, "cumulative_loss_tokens": 11640000, "grad_norm": 0.73828125, "lr": 3e-05, "finish_rate": 0.967, "comp_len": 357.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 46.4, "frames": {"chat": 336}, "mem_gb": 9.75}
+{"step": 98, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.44065201980068036, "tokens": 120000, "cumulative_loss_tokens": 11760000, "grad_norm": 0.63671875, "lr": 3e-05, "finish_rate": 0.964, "comp_len": 388.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 45.3, "frames": {"chat": 309}, "mem_gb": 9.84}
+{"step": 99, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.750593746100987, "tokens": 120000, "cumulative_loss_tokens": 11880000, "grad_norm": 0.80078125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 306.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 53.8, "frames": {"chat": 391}, "mem_gb": 9.73}
+{"step": 100, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.3937398470057795, "tokens": 120000, "cumulative_loss_tokens": 12000000, "grad_norm": 0.61328125, "lr": 3e-05, "finish_rate": 0.83, "comp_len": 454.5, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 44.7, "frames": {"chat": 264}, "mem_gb": 10.12}
+[eval step 100] sample: "To find the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns. \n\nLet's start by writing down the given 4x4 matrix:\n\n\\[\n\\begin{bmatrix}\n12 & -16 & 4 & 16"
+{"step": 101, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.6405833819416662, "tokens": 120000, "cumulative_loss_tokens": 12120000, "grad_norm": 0.6953125, "lr": 3e-05, "finish_rate": 0.994, "comp_len": 343.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 50.1, "frames": {"chat": 349}, "mem_gb": 9.68}
+{"step": 102, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.6110699118683736, "tokens": 120000, "cumulative_loss_tokens": 12240000, "grad_norm": 1.0078125, "lr": 3e-05, "finish_rate": 0.968, "comp_len": 384.6, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 49.6, "frames": {"chat": 312}, "mem_gb": 9.73}
+{"step": 103, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.22543682222130398, "tokens": 120000, "cumulative_loss_tokens": 12360000, "grad_norm": 0.4921875, "lr": 3e-05, "finish_rate": 0.817, "comp_len": 524.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 42.2, "frames": {"chat": 229}, "mem_gb": 10.0}
+{"step": 104, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.571418999376893, "tokens": 120000, "cumulative_loss_tokens": 12480000, "grad_norm": 0.671875, "lr": 3e-05, "finish_rate": 0.938, "comp_len": 416.7, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 46.1, "frames": {"chat": 288}, "mem_gb": 10.0}
+{"step": 105, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.7974187780410051, "tokens": 120000, "cumulative_loss_tokens": 12600000, "grad_norm": 0.8828125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 341.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 50.8, "frames": {"chat": 351}, "mem_gb": 9.66}
+{"step": 106, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.3170151141561568, "tokens": 120000, "cumulative_loss_tokens": 12720000, "grad_norm": 0.6015625, "lr": 3e-05, "finish_rate": 0.812, "comp_len": 538.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 37.1, "frames": {"chat": 223}, "mem_gb": 10.02}
+{"step": 107, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.1569358562398081, "tokens": 120000, "cumulative_loss_tokens": 12840000, "grad_norm": 0.482421875, "lr": 3e-05, "finish_rate": 0.793, "comp_len": 563.4, "t_data_s": 0.1, "t_rollout_s": 0.0, "t_step_s": 34.7, "frames": {"chat": 213}, "mem_gb": 9.99}
+{"step": 108, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.7303174932712689, "tokens": 120000, "cumulative_loss_tokens": 12960000, "grad_norm": 0.78125, "lr": 3e-05, "finish_rate": 0.995, "comp_len": 315.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 54.8, "frames": {"chat": 380}, "mem_gb": 9.84}
+{"step": 109, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.41821051687517513, "tokens": 120000, "cumulative_loss_tokens": 13080000, "grad_norm": 0.61328125, "lr": 3e-05, "finish_rate": 0.893, "comp_len": 442.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 39.7, "frames": {"chat": 271}, "mem_gb": 10.02}
+{"step": 110, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.7821986571711799, "tokens": 120000, "cumulative_loss_tokens": 13200000, "grad_norm": 0.75, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 352.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 49.2, "frames": {"chat": 340}, "mem_gb": 9.91}
+[eval step 110] sample: 'To determine the rank of a matrix, we need to find the maximum number of linearly independent rows or columns in the matrix. \n\nGiven the matrix:\n\\[\n\\begin{bmatrix}\n12 & -16 & 4 & 16 \\\\\n-9 & 11 & -1 & '
+{"step": 111, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.8301274567630142, "tokens": 120000, "cumulative_loss_tokens": 13320000, "grad_norm": 0.82421875, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 327.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 52.2, "frames": {"chat": 366}, "mem_gb": 9.61}
+{"step": 112, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.299316186900499, "tokens": 120000, "cumulative_loss_tokens": 13440000, "grad_norm": 0.54296875, "lr": 3e-05, "finish_rate": 0.854, "comp_len": 515.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 38.0, "frames": {"chat": 233}, "mem_gb": 10.0}
+{"step": 113, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.5317195191866407, "tokens": 120000, "cumulative_loss_tokens": 13560000, "grad_norm": 0.6796875, "lr": 3e-05, "finish_rate": 0.968, "comp_len": 384.6, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 46.6, "frames": {"chat": 312}, "mem_gb": 9.86}
+{"step": 114, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.7483980015807474, "tokens": 120000, "cumulative_loss_tokens": 13680000, "grad_norm": 0.77734375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 330.6, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 51.0, "frames": {"chat": 363}, "mem_gb": 9.58}
+{"step": 115, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.7562728609332194, "tokens": 120000, "cumulative_loss_tokens": 13800000, "grad_norm": 0.7578125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 371.5, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 47.4, "frames": {"chat": 323}, "mem_gb": 9.85}
+{"step": 116, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.8216717583857477, "tokens": 120000, "cumulative_loss_tokens": 13920000, "grad_norm": 0.81640625, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 346.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 50.6, "frames": {"chat": 346}, "mem_gb": 9.59}
+{"step": 117, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.7373267066142212, "tokens": 120000, "cumulative_loss_tokens": 14040000, "grad_norm": 1.0234375, "lr": 3e-05, "finish_rate": 0.975, "comp_len": 377.4, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 45.7, "frames": {"chat": 318}, "mem_gb": 9.74}
+{"step": 118, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.5497194940060377, "tokens": 120000, "cumulative_loss_tokens": 14160000, "grad_norm": 0.65625, "lr": 3e-05, "finish_rate": 0.935, "comp_len": 436.4, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 46.9, "frames": {"chat": 275}, "mem_gb": 9.92}
+{"step": 119, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.6847652262626837, "tokens": 120000, "cumulative_loss_tokens": 14280000, "grad_norm": 0.76953125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 347.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 51.5, "frames": {"chat": 345}, "mem_gb": 9.67}
+{"step": 120, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.686522659535706, "tokens": 120000, "cumulative_loss_tokens": 14400000, "grad_norm": 0.8046875, "lr": 3e-05, "finish_rate": 0.992, "comp_len": 318.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 52.2, "frames": {"chat": 377}, "mem_gb": 9.82}
+[eval step 120] sample: 'To find the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. \n\nGiven the matrix:\n\\[\n\\begin{bmatrix}\n12 & -16 & 4 & 16 \\\\\n-9 & 11 & -1 & '
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+[eval step 130] sample: 'To find the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. A matrix is said to be of rank \\( r \\) if it has \\( r \\) linearly independe'
+{"step": 131, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.39424919714008766, "tokens": 120000, "cumulative_loss_tokens": 15720000, "grad_norm": 0.59765625, "lr": 3e-05, "finish_rate": 0.868, "comp_len": 452.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 40.6, "frames": {"chat": 265}, "mem_gb": 10.0}
+{"step": 132, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.7736657298201074, "tokens": 120000, "cumulative_loss_tokens": 15840000, "grad_norm": 0.82421875, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 356.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 54.3, "frames": {"chat": 337}, "mem_gb": 9.55}
+{"step": 133, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.4310107218934844, "tokens": 120000, "cumulative_loss_tokens": 15960000, "grad_norm": 0.625, "lr": 3e-05, "finish_rate": 0.972, "comp_len": 413.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 48.2, "frames": {"chat": 290}, "mem_gb": 9.81}
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+{"step": 135, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.43771783427683014, "tokens": 120000, "cumulative_loss_tokens": 16200000, "grad_norm": 0.61328125, "lr": 3e-05, "finish_rate": 0.928, "comp_len": 412.4, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 44.1, "frames": {"chat": 291}, "mem_gb": 9.84}
+{"step": 136, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.6974321913416808, "tokens": 120000, "cumulative_loss_tokens": 16320000, "grad_norm": 0.77734375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 350.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 50.9, "frames": {"chat": 342}, "mem_gb": 9.71}
+{"step": 137, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.8007760579209775, "tokens": 120000, "cumulative_loss_tokens": 16440000, "grad_norm": 0.77734375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 355.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 48.8, "frames": {"chat": 338}, "mem_gb": 9.66}
+{"step": 138, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.7884519690003247, "tokens": 120000, "cumulative_loss_tokens": 16560000, "grad_norm": 0.78515625, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 337.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 54.1, "frames": {"chat": 356}, "mem_gb": 9.62}
+{"step": 139, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.3688757979504764, "tokens": 120000, "cumulative_loss_tokens": 16680000, "grad_norm": 0.640625, "lr": 3e-05, "finish_rate": 0.86, "comp_len": 452.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 46.7, "frames": {"chat": 265}, "mem_gb": 10.0}
+{"step": 140, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.6384047362486521, "tokens": 120000, "cumulative_loss_tokens": 16800000, "grad_norm": 0.765625, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 339.9, "t_data_s": 0.2, "t_rollout_s": 0.0, "t_step_s": 53.8, "frames": {"chat": 353}, "mem_gb": 9.71}
+[eval step 140] sample: 'To compute the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. \n\nGiven the matrix:\n\\[\n\\begin{bmatrix}\n12 & -16 & 4 & 16 \\\\\n-9 & 11 & -1'
+{"step": 141, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.6027499658275396, "tokens": 120000, "cumulative_loss_tokens": 16920000, "grad_norm": 0.703125, "lr": 3e-05, "finish_rate": 0.949, "comp_len": 379.7, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 44.4, "frames": {"chat": 316}, "mem_gb": 9.9}
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+{"step": 143, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.5405622877730056, "tokens": 120000, "cumulative_loss_tokens": 17160000, "grad_norm": 0.80859375, "lr": 3e-05, "finish_rate": 0.982, "comp_len": 362.5, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 47.8, "frames": {"chat": 331}, "mem_gb": 9.69}
+{"step": 144, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.5336027219651267, "tokens": 120000, "cumulative_loss_tokens": 17280000, "grad_norm": 0.703125, "lr": 3e-05, "finish_rate": 0.972, "comp_len": 375.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 44.5, "frames": {"chat": 320}, "mem_gb": 10.0}
+{"step": 145, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.48749642031074814, "tokens": 120000, "cumulative_loss_tokens": 17400000, "grad_norm": 0.62890625, "lr": 3e-05, "finish_rate": 0.941, "comp_len": 441.2, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 38.2, "frames": {"chat": 272}, "mem_gb": 9.74}
+{"step": 146, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.8456337846264244, "tokens": 120000, "cumulative_loss_tokens": 17520000, "grad_norm": 0.83984375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 326.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 47.6, "frames": {"chat": 368}, "mem_gb": 9.66}
+{"step": 147, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.6977787470727538, "tokens": 120000, "cumulative_loss_tokens": 17640000, "grad_norm": 0.75, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 365.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 45.6, "frames": {"chat": 328}, "mem_gb": 9.68}
+{"step": 148, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.6741483884370575, "tokens": 120000, "cumulative_loss_tokens": 17760000, "grad_norm": 0.7578125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 368.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 45.0, "frames": {"chat": 326}, "mem_gb": 9.9}
+{"step": 149, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.779587540318879, "tokens": 120000, "cumulative_loss_tokens": 17880000, "grad_norm": 0.7890625, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 329.7, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 48.6, "frames": {"chat": 364}, "mem_gb": 9.84}
+{"step": 150, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.49224459458068015, "tokens": 120000, "cumulative_loss_tokens": 18000000, "grad_norm": 0.625, "lr": 3e-05, "finish_rate": 0.97, "comp_len": 405.4, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 39.8, "frames": {"chat": 296}, "mem_gb": 9.88}
+[eval step 150] sample: 'To compute the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. \n\nGiven the matrix:\n\\[\n\\begin{bmatrix}\n12 & -16 & 4 & 16 \\\\\n-9 & 11 & -1'
+checkpoint snapshot queued -> outputs/healed/grid_general/glean_keep25_s1224/step0150
+wandb: updating run metadata
+wandb: uploading output.log; uploading wandb-summary.json; uploading config.yaml
+wandb: 
+wandb: Run history:
+wandb:               comp_len ▁▁▁▆▅▁▂▇▅▅▅▂▁▄▁▄▇▃▃▃▂▃▃▂▂▂▇█▅▇▃▁▃▅▅▂▅▃▃▄
+wandb: cumulative_loss_tokens ▁▂▂▂▂▂▂▂▃▃▃▃▃▃▄▄▄▅▅▅▅▅▅▆▆▆▆▆▆▆▇▇▇▇▇▇████
+wandb:                  epoch ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
+wandb:            finish_rate ██▇▃█▅▄▅▃▇██▇█▇█▄█▅▄▇██▅██▂▁████▆▅██▃█▆█
+wandb:        forward_topk_kl ▆▃▄▄▇█▆█▂▄▆▅▄▃▅▁▅▆▄▅▇▅▅▄▄▅▃▅▂▆▆▆▄▂▅▃▅▂▅▄
+wandb:              grad_norm █▃▃▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
+wandb:                     lr ▁▅▇█████████████████████████████████████
+wandb:                 mem_gb ▂▆▇▄▇▂▂▄▂▇▅▅▆▃▁▅▅▅▅▇▄▆▅▂▄▅█▃▃▇▅▃▅▂▃▁▅█▇▃
+wandb:                   step ▁▁▁▁▁▂▂▂▂▂▂▃▃▃▃▃▃▃▃▄▄▄▄▄▄▄▅▅▅▆▆▆▆▇▇█████
+wandb:               t_data_s █▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
+wandb:                     +3 ...
+wandb: 
+wandb: Run summary:
+wandb:               comp_len 405.4
+wandb: cumulative_loss_tokens 18000000
+wandb:                  epoch 0
+wandb:            finish_rate 0.97
+wandb:        forward_topk_kl 0.49224
+wandb:              grad_norm 0.625
+wandb:                     lr 3e-05
+wandb:                 mem_gb 9.88
+wandb:                   step 150
+wandb:               t_data_s 0
+wandb:                     +4 ...
+wandb: 
+wandb: 🚀 View run glean_keep25_s1224 at: https://wandb.ai/hbfreed/glean-general-grid/runs/lpq2f0lo
+wandb: ⭐️ View project at: https://wandb.ai/hbfreed/glean-general-grid
+wandb: Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)
+wandb: Find logs at: outputs/healed/grid_general/glean_keep25_s1224/wandb/run-20260717_124922-lpq2f0lo/logs

healed/grid_general/glean_keep25_s1225.console.log

@@ -0,0 +1,213 @@
+/home/henry/Documents/PythonProjects/variable-reap/.venv/lib/python3.12/site-packages/megablocks/grouped_gemm_util.py:10: UserWarning: Grouped GEMM not available.
+  warnings.warn('Grouped GEMM not available.')
+wandb: [wandb.login()] Loaded credentials for https://api.wandb.ai from /home/henry/.netrc.
+wandb: Currently logged in as: hbfreed to https://api.wandb.ai. Use `wandb login --relogin` to force relogin
+wandb: setting up run 8j8lz7cs
+wandb: Tracking run with wandb version 0.28.0
+wandb: Run data is saved locally in outputs/healed/grid_general/glean_keep25_s1225/wandb/run-20260717_125152-8j8lz7cs
+wandb: Run `wandb offline` to turn off syncing.
+wandb: Syncing run glean_keep25_s1225
+wandb: ⭐️ View project at https://wandb.ai/hbfreed/glean-general-grid
+wandb: 🚀 View run at https://wandb.ai/hbfreed/glean-general-grid/runs/8j8lz7cs
+58360 cached top-128 chat trajectories / 22,295,631 unique tokens | 185 steps/epoch | 150 total steps | student params 2.09B | teacher overlap=False
+{"step": 1, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.808664998525381, "tokens": 120000, "cumulative_loss_tokens": 120000, "grad_norm": 23.5, "lr": 6e-06, "finish_rate": 0.997, "comp_len": 362.5, "t_data_s": 0.2, "t_rollout_s": 0.0, "t_step_s": 49.6, "frames": {"chat": 331}, "mem_gb": 9.8}
+The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.
+[eval step 1] sample: "To solve this problem, we'll break it into parts and then sum up the contributions over the respective periods.\n\n\n\n1. **Calculate the contribution from the salary:**\n   - The salary contribution is $2"
+{"step": 2, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.6490384441773096, "tokens": 120000, "cumulative_loss_tokens": 240000, "grad_norm": 20.25, "lr": 9e-06, "finish_rate": 0.997, "comp_len": 350.9, "t_data_s": 0.1, "t_rollout_s": 0.0, "t_step_s": 44.7, "frames": {"chat": 342}, "mem_gb": 9.62}
+{"step": 3, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.8595948845709365, "tokens": 120000, "cumulative_loss_tokens": 360000, "grad_norm": 15.0, "lr": 1.2e-05, "finish_rate": 0.997, "comp_len": 335.2, "t_data_s": 0.1, "t_rollout_s": 0.0, "t_step_s": 45.6, "frames": {"chat": 358}, "mem_gb": 9.66}
+{"step": 4, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.4889224458272259, "tokens": 120000, "cumulative_loss_tokens": 480000, "grad_norm": 9.6875, "lr": 1.5e-05, "finish_rate": 0.997, "comp_len": 348.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 48.2, "frames": {"chat": 344}, "mem_gb": 9.75}
+{"step": 5, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.0059059998517235, "tokens": 120000, "cumulative_loss_tokens": 600000, "grad_norm": 6.5625, "lr": 1.8e-05, "finish_rate": 0.861, "comp_len": 449.4, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 39.1, "frames": {"chat": 267}, "mem_gb": 10.06}
+{"step": 6, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.6072625180219611, "tokens": 120000, "cumulative_loss_tokens": 720000, "grad_norm": 4.8125, "lr": 2.1e-05, "finish_rate": 0.835, "comp_len": 483.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 33.8, "frames": {"chat": 248}, "mem_gb": 9.89}
+{"step": 7, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.2357142568791906, "tokens": 120000, "cumulative_loss_tokens": 840000, "grad_norm": 5.59375, "lr": 2.4e-05, "finish_rate": 0.979, "comp_len": 364.7, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 42.6, "frames": {"chat": 329}, "mem_gb": 9.89}
+{"step": 8, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.2724269317929944, "tokens": 120000, "cumulative_loss_tokens": 960000, "grad_norm": 3.515625, "lr": 2.7000000000000002e-05, "finish_rate": 0.997, "comp_len": 354.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 45.8, "frames": {"chat": 339}, "mem_gb": 9.85}
+{"step": 9, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.4072858887024224, "tokens": 120000, "cumulative_loss_tokens": 1080000, "grad_norm": 3.390625, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 339.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 46.0, "frames": {"chat": 354}, "mem_gb": 9.84}
+{"step": 10, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.7663721626664202, "tokens": 120000, "cumulative_loss_tokens": 1200000, "grad_norm": 2.078125, "lr": 3e-05, "finish_rate": 0.943, "comp_len": 430.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 36.6, "frames": {"chat": 279}, "mem_gb": 9.98}
+[eval step 10] sample: 'To solve this problem, we need to determine how many months it takes for Jamie to reach their goal of $1,000,000, taking into account both the savings from their salary and the investment account.\n\n1.'
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+[eval step 30] sample: "To solve this problem, we need to determine how many months Jamie will save $2,500 each month from their salary to reach a total of $1,000,000, starting with $50,000.\n\nLet's break down the problem int"
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+[eval step 50] sample: 'To determine how many months it will take for Jamie to reach their goal of $1,000,000, we need to consider both the savings from their salary and the interest earned in their investment account.\n\n**St'
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+[eval step 60] sample: 'To solve this problem, we need to determine how many months Jamie will need to save $1,000,000, given their savings rate and investment earnings.\n\n**Step 1: Calculate the total amount Jamie needs to s'
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+[eval step 130] sample: 'To solve this problem, we need to determine how many months it will take Jamie to save enough money to reach their goal of $1,000,000, given their savings and investment details.\n\n**Step 1: Calculate '
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+[eval step 140] sample: 'To solve this problem, we need to determine how many months it will take Jamie to save $1,000,000 from their savings and investment account, given their monthly savings and the interest earned.\n\n**Ste'
+{"step": 141, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.6306400136097023, "tokens": 120000, "cumulative_loss_tokens": 16920000, "grad_norm": 0.70703125, "lr": 3e-05, "finish_rate": 0.98, "comp_len": 346.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 45.0, "frames": {"chat": 346}, "mem_gb": 9.86}
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+{"step": 145, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.5723603961752107, "tokens": 120000, "cumulative_loss_tokens": 17400000, "grad_norm": 0.69921875, "lr": 3e-05, "finish_rate": 0.936, "comp_len": 401.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 39.5, "frames": {"chat": 299}, "mem_gb": 10.01}
+{"step": 146, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.40002196048907934, "tokens": 120000, "cumulative_loss_tokens": 17520000, "grad_norm": 0.63671875, "lr": 3e-05, "finish_rate": 0.88, "comp_len": 463.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 37.6, "frames": {"chat": 259}, "mem_gb": 10.1}
+{"step": 147, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.4529625337978825, "tokens": 120000, "cumulative_loss_tokens": 17640000, "grad_norm": 0.62109375, "lr": 3e-05, "finish_rate": 0.966, "comp_len": 369.2, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 40.6, "frames": {"chat": 325}, "mem_gb": 9.83}
+{"step": 148, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.42111575560091685, "tokens": 120000, "cumulative_loss_tokens": 17760000, "grad_norm": 0.57421875, "lr": 3e-05, "finish_rate": 0.923, "comp_len": 442.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 36.5, "frames": {"chat": 271}, "mem_gb": 9.91}
+{"step": 149, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.6778534247227634, "tokens": 120000, "cumulative_loss_tokens": 17880000, "grad_norm": 0.73828125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 334.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 43.8, "frames": {"chat": 359}, "mem_gb": 9.69}
+{"step": 150, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.6548494756745795, "tokens": 120000, "cumulative_loss_tokens": 18000000, "grad_norm": 0.71875, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 347.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 44.8, "frames": {"chat": 345}, "mem_gb": 9.75}
+[eval step 150] sample: 'To solve this problem, we need to determine how many months it will take Jamie to save $1,000,000 from their savings and investment account, given their monthly savings and the interest earned.\n\n**Ste'
+checkpoint snapshot queued -> outputs/healed/grid_general/glean_keep25_s1225/step0150
+wandb: updating run metadata
+wandb: uploading wandb-summary.json; uploading config.yaml; uploading output.log
+wandb: 
+wandb: Run history:
+wandb:               comp_len ▂▂▂▄▁▄▄▁▄▅▃▁▇▂▄▂▄▁▁▃▇▄▂▂▆▂▃▅▁▃▆▂▃▄▂▂█▄▄▂
+wandb: cumulative_loss_tokens ▁▁▁▁▁▂▂▂▂▂▂▃▃▃▃▃▄▄▄▄▄▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇▇▇██
+wandb:                  epoch ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
+wandb:            finish_rate ██▆▇▅▆▄█▄▁█▄▅▃█▇▄▇▇██▄▂▆▅█▂████▄▃▇▆██▅▇█
+wandb:        forward_topk_kl █▇▅▄▅▂▅▃▃▄▅▁▄▄▄▃▄▃▄▄▃▂▂▄▄▄▄▃▂▃▂▂▄▄▂▂▄▁▂▂
+wandb:              grad_norm █▄▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
+wandb:                     lr ▁▂▃█████████████████████████████████████
+wandb:                 mem_gb ▆▅▇▇▇▄██▅▄▇▇█▆▁▃▃▂▄▃▄▆█▇▁█▃█▂▄▆▅▄██▅▆▅▃▅
+wandb:                   step ▁▁▁▂▂▂▂▃▃▃▃▃▄▄▄▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇▇▇█
+wandb:               t_data_s █▁▁▁█▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁█▁▁▁▁▁▁▁▁
+wandb:                     +3 ...
+wandb: 
+wandb: Run summary:
+wandb:               comp_len 347.8
+wandb: cumulative_loss_tokens 18000000
+wandb:                  epoch 0
+wandb:            finish_rate 0.997
+wandb:        forward_topk_kl 0.65485
+wandb:              grad_norm 0.71875
+wandb:                     lr 3e-05
+wandb:                 mem_gb 9.75
+wandb:                   step 150
+wandb:               t_data_s 0
+wandb:                     +4 ...
+wandb: 
+wandb: 🚀 View run glean_keep25_s1225 at: https://wandb.ai/hbfreed/glean-general-grid/runs/8j8lz7cs
+wandb: ⭐️ View project at: https://wandb.ai/hbfreed/glean-general-grid
+wandb: Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)
+wandb: Find logs at: outputs/healed/grid_general/glean_keep25_s1225/wandb/run-20260717_125152-8j8lz7cs/logs

healed/grid_general/glean_keep25_s1226.console.log

@@ -0,0 +1,212 @@
+/home/henry/Documents/PythonProjects/variable-reap/.venv/lib/python3.12/site-packages/megablocks/grouped_gemm_util.py:10: UserWarning: Grouped GEMM not available.
+  warnings.warn('Grouped GEMM not available.')
+wandb: [wandb.login()] Loaded credentials for https://api.wandb.ai from /home/henry/.netrc.
+wandb: Currently logged in as: hbfreed to https://api.wandb.ai. Use `wandb login --relogin` to force relogin
+wandb: Tracking run with wandb version 0.28.0
+wandb: Run data is saved locally in outputs/healed/grid_general/glean_keep25_s1226/wandb/run-20260717_125422-n3zawkef
+wandb: Run `wandb offline` to turn off syncing.
+wandb: Syncing run glean_keep25_s1226
+wandb: ⭐️ View project at https://wandb.ai/hbfreed/glean-general-grid
+wandb: 🚀 View run at https://wandb.ai/hbfreed/glean-general-grid/runs/n3zawkef
+58360 cached top-128 chat trajectories / 22,295,631 unique tokens | 185 steps/epoch | 150 total steps | student params 2.09B | teacher overlap=False
+{"step": 1, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.3049798537532489, "tokens": 120000, "cumulative_loss_tokens": 120000, "grad_norm": 20.25, "lr": 6e-06, "finish_rate": 0.945, "comp_len": 389.6, "t_data_s": 0.2, "t_rollout_s": 0.0, "t_step_s": 46.1, "frames": {"chat": 308}, "mem_gb": 9.9}
+The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.
+[eval step 1] sample: 'To solve the problem, we start by expressing the terms in the arithmetic sequence in terms based on the common difference $d$. \n\nGiven $a_1 + 3a_8 + a_{15} = 120$, we can write $a_1 + 3a_8 + a_{15} = '
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+[eval step 10] sample: 'To solve the problem, we need to determine the common term \\(a_n\\) of the arithmetic sequence and then use the given equation to find \\(2a_9 - a_{10}\\).\n\n### Step 1: Find the common term \\(a_n\\)\n\nThe '
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+[eval step 20] sample: 'in the arithmetic sequence {a_n} where a_1 is the first term and a_n is the n-th term, we have the equation a_1 + 3a_8 + a_15 = 120. to find the value of 2a_9 - a_{10}, we need to'
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+[eval step 30] sample: "in the arithmetic sequence {a_n} where a_1 + 3a_8 + a_15 = 120, we need to find the value of 2a_9 - a_10.\n\nfirst, let's express the terms of the sequence in terms of a_n.\n\na_"
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+{"step": 38, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.7748054912365973, "tokens": 120000, "cumulative_loss_tokens": 4560000, "grad_norm": 0.85546875, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 349.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 45.3, "frames": {"chat": 343}, "mem_gb": 9.69}
+{"step": 39, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.8812485810771584, "tokens": 120000, "cumulative_loss_tokens": 4680000, "grad_norm": 0.97265625, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 382.2, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 40.6, "frames": {"chat": 314}, "mem_gb": 9.69}
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+[eval step 40] sample: "in an arithmetic sequence, let's denote the first term as \\(a_1\\) and the common difference as \\(d\\). the general term \\(a_n\\) can be expressed as \\(a_n = a_1 + (n-1)d\\).\n\nwe are given the equation \\("
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+{"step": 50, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.6419923638241987, "tokens": 120000, "cumulative_loss_tokens": 6000000, "grad_norm": 0.875, "lr": 3e-05, "finish_rate": 0.977, "comp_len": 350.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 44.1, "frames": {"chat": 342}, "mem_gb": 9.8}
+[eval step 50] sample: 'in the arithmetic sequence {a_n} where a_1 is the first term and a_n is the n-th term, we have the equation: a_1 + 3a_8 + a_{15} = 120. to find the value of 2a_9 - a_{10}, we'
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+[eval step 120] sample: 'to find the value of \\(2a_9 - a_{10}\\) in the arithmetic sequence \\(\\{a_n\\}\\) given that \\(a_1 + 3a_8 + a_{15} = 120\\), we need to follow these steps:\n\n1. **express \\(a_'
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+[eval step 130] sample: "to solve the problem, let's start by understanding the properties of an arithmetic sequence. in an arithmetic sequence, the nth term can be expressed as:\n\n\\[ a_n = a_1 + (n-1)d \\]\n\nwhere \\( a_1 \\) is "
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+[eval step 140] sample: "to solve the problem, let's start by expressing the terms of the arithmetic sequence in terms of the first term \\(a_1\\) and the common difference \\(d\\).\n\nthe general term \\(a_n\\) of an arithmetic sequ"
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+[eval step 150] sample: "to solve the problem, let's start by expressing the terms of the arithmetic sequence in terms of the first term \\(a_1\\) and the common difference \\(d\\).\n\nthe general term \\(a_n\\) of an arithmetic sequ"
+checkpoint snapshot queued -> outputs/healed/grid_general/glean_keep25_s1226/step0150
+wandb: updating run metadata
+wandb: uploading summary, console lines 168-168
+wandb: 
+wandb: Run history:
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+wandb:                 mem_gb ▇▇▁▄▇▆▃█▅▃▂▇▆▆▆▆▇▇▅▁▃▃▅▅▅█▅▅▇█▃▄▅▆▇█▅▆▇▇
+wandb:                   step ▁▁▁▂▂▂▂▂▃▃▃▃▃▃▃▄▄▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▆▇▇▇▇██
+wandb:               t_data_s █▅▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
+wandb:                     +3 ...
+wandb: 
+wandb: Run summary:
+wandb:               comp_len 409.6
+wandb: cumulative_loss_tokens 18000000
+wandb:                  epoch 0
+wandb:            finish_rate 0.942
+wandb:        forward_topk_kl 0.42197
+wandb:              grad_norm 0.60938
+wandb:                     lr 3e-05
+wandb:                 mem_gb 9.97
+wandb:                   step 150
+wandb:               t_data_s 0
+wandb:                     +4 ...
+wandb: 
+wandb: 🚀 View run glean_keep25_s1226 at: https://wandb.ai/hbfreed/glean-general-grid/runs/n3zawkef
+wandb: ⭐️ View project at: https://wandb.ai/hbfreed/glean-general-grid
+wandb: Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)
+wandb: Find logs at: outputs/healed/grid_general/glean_keep25_s1226/wandb/run-20260717_125422-n3zawkef/logs

healed/grid_general/glean_keep50_s1224.console.log

@@ -0,0 +1,216 @@
+/home/henry/Documents/PythonProjects/variable-reap/.venv/lib/python3.12/site-packages/megablocks/grouped_gemm_util.py:10: UserWarning: Grouped GEMM not available.
+  warnings.warn('Grouped GEMM not available.')
+wandb: [wandb.login()] Loaded credentials for https://api.wandb.ai from /home/henry/.netrc.
+wandb: Currently logged in as: hbfreed to https://api.wandb.ai. Use `wandb login --relogin` to force relogin
+wandb: setting up run 09wxy7fc
+wandb: Tracking run with wandb version 0.28.0
+wandb: Run data is saved locally in outputs/healed/grid_general/glean_keep50_s1224/wandb/run-20260718_073554-09wxy7fc
+wandb: Run `wandb offline` to turn off syncing.
+wandb: Syncing run glean_keep50_s1224
+wandb: ⭐️ View project at https://wandb.ai/hbfreed/glean-general-grid
+wandb: 🚀 View run at https://wandb.ai/hbfreed/glean-general-grid/runs/09wxy7fc
+
+58360 cached top-128 chat trajectories / 22,295,631 unique tokens | 185 steps/epoch | 150 total steps | student params 3.70B | teacher overlap=False
+{"step": 1, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.41026050324359287, "tokens": 120000, "cumulative_loss_tokens": 120000, "grad_norm": 3.46875, "lr": 6e-06, "finish_rate": 0.869, "comp_len": 463.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 50.9, "frames": {"chat": 259}, "mem_gb": 15.93}
+The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.
+[eval step 1] sample: "To compute the rank of a given 4x4 matrix, we determine the maximum number of linearly independent rows or columns. Let's start by writing the matrix:\n\n\\[ A = \\begin{bmatrix}\n12 & -16 & 4 & 16 \\\\\n-9 &"
+{"step": 2, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.6125910406349848, "tokens": 120000, "cumulative_loss_tokens": 240000, "grad_norm": 4.0, "lr": 9e-06, "finish_rate": 0.997, "comp_len": 344.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 53.4, "frames": {"chat": 348}, "mem_gb": 15.64}
+{"step": 3, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.5558083608713001, "tokens": 120000, "cumulative_loss_tokens": 360000, "grad_norm": 3.65625, "lr": 1.2e-05, "finish_rate": 0.997, "comp_len": 324.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 55.5, "frames": {"chat": 370}, "mem_gb": 15.79}
+{"step": 4, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.21421229442053785, "tokens": 120000, "cumulative_loss_tokens": 480000, "grad_norm": 1.703125, "lr": 1.5e-05, "finish_rate": 0.852, "comp_len": 491.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 40.2, "frames": {"chat": 244}, "mem_gb": 16.02}
+{"step": 5, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.42800646155917393, "tokens": 120000, "cumulative_loss_tokens": 600000, "grad_norm": 2.015625, "lr": 1.8e-05, "finish_rate": 0.953, "comp_len": 375.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 50.6, "frames": {"chat": 320}, "mem_gb": 15.94}
+{"step": 6, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.5072242975796263, "tokens": 120000, "cumulative_loss_tokens": 720000, "grad_norm": 2.0625, "lr": 2.1e-05, "finish_rate": 0.997, "comp_len": 317.5, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 55.5, "frames": {"chat": 378}, "mem_gb": 15.54}
+{"step": 7, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.5623697880142678, "tokens": 120000, "cumulative_loss_tokens": 840000, "grad_norm": 1.7109375, "lr": 2.4e-05, "finish_rate": 0.997, "comp_len": 312.5, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 56.5, "frames": {"chat": 384}, "mem_gb": 16.04}
+{"step": 8, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.5245557690681268, "tokens": 120000, "cumulative_loss_tokens": 960000, "grad_norm": 1.6875, "lr": 2.7000000000000002e-05, "finish_rate": 0.997, "comp_len": 370.4, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 52.1, "frames": {"chat": 324}, "mem_gb": 15.88}
+{"step": 9, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.19431947070813427, "tokens": 120000, "cumulative_loss_tokens": 1080000, "grad_norm": 1.1328125, "lr": 3e-05, "finish_rate": 0.891, "comp_len": 502.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 41.9, "frames": {"chat": 239}, "mem_gb": 16.05}
+{"step": 10, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.4868344773328553, "tokens": 120000, "cumulative_loss_tokens": 1200000, "grad_norm": 1.1875, "lr": 3e-05, "finish_rate": 0.961, "comp_len": 392.2, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 47.7, "frames": {"chat": 306}, "mem_gb": 15.79}
+[eval step 10] sample: "To determine the rank of a matrix, we need to find the maximum number of linearly independent rows or columns. Let's analyze the given matrix:\n\n\\[\n\\begin{bmatrix}\n12 & -16 & 4 & 16 \\\\\n-9 & 11 & -1 & -"
+{"step": 11, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.49339593205762405, "tokens": 120000, "cumulative_loss_tokens": 1320000, "grad_norm": 1.203125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 364.7, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 52.7, "frames": {"chat": 329}, "mem_gb": 15.82}
+{"step": 12, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.2315545183274895, "tokens": 120000, "cumulative_loss_tokens": 1440000, "grad_norm": 0.7734375, "lr": 3e-05, "finish_rate": 0.882, "comp_len": 472.4, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 42.6, "frames": {"chat": 254}, "mem_gb": 16.05}
+{"step": 13, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.4039000242040182, "tokens": 120000, "cumulative_loss_tokens": 1560000, "grad_norm": 0.98828125, "lr": 3e-05, "finish_rate": 0.974, "comp_len": 439.6, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 46.2, "frames": {"chat": 273}, "mem_gb": 16.03}
+{"step": 14, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.14603102097815523, "tokens": 120000, "cumulative_loss_tokens": 1680000, "grad_norm": 0.69140625, "lr": 3e-05, "finish_rate": 0.873, "comp_len": 508.5, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 40.7, "frames": {"chat": 236}, "mem_gb": 15.95}
+{"step": 15, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.5283165326805165, "tokens": 120000, "cumulative_loss_tokens": 1800000, "grad_norm": 0.9609375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 315.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 61.2, "frames": {"chat": 381}, "mem_gb": 15.68}
+{"step": 16, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.18666007972145454, "tokens": 120000, "cumulative_loss_tokens": 1920000, "grad_norm": 0.59765625, "lr": 3e-05, "finish_rate": 0.925, "comp_len": 451.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 44.8, "frames": {"chat": 266}, "mem_gb": 15.88}
+{"step": 17, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.30335749817738933, "tokens": 120000, "cumulative_loss_tokens": 2040000, "grad_norm": 0.703125, "lr": 3e-05, "finish_rate": 0.961, "comp_len": 389.6, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 56.5, "frames": {"chat": 308}, "mem_gb": 15.87}
+{"step": 18, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.2528139771472663, "tokens": 120000, "cumulative_loss_tokens": 2160000, "grad_norm": 0.6015625, "lr": 3e-05, "finish_rate": 0.853, "comp_len": 439.6, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 47.8, "frames": {"chat": 273}, "mem_gb": 16.05}
+{"step": 19, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.4353269448962373, "tokens": 120000, "cumulative_loss_tokens": 2280000, "grad_norm": 0.76171875, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 326.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 66.2, "frames": {"chat": 368}, "mem_gb": 15.68}
+{"step": 20, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.5400869541162004, "tokens": 120000, "cumulative_loss_tokens": 2400000, "grad_norm": 0.8125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 354.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 59.3, "frames": {"chat": 339}, "mem_gb": 15.92}
+[eval step 20] sample: "To determine the rank of a matrix, we need to find the maximum number of linearly independent rows or columns. Let's start by reducing the matrix to row-echelon form (REF) or row-intercept form (RIF)."
+{"step": 21, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.2673527224904237, "tokens": 120000, "cumulative_loss_tokens": 2520000, "grad_norm": 0.609375, "lr": 3e-05, "finish_rate": 0.963, "comp_len": 402.7, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 53.1, "frames": {"chat": 298}, "mem_gb": 15.85}
+{"step": 22, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.42571741502949345, "tokens": 120000, "cumulative_loss_tokens": 2640000, "grad_norm": 0.71875, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 342.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 63.9, "frames": {"chat": 350}, "mem_gb": 15.57}
+{"step": 23, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.22796272560587774, "tokens": 120000, "cumulative_loss_tokens": 2760000, "grad_norm": 0.6015625, "lr": 3e-05, "finish_rate": 0.858, "comp_len": 487.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 47.7, "frames": {"chat": 246}, "mem_gb": 16.02}
+{"step": 24, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.4381943601517628, "tokens": 120000, "cumulative_loss_tokens": 2880000, "grad_norm": 0.7578125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 320.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 67.5, "frames": {"chat": 375}, "mem_gb": 15.75}
+{"step": 25, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.4388858132005359, "tokens": 120000, "cumulative_loss_tokens": 3000000, "grad_norm": 0.7578125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 340.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 64.3, "frames": {"chat": 352}, "mem_gb": 15.8}
+{"step": 26, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.44192338454971714, "tokens": 120000, "cumulative_loss_tokens": 3120000, "grad_norm": 0.6953125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 303.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 71.5, "frames": {"chat": 396}, "mem_gb": 15.76}
+{"step": 27, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.36236347859626017, "tokens": 120000, "cumulative_loss_tokens": 3240000, "grad_norm": 0.70703125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 331.5, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 64.3, "frames": {"chat": 362}, "mem_gb": 15.61}
+{"step": 28, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.463430282891728, "tokens": 120000, "cumulative_loss_tokens": 3360000, "grad_norm": 0.76953125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 325.2, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 65.4, "frames": {"chat": 369}, "mem_gb": 15.82}
+{"step": 29, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.4662565063331276, "tokens": 120000, "cumulative_loss_tokens": 3480000, "grad_norm": 0.7421875, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 300.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 73.0, "frames": {"chat": 399}, "mem_gb": 15.67}
+{"step": 30, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.22530132904412845, "tokens": 120000, "cumulative_loss_tokens": 3600000, "grad_norm": 1.0625, "lr": 3e-05, "finish_rate": 0.911, "comp_len": 485.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 50.5, "frames": {"chat": 247}, "mem_gb": 15.95}
+[eval step 30] sample: "To determine the rank of a matrix, we need to find the maximum number of linearly independent rows or columns. Let's analyze the given matrix:\n\n\\[\n\\begin{bmatrix}\n12 & -16 & 4 & 16 \\\\\n-9 & 11 & -1 & -"
+{"step": 31, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.08528111579132577, "tokens": 120000, "cumulative_loss_tokens": 3720000, "grad_norm": 0.515625, "lr": 3e-05, "finish_rate": 0.891, "comp_len": 521.7, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 45.5, "frames": {"chat": 230}, "mem_gb": 15.88}
+{"step": 32, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.18954650380682822, "tokens": 120000, "cumulative_loss_tokens": 3840000, "grad_norm": 0.59765625, "lr": 3e-05, "finish_rate": 0.905, "comp_len": 456.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 55.5, "frames": {"chat": 263}, "mem_gb": 16.05}
+{"step": 33, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.2115665530679437, "tokens": 120000, "cumulative_loss_tokens": 3960000, "grad_norm": 0.640625, "lr": 3e-05, "finish_rate": 0.848, "comp_len": 454.5, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 52.8, "frames": {"chat": 264}, "mem_gb": 16.04}
+{"step": 34, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.3928269195322103, "tokens": 120000, "cumulative_loss_tokens": 4080000, "grad_norm": 0.703125, "lr": 3e-05, "finish_rate": 0.991, "comp_len": 365.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 63.0, "frames": {"chat": 328}, "mem_gb": 15.9}
+{"step": 35, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.18860378520867477, "tokens": 120000, "cumulative_loss_tokens": 4200000, "grad_norm": 0.59375, "lr": 3e-05, "finish_rate": 0.925, "comp_len": 427.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 57.2, "frames": {"chat": 281}, "mem_gb": 15.97}
+{"step": 36, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.13190081692673267, "tokens": 120000, "cumulative_loss_tokens": 4320000, "grad_norm": 0.4921875, "lr": 3e-05, "finish_rate": 0.861, "comp_len": 506.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 47.9, "frames": {"chat": 237}, "mem_gb": 15.96}
+{"step": 37, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.17077195692410072, "tokens": 120000, "cumulative_loss_tokens": 4440000, "grad_norm": 0.5078125, "lr": 3e-05, "finish_rate": 0.91, "comp_len": 431.7, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 52.3, "frames": {"chat": 278}, "mem_gb": 16.05}
+{"step": 38, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.38257827037526293, "tokens": 120000, "cumulative_loss_tokens": 4560000, "grad_norm": 0.69140625, "lr": 3e-05, "finish_rate": 0.955, "comp_len": 387.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 62.7, "frames": {"chat": 310}, "mem_gb": 15.92}
+{"step": 39, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.22061063767167896, "tokens": 120000, "cumulative_loss_tokens": 4680000, "grad_norm": 0.54296875, "lr": 3e-05, "finish_rate": 0.959, "comp_len": 406.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 52.9, "frames": {"chat": 295}, "mem_gb": 15.86}
+{"step": 40, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.2498053479500736, "tokens": 120000, "cumulative_loss_tokens": 4800000, "grad_norm": 0.58203125, "lr": 3e-05, "finish_rate": 0.957, "comp_len": 364.7, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 60.8, "frames": {"chat": 329}, "mem_gb": 15.87}
+[eval step 40] sample: "To compute the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. Here's how we can do it step-by-step:\n\n1. **Formulate the Matrix**:\n   \\"
+{"step": 41, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.23681824228313442, "tokens": 120000, "cumulative_loss_tokens": 4920000, "grad_norm": 0.5546875, "lr": 3e-05, "finish_rate": 0.905, "comp_len": 456.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 52.9, "frames": {"chat": 263}, "mem_gb": 15.99}
+{"step": 42, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.26467789245061574, "tokens": 120000, "cumulative_loss_tokens": 5040000, "grad_norm": 0.53125, "lr": 3e-05, "finish_rate": 0.981, "comp_len": 381.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 57.5, "frames": {"chat": 315}, "mem_gb": 15.87}
+{"step": 43, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.36451303250255684, "tokens": 120000, "cumulative_loss_tokens": 5160000, "grad_norm": 0.65625, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 339.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 69.4, "frames": {"chat": 354}, "mem_gb": 15.82}
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+[eval step 50] sample: "To solve this problem, we need to determine the rank of the given 4x4 matrix. The rank of a matrix is defined as the maximum number of linearly independent rows or columns in the matrix.\n\nLet's repres"
+checkpoint snapshot queued -> outputs/healed/grid_general/glean_keep50_s1224/step0050
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+[eval step 90] sample: 'To find the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. A matrix is said to be of rank \\( r \\) if it has \\( r \\) linearly independe'
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+[eval step 100] sample: 'To compute the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. \n\nGiven the matrix:\n\n\\[\n\\begin{bmatrix}\n12 & -16 & 4 & 16 \\\\\n-9 & 11 & -'
+checkpoint snapshot queued -> outputs/healed/grid_general/glean_keep50_s1224/step0100
+{"step": 101, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.3254158294330351, "tokens": 120000, "cumulative_loss_tokens": 12120000, "grad_norm": 0.578125, "lr": 3e-05, "finish_rate": 0.994, "comp_len": 343.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 57.7, "frames": {"chat": 349}, "mem_gb": 15.72}
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+[eval step 110] sample: 'To compute the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. A matrix is said to be of rank \\( r \\) if it has \\( r \\) linearly indepe'
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+[eval step 120] sample: 'To compute the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. \n\nGiven the matrix:\n\n\\[\n\\begin{bmatrix}\n12 & -16 & 4 & 16 \\\\\n-9 & 11 & -'
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+[eval step 130] sample: "To compute the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. \n\nLet's represent the given matrix \\( A \\) as follows:\n\\[ A = \\begin{bma"
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+[eval step 140] sample: "To compute the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. \n\nLet's represent the given matrix \\( A \\) as follows:\n\\[ A = \\begin{bma"
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+{"step": 145, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.22945189654111553, "tokens": 120000, "cumulative_loss_tokens": 17400000, "grad_norm": 0.5, "lr": 3e-05, "finish_rate": 0.941, "comp_len": 441.2, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 47.1, "frames": {"chat": 272}, "mem_gb": 15.78}
+{"step": 146, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.4198826794961778, "tokens": 120000, "cumulative_loss_tokens": 17520000, "grad_norm": 0.6640625, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 326.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 63.0, "frames": {"chat": 368}, "mem_gb": 15.7}
+{"step": 147, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.34152062557879836, "tokens": 120000, "cumulative_loss_tokens": 17640000, "grad_norm": 0.60546875, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 365.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 60.4, "frames": {"chat": 328}, "mem_gb": 15.73}
+{"step": 148, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.33140167462996517, "tokens": 120000, "cumulative_loss_tokens": 17760000, "grad_norm": 0.5859375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 368.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 56.1, "frames": {"chat": 326}, "mem_gb": 15.95}
+{"step": 149, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.38146266262708234, "tokens": 120000, "cumulative_loss_tokens": 17880000, "grad_norm": 0.62109375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 329.7, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 62.9, "frames": {"chat": 364}, "mem_gb": 15.89}
+{"step": 150, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.23909615996540523, "tokens": 120000, "cumulative_loss_tokens": 18000000, "grad_norm": 0.48046875, "lr": 3e-05, "finish_rate": 0.97, "comp_len": 405.4, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 45.6, "frames": {"chat": 296}, "mem_gb": 15.93}
+[eval step 150] sample: "To compute the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. \n\nLet's break down the steps:\n\n1. **Formulate the Matrix:**\n   \\[\n   A ="
+checkpoint snapshot queued -> outputs/healed/grid_general/glean_keep50_s1224/step0150
+wandb: updating run metadata
+wandb: uploading output.log; uploading wandb-summary.json; uploading config.yaml
+wandb: 
+wandb: Run history:
+wandb:               comp_len ▂▇▅▁▅▂▂▁▂█▅▂▅▄▃▄▃▃▄▅▃▁▆▂▄▂▂▁▂▄▆▂▃▃▂▃▃▅▃▄
+wandb: cumulative_loss_tokens ▁▁▂▂▂▂▂▂▂▂▃▃▃▃▃▄▄▄▄▄▅▅▅▆▆▆▆▆▆▇▇▇▇▇▇▇▇███
+wandb:                  epoch ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
+wandb:            finish_rate █▇█▅▂█▄▇██▃▆█▇██▅▆▆▇█▃██▇█▇▁███████▃█▆▇▇
+wandb:        forward_topk_kl █▂▁▇▅▃▂▆█▆▆▂▁▂▅▄▃▃▆▃▂▅▂▅▅▁▃▂▅▃▅▃▄▃▂▄▅▃▆▂
+wandb:              grad_norm █▅▃▂▁▁▂▂▂▂▂▁▁▁▁▁▁▁▂▂▁▁▁▁▁▂▁▃▁▁▁▁▂▂▁▁▂▁▁▁
+wandb:                     lr ▁▂▃█████████████████████████████████████
+wandb:                 mem_gb ▇▁▇▇▆▇▇▄▆▅▅▇▅▄▆▄▂▇▇▁▃▇▆▃▂▅▃█▇█▅▂▃▃▇▃█▇▄▅
+wandb:                   step ▁▁▁▂▂▂▃▃▃▃▃▃▄▄▄▄▄▄▄▄▆▆▆▆▆▆▆▆▇▇▇▇▇▇▇█████
+wandb:               t_data_s ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
+wandb:                     +3 ...
+wandb: 
+wandb: Run summary:
+wandb:               comp_len 405.4
+wandb: cumulative_loss_tokens 18000000
+wandb:                  epoch 0
+wandb:            finish_rate 0.97
+wandb:        forward_topk_kl 0.2391
+wandb:              grad_norm 0.48047
+wandb:                     lr 3e-05
+wandb:                 mem_gb 15.93
+wandb:                   step 150
+wandb:               t_data_s 0
+wandb:                     +4 ...
+wandb: 
+wandb: 🚀 View run glean_keep50_s1224 at: https://wandb.ai/hbfreed/glean-general-grid/runs/09wxy7fc
+wandb: ⭐️ View project at: https://wandb.ai/hbfreed/glean-general-grid
+wandb: Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)
+wandb: Find logs at: outputs/healed/grid_general/glean_keep50_s1224/wandb/run-20260718_073554-09wxy7fc/logs

healed/grid_general/glean_keep50_s1225.console.log

@@ -0,0 +1,216 @@
+/home/henry/Documents/PythonProjects/variable-reap/.venv/lib/python3.12/site-packages/megablocks/grouped_gemm_util.py:10: UserWarning: Grouped GEMM not available.
+  warnings.warn('Grouped GEMM not available.')
+wandb: [wandb.login()] Loaded credentials for https://api.wandb.ai from /home/henry/.netrc.
+wandb: Currently logged in as: hbfreed to https://api.wandb.ai. Use `wandb login --relogin` to force relogin
+wandb: setting up run 5uic08kb
+wandb: Tracking run with wandb version 0.28.0
+wandb: Run data is saved locally in outputs/healed/grid_general/glean_keep50_s1225/wandb/run-20260718_073824-5uic08kb
+wandb: Run `wandb offline` to turn off syncing.
+wandb: Syncing run glean_keep50_s1225
+wandb: ⭐️ View project at https://wandb.ai/hbfreed/glean-general-grid
+wandb: 🚀 View run at https://wandb.ai/hbfreed/glean-general-grid/runs/5uic08kb
+
+58360 cached top-128 chat trajectories / 22,295,631 unique tokens | 185 steps/epoch | 150 total steps | student params 3.70B | teacher overlap=False
+{"step": 1, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.6853121227722615, "tokens": 120000, "cumulative_loss_tokens": 120000, "grad_norm": 4.5625, "lr": 6e-06, "finish_rate": 0.997, "comp_len": 362.5, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 59.5, "frames": {"chat": 331}, "mem_gb": 15.8}
+The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.
+[eval step 1] sample: "To determine how many months it will take Jamie to reach their goal of $1,000,000, we need to consider both the monthly savings and the interest earned on the growing savings.\n\nLet's denote \\( n \\) as"
+{"step": 2, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.6300968874294932, "tokens": 120000, "cumulative_loss_tokens": 240000, "grad_norm": 4.15625, "lr": 9e-06, "finish_rate": 0.997, "comp_len": 350.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 54.0, "frames": {"chat": 342}, "mem_gb": 15.67}
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+{"step": 4, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.5611744172318528, "tokens": 120000, "cumulative_loss_tokens": 480000, "grad_norm": 2.96875, "lr": 1.5e-05, "finish_rate": 0.997, "comp_len": 348.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 57.6, "frames": {"chat": 344}, "mem_gb": 15.79}
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+[eval step 10] sample: 'To determine how many months it will take Jamie to reach their goal of $1,000,000, we need to consider both their monthly savings and the interest earned on their investment account.\n\n1. **Monthly Sav'
+{"step": 11, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.39639907065903146, "tokens": 120000, "cumulative_loss_tokens": 1320000, "grad_norm": 0.9921875, "lr": 3e-05, "finish_rate": 0.959, "comp_len": 377.4, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 51.6, "frames": {"chat": 318}, "mem_gb": 15.95}
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+[eval step 20] sample: 'To solve this problem, we need to determine how many months it will take Jamie to reach their savings goal of $1,000,000, given their monthly savings and the interest earned on their investment.\n\n**St'
+{"step": 21, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.41813104864781103, "tokens": 120000, "cumulative_loss_tokens": 2520000, "grad_norm": 0.765625, "lr": 3e-05, "finish_rate": 0.98, "comp_len": 401.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 51.9, "frames": {"chat": 299}, "mem_gb": 16.05}
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+{"step": 29, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.26630948204637195, "tokens": 120000, "cumulative_loss_tokens": 3480000, "grad_norm": 0.59375, "lr": 3e-05, "finish_rate": 0.926, "comp_len": 425.5, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 46.7, "frames": {"chat": 282}, "mem_gb": 15.9}
+{"step": 30, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.404437967997541, "tokens": 120000, "cumulative_loss_tokens": 3600000, "grad_norm": 0.67578125, "lr": 3e-05, "finish_rate": 0.979, "comp_len": 356.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 53.7, "frames": {"chat": 337}, "mem_gb": 15.93}
+[eval step 30] sample: 'To solve this problem, we need to determine how many months it will take Jamie to reach their savings goal of $1,000,000, given their monthly savings and the interest earned on their investment.\n\n**St'
+{"step": 31, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.2456684964166023, "tokens": 120000, "cumulative_loss_tokens": 3720000, "grad_norm": 0.578125, "lr": 3e-05, "finish_rate": 0.891, "comp_len": 436.4, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 47.5, "frames": {"chat": 275}, "mem_gb": 15.99}
+{"step": 32, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.43714953695784015, "tokens": 120000, "cumulative_loss_tokens": 3840000, "grad_norm": 0.69140625, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 377.4, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 52.8, "frames": {"chat": 318}, "mem_gb": 15.8}
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+[eval step 100] sample: 'To solve this problem, we need to determine how many months it will take Jamie to reach a savings goal of $1,000,000 given their monthly savings and investment earnings.\n\n**Step 1: Calculate the total'
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+{"step": 137, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.32028547865971924, "tokens": 120000, "cumulative_loss_tokens": 16440000, "grad_norm": 0.609375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 339.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 59.5, "frames": {"chat": 354}, "mem_gb": 15.73}
+{"step": 138, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.17074598619239406, "tokens": 120000, "cumulative_loss_tokens": 16560000, "grad_norm": 0.4609375, "lr": 3e-05, "finish_rate": 0.93, "comp_len": 444.4, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 46.8, "frames": {"chat": 270}, "mem_gb": 15.99}
+{"step": 139, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.33173287250131056, "tokens": 120000, "cumulative_loss_tokens": 16680000, "grad_norm": 0.58203125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 349.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 54.5, "frames": {"chat": 343}, "mem_gb": 15.77}
+{"step": 140, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.07786983747505583, "tokens": 120000, "cumulative_loss_tokens": 16800000, "grad_norm": 0.361328125, "lr": 3e-05, "finish_rate": 0.845, "comp_len": 517.2, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 40.4, "frames": {"chat": 232}, "mem_gb": 15.96}
+[eval step 140] sample: 'To solve this problem, we need to determine how many months it will take Jamie to reach a savings goal of $1,000,000 given their monthly savings and investment earnings.\n\n**Step 1: Calculate the month'
+{"step": 141, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.3108461571379565, "tokens": 120000, "cumulative_loss_tokens": 16920000, "grad_norm": 0.58203125, "lr": 3e-05, "finish_rate": 0.98, "comp_len": 346.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 55.0, "frames": {"chat": 346}, "mem_gb": 15.91}
+{"step": 142, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.2508645673315041, "tokens": 120000, "cumulative_loss_tokens": 17040000, "grad_norm": 0.51171875, "lr": 3e-05, "finish_rate": 0.951, "comp_len": 392.2, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 48.7, "frames": {"chat": 306}, "mem_gb": 15.99}
+{"step": 143, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.1583715583874844, "tokens": 120000, "cumulative_loss_tokens": 17160000, "grad_norm": 0.419921875, "lr": 3e-05, "finish_rate": 0.923, "comp_len": 439.6, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 44.4, "frames": {"chat": 273}, "mem_gb": 16.04}
+{"step": 144, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.17659224952206326, "tokens": 120000, "cumulative_loss_tokens": 17280000, "grad_norm": 0.45703125, "lr": 3e-05, "finish_rate": 0.959, "comp_len": 451.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 43.7, "frames": {"chat": 266}, "mem_gb": 15.86}
+{"step": 145, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.2698487444297721, "tokens": 120000, "cumulative_loss_tokens": 17400000, "grad_norm": 0.52734375, "lr": 3e-05, "finish_rate": 0.936, "comp_len": 401.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 48.5, "frames": {"chat": 299}, "mem_gb": 16.06}
+{"step": 146, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.18426784236074115, "tokens": 120000, "cumulative_loss_tokens": 17520000, "grad_norm": 0.46875, "lr": 3e-05, "finish_rate": 0.88, "comp_len": 463.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 45.9, "frames": {"chat": 259}, "mem_gb": 16.14}
+{"step": 147, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.21372967740772292, "tokens": 120000, "cumulative_loss_tokens": 17640000, "grad_norm": 0.50390625, "lr": 3e-05, "finish_rate": 0.966, "comp_len": 369.2, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 50.1, "frames": {"chat": 325}, "mem_gb": 15.88}
+{"step": 148, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.20076305093116437, "tokens": 120000, "cumulative_loss_tokens": 17760000, "grad_norm": 0.48046875, "lr": 3e-05, "finish_rate": 0.923, "comp_len": 442.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 44.9, "frames": {"chat": 271}, "mem_gb": 15.95}
+{"step": 149, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.3275998032643149, "tokens": 120000, "cumulative_loss_tokens": 17880000, "grad_norm": 0.578125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 334.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 54.0, "frames": {"chat": 359}, "mem_gb": 15.74}
+{"step": 150, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.31545391854941845, "tokens": 120000, "cumulative_loss_tokens": 18000000, "grad_norm": 0.578125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 347.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 54.9, "frames": {"chat": 345}, "mem_gb": 15.8}
+[eval step 150] sample: 'To solve this problem, we need to determine how many months it will take Jamie to reach a savings goal of $1,000,000 given their monthly savings and investment earnings.\n\n**Step 1: Calculate the month'
+checkpoint snapshot queued -> outputs/healed/grid_general/glean_keep50_s1225/step0150
+wandb: updating run metadata
+wandb: uploading output.log; uploading config.yaml
+wandb: 
+wandb: Run history:
+wandb:               comp_len ▃▄▆▄▅▂▄▅▂▅▆▂▁▃▄▂▂▃█▃▄▂▆▄▄▄▅▆▂▂▂▄▃▅▂▆█▃▄▆
+wandb: cumulative_loss_tokens ▁▁▁▁▂▂▂▂▂▂▃▃▃▃▃▄▄▄▄▄▄▅▅▅▅▅▅▆▆▆▇▇▇▇▇▇████
+wandb:                  epoch ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
+wandb:            finish_rate █▃▄▆▇▆█▄█▄█▄▅▅███▇▆██▅▁█▂▃█▇▇████▇▇▃▇▆▇█
+wandb:        forward_topk_kl █▅▂▂▅▄▃▁▂▄▅▅▁▅▂▃▂▁▄▂▄▄▁▃▂▃▃▂▂▃▃▂▄▄▄▁▂▂▁▃
+wandb:              grad_norm █▇▃▂▂▂▁▂▂▁▄▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
+wandb:                     lr ▁▆██████████████████████████████████████
+wandb:                 mem_gb ▂▃▅▇▄▆▄▄▆▇█▃▆▁▃▆█▇▆▅▆▁▅█▃▇▆▆▇▅▆▅█▅▄▇▄▆▅▅
+wandb:                   step ▁▁▁▂▂▂▂▃▃▃▄▄▄▄▄▄▅▅▅▅▅▅▅▅▅▆▆▆▆▇▇▇▇▇▇▇████
+wandb:               t_data_s ▁▁▁▁▁█▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
+wandb:                     +3 ...
+wandb: 
+wandb: Run summary:
+wandb:               comp_len 347.8
+wandb: cumulative_loss_tokens 18000000
+wandb:                  epoch 0
+wandb:            finish_rate 0.997
+wandb:        forward_topk_kl 0.31545
+wandb:              grad_norm 0.57812
+wandb:                     lr 3e-05
+wandb:                 mem_gb 15.8
+wandb:                   step 150
+wandb:               t_data_s 0
+wandb:                     +4 ...
+wandb: 
+wandb: 🚀 View run glean_keep50_s1225 at: https://wandb.ai/hbfreed/glean-general-grid/runs/5uic08kb
+wandb: ⭐️ View project at: https://wandb.ai/hbfreed/glean-general-grid
+wandb: Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)
+wandb: Find logs at: outputs/healed/grid_general/glean_keep50_s1225/wandb/run-20260718_073824-5uic08kb/logs

healed/grid_general/glean_keep50_s1226.console.log

@@ -0,0 +1,216 @@
+/home/henry/Documents/PythonProjects/variable-reap/.venv/lib/python3.12/site-packages/megablocks/grouped_gemm_util.py:10: UserWarning: Grouped GEMM not available.
+  warnings.warn('Grouped GEMM not available.')
+wandb: [wandb.login()] Loaded credentials for https://api.wandb.ai from /home/henry/.netrc.
+wandb: Currently logged in as: hbfreed to https://api.wandb.ai. Use `wandb login --relogin` to force relogin
+wandb: setting up run frl49wbd
+wandb: Tracking run with wandb version 0.28.0
+wandb: Run data is saved locally in outputs/healed/grid_general/glean_keep50_s1226/wandb/run-20260718_074054-frl49wbd
+wandb: Run `wandb offline` to turn off syncing.
+wandb: Syncing run glean_keep50_s1226
+wandb: ⭐️ View project at https://wandb.ai/hbfreed/glean-general-grid
+wandb: 🚀 View run at https://wandb.ai/hbfreed/glean-general-grid/runs/frl49wbd
+
+58360 cached top-128 chat trajectories / 22,295,631 unique tokens | 185 steps/epoch | 150 total steps | student params 3.70B | teacher overlap=False
+{"step": 1, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.44869900887217373, "tokens": 120000, "cumulative_loss_tokens": 120000, "grad_norm": 3.21875, "lr": 6e-06, "finish_rate": 0.945, "comp_len": 389.6, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 54.9, "frames": {"chat": 308}, "mem_gb": 15.9}
+The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.
+[eval step 1] sample: "to solve the problem, let's start by expressing the terms of the arithmetic sequence. in an arithmetic sequence, each term can be written as \\(a_n = a_1 + (n-1)d\\), where \\(a_1\\) is the first term and"
+{"step": 2, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.17543124351594597, "tokens": 120000, "cumulative_loss_tokens": 240000, "grad_norm": 2.46875, "lr": 9e-06, "finish_rate": 0.754, "comp_len": 568.7, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 37.8, "frames": {"chat": 211}, "mem_gb": 16.04}
+{"step": 3, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.3475665108046184, "tokens": 120000, "cumulative_loss_tokens": 360000, "grad_norm": 2.625, "lr": 1.2e-05, "finish_rate": 0.915, "comp_len": 427.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 45.0, "frames": {"chat": 281}, "mem_gb": 16.04}
+{"step": 4, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.6226910541420803, "tokens": 120000, "cumulative_loss_tokens": 480000, "grad_norm": 3.453125, "lr": 1.5e-05, "finish_rate": 0.997, "comp_len": 327.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 55.3, "frames": {"chat": 367}, "mem_gb": 15.59}
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+{"step": 6, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.4530718907893946, "tokens": 120000, "cumulative_loss_tokens": 720000, "grad_norm": 2.0, "lr": 2.1e-05, "finish_rate": 0.919, "comp_len": 442.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 46.5, "frames": {"chat": 271}, "mem_gb": 16.06}
+{"step": 7, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.3028890410714472, "tokens": 120000, "cumulative_loss_tokens": 840000, "grad_norm": 1.2109375, "lr": 2.4e-05, "finish_rate": 0.922, "comp_len": 446.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 44.4, "frames": {"chat": 269}, "mem_gb": 16.03}
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+{"step": 9, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.2287902546222632, "tokens": 120000, "cumulative_loss_tokens": 1080000, "grad_norm": 0.9140625, "lr": 3e-05, "finish_rate": 0.864, "comp_len": 493.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 42.2, "frames": {"chat": 243}, "mem_gb": 16.04}
+{"step": 10, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.33752491750195623, "tokens": 120000, "cumulative_loss_tokens": 1200000, "grad_norm": 0.99609375, "lr": 3e-05, "finish_rate": 0.952, "comp_len": 409.6, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 47.7, "frames": {"chat": 293}, "mem_gb": 15.96}
+[eval step 10] sample: 'to solve this problem, we need to understand the properties of an arithmetic sequence. in an arithmetic sequence, each term is a constant plus a multiple of the common difference \\(d\\).\n\ngiven: \\(a_1 '
+{"step": 11, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.19984550218141328, "tokens": 120000, "cumulative_loss_tokens": 1320000, "grad_norm": 0.84765625, "lr": 3e-05, "finish_rate": 0.872, "comp_len": 495.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 42.7, "frames": {"chat": 242}, "mem_gb": 16.04}
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+{"step": 14, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.25008603849976013, "tokens": 120000, "cumulative_loss_tokens": 1680000, "grad_norm": 0.83984375, "lr": 3e-05, "finish_rate": 0.884, "comp_len": 463.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 45.6, "frames": {"chat": 259}, "mem_gb": 16.04}
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+{"step": 20, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.45042725155310087, "tokens": 120000, "cumulative_loss_tokens": 2400000, "grad_norm": 0.7890625, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 349.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 55.3, "frames": {"chat": 343}, "mem_gb": 15.72}
+[eval step 20] sample: "to solve the problem, let's first define the terms of the arithmetic sequence. in an arithmetic sequence, each term is obtained by adding a constant difference, d, to the previous term. the nth term, "
+{"step": 21, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.13549072327846662, "tokens": 120000, "cumulative_loss_tokens": 2520000, "grad_norm": 0.828125, "lr": 3e-05, "finish_rate": 0.889, "comp_len": 491.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 41.4, "frames": {"chat": 244}, "mem_gb": 16.04}
+{"step": 22, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.26835141508476806, "tokens": 120000, "cumulative_loss_tokens": 2640000, "grad_norm": 0.609375, "lr": 3e-05, "finish_rate": 0.965, "comp_len": 354.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 52.5, "frames": {"chat": 339}, "mem_gb": 15.88}
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+[eval step 130] sample: "to solve the problem, let's first understand the properties of an arithmetic sequence. in an arithmetic sequence, each term is obtained by adding a constant difference, denoted as d, to the previous t"
+{"step": 131, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.20706040653378394, "tokens": 120000, "cumulative_loss_tokens": 15720000, "grad_norm": 0.515625, "lr": 3e-05, "finish_rate": 0.97, "comp_len": 400.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 48.3, "frames": {"chat": 300}, "mem_gb": 15.84}
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+{"step": 139, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.3743262184455991, "tokens": 120000, "cumulative_loss_tokens": 16680000, "grad_norm": 0.64453125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 320.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 59.3, "frames": {"chat": 374}, "mem_gb": 15.9}
+{"step": 140, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.18988548532904437, "tokens": 120000, "cumulative_loss_tokens": 16800000, "grad_norm": 0.50390625, "lr": 3e-05, "finish_rate": 0.889, "comp_len": 428.6, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 45.9, "frames": {"chat": 280}, "mem_gb": 16.07}
+[eval step 140] sample: "to solve the problem, let's first understand the properties of an arithmetic sequence. in an arithmetic sequence, each term is obtained by adding a constant difference, denoted as \\(d\\). the \\(n\\)-th "
+{"step": 141, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.14103970303904886, "tokens": 120000, "cumulative_loss_tokens": 16920000, "grad_norm": 0.4453125, "lr": 3e-05, "finish_rate": 0.884, "comp_len": 434.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 46.2, "frames": {"chat": 276}, "mem_gb": 15.92}
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+{"step": 143, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.38401385176287345, "tokens": 120000, "cumulative_loss_tokens": 17160000, "grad_norm": 0.71875, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 333.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 53.5, "frames": {"chat": 360}, "mem_gb": 15.59}
+{"step": 144, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.37136507782653594, "tokens": 120000, "cumulative_loss_tokens": 17280000, "grad_norm": 0.625, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 328.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 59.3, "frames": {"chat": 365}, "mem_gb": 15.77}
+{"step": 145, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.2997990082692355, "tokens": 120000, "cumulative_loss_tokens": 17400000, "grad_norm": 0.52734375, "lr": 3e-05, "finish_rate": 0.988, "comp_len": 355.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 51.0, "frames": {"chat": 338}, "mem_gb": 15.85}
+{"step": 146, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.1491951719417237, "tokens": 120000, "cumulative_loss_tokens": 17520000, "grad_norm": 0.48046875, "lr": 3e-05, "finish_rate": 0.888, "comp_len": 434.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 47.4, "frames": {"chat": 276}, "mem_gb": 15.96}
+{"step": 147, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.0753031477817179, "tokens": 120000, "cumulative_loss_tokens": 17640000, "grad_norm": 0.412109375, "lr": 3e-05, "finish_rate": 0.761, "comp_len": 574.2, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 39.0, "frames": {"chat": 209}, "mem_gb": 16.04}
+{"step": 148, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.3350944878866586, "tokens": 120000, "cumulative_loss_tokens": 17760000, "grad_norm": 0.625, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 316.6, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 57.8, "frames": {"chat": 379}, "mem_gb": 15.64}
+{"step": 149, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.2944304664655899, "tokens": 120000, "cumulative_loss_tokens": 17880000, "grad_norm": 0.578125, "lr": 3e-05, "finish_rate": 0.928, "comp_len": 390.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 50.9, "frames": {"chat": 307}, "mem_gb": 15.98}
+{"step": 150, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.19635752345559498, "tokens": 120000, "cumulative_loss_tokens": 18000000, "grad_norm": 0.484375, "lr": 3e-05, "finish_rate": 0.942, "comp_len": 409.6, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 49.3, "frames": {"chat": 293}, "mem_gb": 16.02}
+[eval step 150] sample: "to solve the problem, let's first understand the properties of an arithmetic sequence. in an arithmetic sequence, each term is obtained by adding a constant difference, denoted as \\(d\\). the \\(n\\)-th "
+checkpoint snapshot queued -> outputs/healed/grid_general/glean_keep50_s1226/step0150
+wandb: updating run metadata
+wandb: uploading output.log; uploading wandb-summary.json; uploading config.yaml
+wandb: 
+wandb: Run history:
+wandb:               comp_len █▂▄▂▂▆▁▃▃▂▂▅▃▆▃▄▃▆▅▆▇▄▂▃▃▄▂▃▄▂▂▅▂▂▁▃▄▂▂▄
+wandb: cumulative_loss_tokens ▁▁▁▁▁▂▂▂▂▂▂▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▅▅▆▆▆▇▇▇▇▇███
+wandb:                  epoch ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
+wandb:            finish_rate █▆██▆▆▅▃▇█▄█▅▆██▅▅▃█▃█████▆▁▅███▄█▇▆▂▁█▅
+wandb:        forward_topk_kl █▄▆▄▇▄▁▄▃▆▄▃▄▄▆▅▄▅▆▃▆▄▆▅▁▄▆▅▅▆▅▆▆▅▅▃▃▆▃▄
+wandb:              grad_norm █▅▃▄▃▂▃▂▂▂▂▁▁▂▂▂▂▂▂▁▁▂▂▂▂▂▂▂▂▂▂▁▁▂▁▂▁▁▁▁
+wandb:                     lr ▁▅▆█████████████████████████████████████
+wandb:                 mem_gb ▆▇▃▂▄▆▄▄█▄▅▄▄▇▃▆▅▃▄▆▇▆▅█▃▆▇▂▃▅▇▁▅▅▄▂▃▅▆▇
+wandb:                   step ▁▁▂▂▂▂▂▃▃▃▄▄▄▄▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇▇███
+wandb:               t_data_s ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
+wandb:                     +3 ...
+wandb: 
+wandb: Run summary:
+wandb:               comp_len 409.6
+wandb: cumulative_loss_tokens 18000000
+wandb:                  epoch 0
+wandb:            finish_rate 0.942
+wandb:        forward_topk_kl 0.19636
+wandb:              grad_norm 0.48438
+wandb:                     lr 3e-05
+wandb:                 mem_gb 16.02
+wandb:                   step 150
+wandb:               t_data_s 0
+wandb:                     +4 ...
+wandb: 
+wandb: 🚀 View run glean_keep50_s1226 at: https://wandb.ai/hbfreed/glean-general-grid/runs/frl49wbd
+wandb: ⭐️ View project at: https://wandb.ai/hbfreed/glean-general-grid
+wandb: Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)
+wandb: Find logs at: outputs/healed/grid_general/glean_keep50_s1226/wandb/run-20260718_074054-frl49wbd/logs

healed/grid_general/glean_keep75_s1224.console.log

@@ -0,0 +1,216 @@
+/home/henry/Documents/PythonProjects/variable-reap/.venv/lib/python3.12/site-packages/megablocks/grouped_gemm_util.py:10: UserWarning: Grouped GEMM not available.
+  warnings.warn('Grouped GEMM not available.')
+wandb: [wandb.login()] Loaded credentials for https://api.wandb.ai from /home/henry/.netrc.
+wandb: Currently logged in as: hbfreed to https://api.wandb.ai. Use `wandb login --relogin` to force relogin
+wandb: setting up run kv8yaoo0
+wandb: Tracking run with wandb version 0.28.0
+wandb: Run data is saved locally in outputs/healed/grid_general/glean_keep75_s1224/wandb/run-20260718_162400-kv8yaoo0
+wandb: Run `wandb offline` to turn off syncing.
+wandb: Syncing run glean_keep75_s1224
+wandb: ⭐️ View project at https://wandb.ai/hbfreed/glean-general-grid
+wandb: 🚀 View run at https://wandb.ai/hbfreed/glean-general-grid/runs/kv8yaoo0
+
+58360 cached top-128 chat trajectories / 22,295,631 unique tokens | 185 steps/epoch | 150 total steps | student params 5.31B | teacher overlap=False
+{"step": 1, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.13071662265943984, "tokens": 120000, "cumulative_loss_tokens": 120000, "grad_norm": 1.8671875, "lr": 6e-06, "finish_rate": 0.869, "comp_len": 463.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 58.2, "frames": {"chat": 259}, "mem_gb": 21.93}
+The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.
+[eval step 1] sample: "To compute the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. Here's how we can do it step-by-step:\n\n1. **Write down the matrix:**\n   "
+{"step": 2, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.19075942706478138, "tokens": 120000, "cumulative_loss_tokens": 240000, "grad_norm": 1.75, "lr": 9e-06, "finish_rate": 0.997, "comp_len": 344.8, "t_data_s": 0.1, "t_rollout_s": 0.0, "t_step_s": 61.5, "frames": {"chat": 348}, "mem_gb": 21.69}
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+[eval step 10] sample: 'To compute the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns. A matrix is said to be full rank if all its rows (or columns) are linearly independent'
+{"step": 11, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.16721250823534403, "tokens": 120000, "cumulative_loss_tokens": 1320000, "grad_norm": 0.94921875, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 364.7, "t_data_s": 0.1, "t_rollout_s": 0.0, "t_step_s": 61.6, "frames": {"chat": 329}, "mem_gb": 21.87}
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+[eval step 20] sample: 'To find the rank of a matrix, we need to determine the linearly independent rows or columns. A matrix is row-rank-reduced if it has no row with a leading coefficient of zero, and column-rank-reduced i'
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+[eval step 30] sample: "To find the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns. \n\nLet's start by examining the rows:\n\n1. **Row 1:** \\(12, -16, 4, 16\\)\n2. **Row 2:** \\( -"
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+[eval step 40] sample: 'To compute the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. We can do this by using Python and the `numpy` library, which provides a'
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+[eval step 50] sample: "To solve this problem, we need to determine the rank of the given 4x4 matrix. The rank of a matrix is the maximum number of linearly independent rows or columns in the matrix.\n\nLet's break down the st"
+checkpoint snapshot queued -> outputs/healed/grid_general/glean_keep75_s1224/step0050
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+[eval step 60] sample: "To compute the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. \n\nLet's represent the given 4x4 matrix:\n\n\\[\n\\begin{bmatrix}\n12 & -16 & 4"
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+[eval step 70] sample: 'To find the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns. In this 4x4 matrix:\n\n\\[\n\\begin{bmatrix}\n12 & -16 & 4 & 16 \\\\\n-9 & 11 & -1 & -10 \\\\\n0 & 1'
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+[eval step 80] sample: 'To find the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns. A matrix is said to be of rank \\( r \\) if it has \\( r \\) linearly independent rows or col'
+{"step": 81, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.08265764909470454, "tokens": 120000, "cumulative_loss_tokens": 9720000, "grad_norm": 0.400390625, "lr": 3e-05, "finish_rate": 0.965, "comp_len": 416.7, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 53.8, "frames": {"chat": 288}, "mem_gb": 21.9}
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+[eval step 90] sample: 'To find the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. A matrix is said to be of rank \\( r \\) if it has \\( r \\) linearly independe'
+{"step": 91, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.0862862615711987, "tokens": 120000, "cumulative_loss_tokens": 10920000, "grad_norm": 0.423828125, "lr": 3e-05, "finish_rate": 0.919, "comp_len": 442.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 52.0, "frames": {"chat": 271}, "mem_gb": 22.08}
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+{"step": 98, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.08199445860886481, "tokens": 120000, "cumulative_loss_tokens": 11760000, "grad_norm": 0.4453125, "lr": 3e-05, "finish_rate": 0.964, "comp_len": 388.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 59.8, "frames": {"chat": 309}, "mem_gb": 21.93}
+{"step": 99, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.13870517097897828, "tokens": 120000, "cumulative_loss_tokens": 11880000, "grad_norm": 0.5234375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 306.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 70.8, "frames": {"chat": 391}, "mem_gb": 21.82}
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+[eval step 100] sample: 'To compute the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. \n\nGiven the matrix:\n\n\\[\n\\begin{bmatrix}\n12 & -16 & 4 & 16 \\\\\n-9 & 11 & -'
+checkpoint snapshot queued -> outputs/healed/grid_general/glean_keep75_s1224/step0100
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+[eval step 110] sample: 'To find the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. A matrix is said to be of rank \\( r \\) if it has \\( r \\) linearly independe'
+{"step": 111, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.14512051825829161, "tokens": 120000, "cumulative_loss_tokens": 13320000, "grad_norm": 0.51953125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 327.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 65.5, "frames": {"chat": 366}, "mem_gb": 21.7}
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+[eval step 120] sample: 'To compute the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. A matrix is rank-deficient if it has fewer linearly independent rows or '
+{"step": 121, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.09910357171046393, "tokens": 120000, "cumulative_loss_tokens": 14520000, "grad_norm": 0.44140625, "lr": 3e-05, "finish_rate": 0.956, "comp_len": 408.2, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 55.6, "frames": {"chat": 294}, "mem_gb": 22.14}
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+{"step": 129, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.10328283424891221, "tokens": 120000, "cumulative_loss_tokens": 15480000, "grad_norm": 0.462890625, "lr": 3e-05, "finish_rate": 0.974, "comp_len": 387.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 58.7, "frames": {"chat": 310}, "mem_gb": 21.94}
+{"step": 130, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.06663515242016874, "tokens": 120000, "cumulative_loss_tokens": 15600000, "grad_norm": 0.419921875, "lr": 3e-05, "finish_rate": 0.834, "comp_len": 463.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 51.0, "frames": {"chat": 259}, "mem_gb": 22.12}
+[eval step 130] sample: "To find the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. \n\nLet's represent the given matrix:\n\n\\[\n\\begin{bmatrix}\n12 & -16 & 4 & 16 \\"
+{"step": 131, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.06518598229006553, "tokens": 120000, "cumulative_loss_tokens": 15720000, "grad_norm": 0.421875, "lr": 3e-05, "finish_rate": 0.868, "comp_len": 452.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 52.5, "frames": {"chat": 265}, "mem_gb": 22.1}
+{"step": 132, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.1502952246360791, "tokens": 120000, "cumulative_loss_tokens": 15840000, "grad_norm": 0.54296875, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 356.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 63.4, "frames": {"chat": 337}, "mem_gb": 21.64}
+{"step": 133, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.07359331269300698, "tokens": 120000, "cumulative_loss_tokens": 15960000, "grad_norm": 0.419921875, "lr": 3e-05, "finish_rate": 0.972, "comp_len": 413.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 56.7, "frames": {"chat": 290}, "mem_gb": 21.91}
+{"step": 134, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.12302973660406036, "tokens": 120000, "cumulative_loss_tokens": 16080000, "grad_norm": 0.5078125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 343.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 64.6, "frames": {"chat": 349}, "mem_gb": 21.84}
+{"step": 135, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.07360859731266585, "tokens": 120000, "cumulative_loss_tokens": 16200000, "grad_norm": 0.41015625, "lr": 3e-05, "finish_rate": 0.928, "comp_len": 412.4, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 55.8, "frames": {"chat": 291}, "mem_gb": 21.94}
+{"step": 136, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.12916730738196056, "tokens": 120000, "cumulative_loss_tokens": 16320000, "grad_norm": 0.51953125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 350.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 64.3, "frames": {"chat": 342}, "mem_gb": 21.8}
+{"step": 137, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.17012806373077136, "tokens": 120000, "cumulative_loss_tokens": 16440000, "grad_norm": 0.55078125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 355.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 62.1, "frames": {"chat": 338}, "mem_gb": 21.75}
+{"step": 138, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.14694349039436008, "tokens": 120000, "cumulative_loss_tokens": 16560000, "grad_norm": 0.5234375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 337.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 67.7, "frames": {"chat": 356}, "mem_gb": 21.71}
+{"step": 139, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.06565651553365558, "tokens": 120000, "cumulative_loss_tokens": 16680000, "grad_norm": 0.392578125, "lr": 3e-05, "finish_rate": 0.86, "comp_len": 452.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 57.6, "frames": {"chat": 265}, "mem_gb": 22.09}
+{"step": 140, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.11438166577975886, "tokens": 120000, "cumulative_loss_tokens": 16800000, "grad_norm": 0.49609375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 339.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 66.7, "frames": {"chat": 353}, "mem_gb": 21.81}
+[eval step 140] sample: 'To find the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. \n\nGiven the matrix:\n\\[\n\\begin{bmatrix}\n12 & -16 & 4 & 16 \\\\\n-9 & 11 & -1 & '
+{"step": 141, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.11060065792566165, "tokens": 120000, "cumulative_loss_tokens": 16920000, "grad_norm": 0.498046875, "lr": 3e-05, "finish_rate": 0.949, "comp_len": 379.7, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 60.8, "frames": {"chat": 316}, "mem_gb": 21.99}
+{"step": 142, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.09960311313181494, "tokens": 120000, "cumulative_loss_tokens": 17040000, "grad_norm": 0.451171875, "lr": 3e-05, "finish_rate": 0.945, "comp_len": 415.2, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 58.5, "frames": {"chat": 289}, "mem_gb": 22.14}
+{"step": 143, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.09936332012518154, "tokens": 120000, "cumulative_loss_tokens": 17160000, "grad_norm": 0.462890625, "lr": 3e-05, "finish_rate": 0.982, "comp_len": 362.5, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 63.6, "frames": {"chat": 331}, "mem_gb": 21.78}
+{"step": 144, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.09064436243596331, "tokens": 120000, "cumulative_loss_tokens": 17280000, "grad_norm": 0.43359375, "lr": 3e-05, "finish_rate": 0.972, "comp_len": 375.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 59.6, "frames": {"chat": 320}, "mem_gb": 22.1}
+{"step": 145, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.08683979377732612, "tokens": 120000, "cumulative_loss_tokens": 17400000, "grad_norm": 0.376953125, "lr": 3e-05, "finish_rate": 0.941, "comp_len": 441.2, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 51.5, "frames": {"chat": 272}, "mem_gb": 21.83}
+{"step": 146, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.17217374604499588, "tokens": 120000, "cumulative_loss_tokens": 17520000, "grad_norm": 0.5625, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 326.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 68.6, "frames": {"chat": 368}, "mem_gb": 21.75}
+{"step": 147, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.12800495204334147, "tokens": 120000, "cumulative_loss_tokens": 17640000, "grad_norm": 0.474609375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 365.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 66.6, "frames": {"chat": 328}, "mem_gb": 21.78}
+{"step": 148, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.12136686044478168, "tokens": 120000, "cumulative_loss_tokens": 17760000, "grad_norm": 0.5078125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 368.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 65.6, "frames": {"chat": 326}, "mem_gb": 21.99}
+{"step": 149, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.14625438564123275, "tokens": 120000, "cumulative_loss_tokens": 17880000, "grad_norm": 0.49609375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 329.7, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 65.0, "frames": {"chat": 364}, "mem_gb": 21.93}
+{"step": 150, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.0935579854795166, "tokens": 120000, "cumulative_loss_tokens": 18000000, "grad_norm": 0.4140625, "lr": 3e-05, "finish_rate": 0.97, "comp_len": 405.4, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 53.2, "frames": {"chat": 296}, "mem_gb": 21.97}
+[eval step 150] sample: 'To compute the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. \n\nGiven the matrix:\n\\[\n\\begin{bmatrix}\n12 & -16 & 4 & 16 \\\\\n-9 & 11 & -1'
+checkpoint snapshot queued -> outputs/healed/grid_general/glean_keep75_s1224/step0150
+wandb: updating run metadata
+wandb: uploading summary, console lines 171-171
+wandb: 
+wandb: Run history:
+wandb:               comp_len ▄▇▁▃██▇▃▆▆▄▇▅▄▃▄▇▇▄▄▂▆▃▃▆▂▃▃▃▁▄▄▅▃▄▅▅▃▃▃
+wandb: cumulative_loss_tokens ▁▂▂▂▂▂▂▃▃▃▃▃▃▃▄▄▄▄▄▄▅▅▅▅▅▆▆▆▆▆▆▆▇▇▇▇▇███
+wandb:                  epoch ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
+wandb:            finish_rate ▃██▄█▅▄▂▅▃▄▇██▆█▇███▆█▇▇██▇▇▁▆███▇▆█▂███
+wandb:        forward_topk_kl ▅▅▆██▇▃▁█▂▄▇▂▆▄▃▄▅▆▂▆▅▆▅▄▃▅▂▁▁▆▆▅▃▃▇▂▄▄▃
+wandb:              grad_norm ▆▆▆▄▅▂▂▄▄▃█▃▂▂▂▃▂▂▃▂▃▂▂▁▃▂▂▃▂▃▁▃▂▁▂▃▂▂▂▂
+wandb:                     lr ▁███████████████████████████████████████
+wandb:                 mem_gb ██▆▄▃██▆▇▆▆▃█▇▄▁█▄▆▆█▄▄▂▂▃▂▅▄▃██▆▃▃█▁▃▆▅
+wandb:                   step ▁▁▁▁▁▂▂▂▂▂▃▃▃▃▄▄▄▄▄▅▅▅▅▆▆▆▆▆▆▇▇▇▇▇▇▇████
+wandb:               t_data_s ▁▁▁▁█▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
+wandb:                     +3 ...
+wandb: 
+wandb: Run summary:
+wandb:               comp_len 405.4
+wandb: cumulative_loss_tokens 18000000
+wandb:                  epoch 0
+wandb:            finish_rate 0.97
+wandb:        forward_topk_kl 0.09356
+wandb:              grad_norm 0.41406
+wandb:                     lr 3e-05
+wandb:                 mem_gb 21.97
+wandb:                   step 150
+wandb:               t_data_s 0
+wandb:                     +4 ...
+wandb: 
+wandb: 🚀 View run glean_keep75_s1224 at: https://wandb.ai/hbfreed/glean-general-grid/runs/kv8yaoo0
+wandb: ⭐️ View project at: https://wandb.ai/hbfreed/glean-general-grid
+wandb: Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)
+wandb: Find logs at: outputs/healed/grid_general/glean_keep75_s1224/wandb/run-20260718_162400-kv8yaoo0/logs

healed/grid_general/glean_keep75_s1225.console.log

@@ -0,0 +1,215 @@
+/home/henry/Documents/PythonProjects/variable-reap/.venv/lib/python3.12/site-packages/megablocks/grouped_gemm_util.py:10: UserWarning: Grouped GEMM not available.
+  warnings.warn('Grouped GEMM not available.')
+wandb: [wandb.login()] Loaded credentials for https://api.wandb.ai from /home/henry/.netrc.
+wandb: Currently logged in as: hbfreed to https://api.wandb.ai. Use `wandb login --relogin` to force relogin
+wandb: Tracking run with wandb version 0.28.0
+wandb: Run data is saved locally in outputs/healed/grid_general/glean_keep75_s1225/wandb/run-20260718_162630-suscdijy
+wandb: Run `wandb offline` to turn off syncing.
+wandb: Syncing run glean_keep75_s1225
+wandb: ⭐️ View project at https://wandb.ai/hbfreed/glean-general-grid
+wandb: 🚀 View run at https://wandb.ai/hbfreed/glean-general-grid/runs/suscdijy
+
+58360 cached top-128 chat trajectories / 22,295,631 unique tokens | 185 steps/epoch | 150 total steps | student params 5.31B | teacher overlap=False
+{"step": 1, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.22111648944811896, "tokens": 120000, "cumulative_loss_tokens": 120000, "grad_norm": 1.9453125, "lr": 6e-06, "finish_rate": 0.997, "comp_len": 362.5, "t_data_s": 0.1, "t_rollout_s": 0.0, "t_step_s": 70.0, "frames": {"chat": 331}, "mem_gb": 21.8}
+The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.
+[eval step 1] sample: "To solve this problem we need to consider both the monthly savings and the interest earned on the investment account. Let's break it down step-by-step:\n\n1. **Initial Savings:**\n   Jamie starts with $5"
+{"step": 2, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.20739211166581759, "tokens": 120000, "cumulative_loss_tokens": 240000, "grad_norm": 1.765625, "lr": 9e-06, "finish_rate": 0.997, "comp_len": 350.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 63.4, "frames": {"chat": 342}, "mem_gb": 21.72}
+{"step": 3, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.2209934898734403, "tokens": 120000, "cumulative_loss_tokens": 360000, "grad_norm": 1.75, "lr": 1.2e-05, "finish_rate": 0.997, "comp_len": 335.2, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 65.2, "frames": {"chat": 358}, "mem_gb": 21.75}
+{"step": 4, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.1873948453382589, "tokens": 120000, "cumulative_loss_tokens": 480000, "grad_norm": 1.234375, "lr": 1.5e-05, "finish_rate": 0.997, "comp_len": 348.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 67.7, "frames": {"chat": 344}, "mem_gb": 21.84}
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+[eval step 10] sample: 'To determine how many months it will take for Jamie to reach their savings goal of $1,000,000, we need to consider both their monthly savings and the interest earned from their investment account.\n\n1.'
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+[eval step 20] sample: 'To solve this problem, we need to determine how many months it will take for Jamie to reach their savings goal of $1,000,000, given their monthly savings and the interest earned from their investment '
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+[eval step 30] sample: 'To solve this problem, we need to determine how many months it will take Jamie to reach a savings total of $1,000,000, starting from an initial savings of $50,000, with monthly savings of $2,500 and a'
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+[eval step 40] sample: 'To solve this problem, we need to determine how many months it will take for Jamie to reach their savings goal of $1,000,000, given their monthly savings and the interest earned from their investment '
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+[eval step 50] sample: 'To determine how many months it will take Jamie to reach their savings goal of $1,000,000, we need to consider both their monthly savings and the interest earned from their investment account.\n\n**Step'
+checkpoint snapshot queued -> outputs/healed/grid_general/glean_keep75_s1225/step0050
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+[eval step 110] sample: 'To solve this problem, we need to determine how many months it will take for Jamie to reach their savings goal of $1,000,000, given their monthly savings and the interest earned from their investment '
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+[eval step 120] sample: 'To solve this problem, we need to determine how many months it will take for Jamie to reach their savings goal of $1,000,000, given their monthly savings and the interest earned from their investment '
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+[eval step 130] sample: 'To solve this problem, we need to determine how many months it will take for Jamie to reach their savings goal of $1,000,000, given their monthly savings and the interest earned from their investment '
+{"step": 131, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.12712168140066787, "tokens": 120000, "cumulative_loss_tokens": 15720000, "grad_norm": 0.49609375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 342.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 72.5, "frames": {"chat": 350}, "mem_gb": 21.77}
+{"step": 132, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.07436676307577485, "tokens": 120000, "cumulative_loss_tokens": 15840000, "grad_norm": 0.408203125, "lr": 3e-05, "finish_rate": 0.959, "comp_len": 411.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 62.7, "frames": {"chat": 292}, "mem_gb": 21.88}
+{"step": 133, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.13760671476287145, "tokens": 120000, "cumulative_loss_tokens": 15960000, "grad_norm": 0.51953125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 337.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 70.8, "frames": {"chat": 356}, "mem_gb": 21.7}
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+{"step": 135, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.08368868318758128, "tokens": 120000, "cumulative_loss_tokens": 16200000, "grad_norm": 0.421875, "lr": 3e-05, "finish_rate": 0.976, "comp_len": 363.6, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 66.2, "frames": {"chat": 330}, "mem_gb": 22.06}
+{"step": 136, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.12720819443305956, "tokens": 120000, "cumulative_loss_tokens": 16320000, "grad_norm": 0.484375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 330.6, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 74.9, "frames": {"chat": 363}, "mem_gb": 21.91}
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+{"step": 138, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.06660643954688372, "tokens": 120000, "cumulative_loss_tokens": 16560000, "grad_norm": 0.390625, "lr": 3e-05, "finish_rate": 0.93, "comp_len": 444.4, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 54.5, "frames": {"chat": 270}, "mem_gb": 22.04}
+{"step": 139, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.12762515747703923, "tokens": 120000, "cumulative_loss_tokens": 16680000, "grad_norm": 0.484375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 349.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 64.1, "frames": {"chat": 343}, "mem_gb": 21.82}
+{"step": 140, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.030163056720176246, "tokens": 120000, "cumulative_loss_tokens": 16800000, "grad_norm": 0.33984375, "lr": 3e-05, "finish_rate": 0.845, "comp_len": 517.2, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 47.1, "frames": {"chat": 232}, "mem_gb": 22.01}
+[eval step 140] sample: 'To determine how many months it will take Jamie to reach their savings goal of $1,000,000, we need to consider both the monthly savings and the interest earned from the investment account.\n\n**Step 1: '
+{"step": 141, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.12121607892350293, "tokens": 120000, "cumulative_loss_tokens": 16920000, "grad_norm": 0.5078125, "lr": 3e-05, "finish_rate": 0.98, "comp_len": 346.8, "t_data_s": 0.1, "t_rollout_s": 0.0, "t_step_s": 64.8, "frames": {"chat": 346}, "mem_gb": 21.95}
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+{"step": 143, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.05772588231681536, "tokens": 120000, "cumulative_loss_tokens": 17160000, "grad_norm": 0.357421875, "lr": 3e-05, "finish_rate": 0.923, "comp_len": 439.6, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 52.0, "frames": {"chat": 273}, "mem_gb": 22.08}
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+{"step": 145, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.10201068616132251, "tokens": 120000, "cumulative_loss_tokens": 17400000, "grad_norm": 0.458984375, "lr": 3e-05, "finish_rate": 0.936, "comp_len": 401.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 56.8, "frames": {"chat": 299}, "mem_gb": 22.11}
+{"step": 146, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.06552851478964246, "tokens": 120000, "cumulative_loss_tokens": 17520000, "grad_norm": 0.3828125, "lr": 3e-05, "finish_rate": 0.88, "comp_len": 463.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 53.7, "frames": {"chat": 259}, "mem_gb": 22.19}
+{"step": 147, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.08131732919542119, "tokens": 120000, "cumulative_loss_tokens": 17640000, "grad_norm": 0.400390625, "lr": 3e-05, "finish_rate": 0.966, "comp_len": 369.2, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 58.8, "frames": {"chat": 325}, "mem_gb": 21.92}
+{"step": 148, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.07450551387115847, "tokens": 120000, "cumulative_loss_tokens": 17760000, "grad_norm": 0.400390625, "lr": 3e-05, "finish_rate": 0.923, "comp_len": 442.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 52.6, "frames": {"chat": 271}, "mem_gb": 22.0}
+{"step": 149, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.1205579935764273, "tokens": 120000, "cumulative_loss_tokens": 17880000, "grad_norm": 0.484375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 334.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 63.5, "frames": {"chat": 359}, "mem_gb": 21.79}
+{"step": 150, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.11683979006973096, "tokens": 120000, "cumulative_loss_tokens": 18000000, "grad_norm": 0.4765625, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 347.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 68.6, "frames": {"chat": 345}, "mem_gb": 21.85}
+[eval step 150] sample: 'To solve this problem, we need to determine how many months it will take for Jamie to reach their savings goal of $1,000,000, given their monthly savings and the interest earned from their investment '
+checkpoint snapshot queued -> outputs/healed/grid_general/glean_keep75_s1225/step0150
+wandb: updating run metadata
+wandb: uploading config.yaml
+wandb: 
+wandb: Run history:
+wandb:               comp_len ▂▆▂▃▆▄▂▄▅▃▁▃▄▄▁▃▅▆▂▄▄▂▅▂▄▄▅▃█▆▁▃▆▂▇▃▂▆▆▂
+wandb: cumulative_loss_tokens ▁▁▁▁▁▂▂▂▂▂▃▃▃▃▃▃▃▃▃▄▄▄▅▅▅▆▆▆▆▆▆▆▇▇▇▇▇▇██
+wandb:                  epoch ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
+wandb:            finish_rate ██▇█▇█▆▇██▁███▅▇▄█▆██▄▂█▅████▅▂▄█▃██▇█▇█
+wandb:        forward_topk_kl █▇▆▃▄▇▂▆▅▂▆▅▆▂▂▅▂▅▅▄▄▂▅▄▂▅▂▃▅▂▅▂▂▅▁▂▄▃▁▄
+wandb:              grad_norm ▄▃▂▂▂▂▂▂▁▂▂▂▁▂▂▁▃▂▁▁▁█▁▁▁▁▁▁▂▂▁▁▂▁▁▁▁▁▁▁
+wandb:                     lr ▁███████████████████████████████████████
+wandb:                 mem_gb ▂▃▆▅▇▆▇▄▄▄▆▄▁▂▆▂▃▃▁▄▅▇▅▄▇▇▂█▅▇▆▄▆▃▇▃▂▇▇▃
+wandb:                   step ▁▁▁▁▁▂▂▂▂▂▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇▇███
+wandb:               t_data_s ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
+wandb:                     +3 ...
+wandb: 
+wandb: Run summary:
+wandb:               comp_len 347.8
+wandb: cumulative_loss_tokens 18000000
+wandb:                  epoch 0
+wandb:            finish_rate 0.997
+wandb:        forward_topk_kl 0.11684
+wandb:              grad_norm 0.47656
+wandb:                     lr 3e-05
+wandb:                 mem_gb 21.85
+wandb:                   step 150
+wandb:               t_data_s 0
+wandb:                     +4 ...
+wandb: 
+wandb: 🚀 View run glean_keep75_s1225 at: https://wandb.ai/hbfreed/glean-general-grid/runs/suscdijy
+wandb: ⭐️ View project at: https://wandb.ai/hbfreed/glean-general-grid
+wandb: Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)
+wandb: Find logs at: outputs/healed/grid_general/glean_keep75_s1225/wandb/run-20260718_162630-suscdijy/logs

healed/grid_general/glean_keep75_s1226.console.log

@@ -0,0 +1,215 @@
+/home/henry/Documents/PythonProjects/variable-reap/.venv/lib/python3.12/site-packages/megablocks/grouped_gemm_util.py:10: UserWarning: Grouped GEMM not available.
+  warnings.warn('Grouped GEMM not available.')
+wandb: [wandb.login()] Loaded credentials for https://api.wandb.ai from /home/henry/.netrc.
+wandb: Currently logged in as: hbfreed to https://api.wandb.ai. Use `wandb login --relogin` to force relogin
+wandb: Tracking run with wandb version 0.28.0
+wandb: Run data is saved locally in outputs/healed/grid_general/glean_keep75_s1226/wandb/run-20260718_192317-q973uip1
+wandb: Run `wandb offline` to turn off syncing.
+wandb: Syncing run glean_keep75_s1226
+wandb: ⭐️ View project at https://wandb.ai/hbfreed/glean-general-grid
+wandb: 🚀 View run at https://wandb.ai/hbfreed/glean-general-grid/runs/q973uip1
+
+58360 cached top-128 chat trajectories / 22,295,631 unique tokens | 185 steps/epoch | 150 total steps | student params 5.31B | teacher overlap=False
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+The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.
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+checkpoint snapshot queued -> outputs/healed/grid_general/glean_keep75_s1226/step0050
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+[eval step 70] sample: "to solve the problem, let's start by expressing the terms of the arithmetic sequence in terms of the first term \\(a_1\\) and the common difference \\(d\\).\n\nin an arithmetic sequence, the \\(n\\)-th term i"
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+[eval step 80] sample: "to solve the problem, let's start by expressing the terms of the arithmetic sequence in terms of the first term \\(a_1\\) and the common difference \\(d\\).\n\nin an arithmetic sequence, the \\(n\\)-th term i"
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+[eval step 90] sample: "to solve the problem, let's start by expressing the terms of the arithmetic sequence in terms of the first term \\(a_1\\) and the common difference \\(d\\).\n\nin an arithmetic sequence, the \\(n\\)-th term i"
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+[eval step 100] sample: "to solve the problem, let's start by expressing the terms of the arithmetic sequence in terms of the first term \\(a_1\\) and the common difference \\(d\\).\n\nin an arithmetic sequence, the \\(n\\)-th term i"
+checkpoint snapshot queued -> outputs/healed/grid_general/glean_keep75_s1226/step0100
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+[eval step 120] sample: "to solve the problem, let's start by expressing the terms of the arithmetic sequence in terms of the first term \\(a_1\\) and the common difference \\(d\\).\n\nin an arithmetic sequence, the \\(n\\)-th term i"
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+[eval step 130] sample: "to solve the problem, let's start by expressing the terms of the arithmetic sequence in terms of the first term \\(a_1\\) and the common difference \\(d\\).\n\nin an arithmetic sequence, the \\(n\\)-th term i"
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+[eval step 140] sample: "to solve the problem, let's start by expressing the terms of the arithmetic sequence in terms of the first term \\(a_1\\) and the common difference \\(d\\).\n\nin an arithmetic sequence, the \\(n\\)-th term i"
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+[eval step 150] sample: "to solve the problem, let's start by expressing the terms of the arithmetic sequence in terms of the first term \\(a_1\\) and the common difference \\(d\\).\n\nin an arithmetic sequence, the \\(n\\)-th term i"
+checkpoint snapshot queued -> outputs/healed/grid_general/glean_keep75_s1226/step0150
+wandb: updating run metadata
+wandb: uploading summary, console lines 171-171
+wandb: 
+wandb: Run history:
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+wandb: cumulative_loss_tokens ▁▁▁▂▂▂▂▂▂▃▃▃▃▃▄▄▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▆▇▇▇▇███
+wandb:                  epoch ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
+wandb:            finish_rate ▇▁▆█▆▄███▇██▇█▆▆▆▅▆▇██▃▇██▇█▆▇▇▅█▇██▇▄▅▆
+wandb:        forward_topk_kl ▆█▇▇▆▇▃▄▅▄▅▃▆▃▄▇▆▆▆▅▄▅▄▆▅▆▁▁▅▅▅▄▃▅▂▂▃▃▆▂
+wandb:              grad_norm █▇▅▃▃▃▂▂▃▂▁▂▂▁▂▂▂▂▁▂▂▂▁▁▂▂▂▂▂▁▂▂▂▂▂▁▁▁▁▂
+wandb:                     lr ▁▃▅▇████████████████████████████████████
+wandb:                 mem_gb ▇▁▁▃▃▇▆▅▆▄▅▆▇▇▅▇▇▃▇▆▆█▆▂▇▂▅▃▃█▄▂▆▂▆█▆█▁▇
+wandb:                   step ▁▁▁▁▁▂▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇█
+wandb:               t_data_s ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
+wandb:                     +3 ...
+wandb: 
+wandb: Run summary:
+wandb:               comp_len 409.6
+wandb: cumulative_loss_tokens 18000000
+wandb:                  epoch 0
+wandb:            finish_rate 0.942
+wandb:        forward_topk_kl 0.07231
+wandb:              grad_norm 0.40234
+wandb:                     lr 3e-05
+wandb:                 mem_gb 22.06
+wandb:                   step 150
+wandb:               t_data_s 0
+wandb:                     +4 ...
+wandb: 
+wandb: 🚀 View run glean_keep75_s1226 at: https://wandb.ai/hbfreed/glean-general-grid/runs/q973uip1
+wandb: ⭐️ View project at: https://wandb.ai/hbfreed/glean-general-grid
+wandb: Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)
+wandb: Find logs at: outputs/healed/grid_general/glean_keep75_s1226/wandb/run-20260718_192317-q973uip1/logs

healed/grid_general/reap_keep25_s1224.console.log

@@ -0,0 +1,213 @@
+/home/henry/Documents/PythonProjects/variable-reap/.venv/lib/python3.12/site-packages/megablocks/grouped_gemm_util.py:10: UserWarning: Grouped GEMM not available.
+  warnings.warn('Grouped GEMM not available.')
+wandb: [wandb.login()] Loaded credentials for https://api.wandb.ai from /home/henry/.netrc.
+wandb: Currently logged in as: hbfreed to https://api.wandb.ai. Use `wandb login --relogin` to force relogin
+wandb: setting up run ydhbbk1z
+wandb: Tracking run with wandb version 0.28.0
+wandb: Run data is saved locally in outputs/healed/grid_general/reap_keep25_s1224/wandb/run-20260717_150109-ydhbbk1z
+wandb: Run `wandb offline` to turn off syncing.
+wandb: Syncing run reap_keep25_s1224
+wandb: ⭐️ View project at https://wandb.ai/hbfreed/glean-general-grid
+wandb: 🚀 View run at https://wandb.ai/hbfreed/glean-general-grid/runs/ydhbbk1z
+58360 cached top-128 chat trajectories / 22,295,631 unique tokens | 185 steps/epoch | 150 total steps | student params 2.09B | teacher overlap=False
+{"step": 1, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 6.714513613096873, "tokens": 120000, "cumulative_loss_tokens": 120000, "grad_norm": 90.5, "lr": 6e-06, "finish_rate": 0.869, "comp_len": 463.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 53.5, "frames": {"chat": 259}, "mem_gb": 9.93}
+The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.
+[eval step 1] sample: 'POR:5GYUI Oisse\nlt g intro- N1(numbersialudild)\n\nnou natorls:1[n-in-,orler acday.#-=1 \n"""\n"""Mnel=>and"130-lc4-'
+{"step": 2, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 7.052694697197278, "tokens": 120000, "cumulative_loss_tokens": 240000, "grad_norm": 71.5, "lr": 9e-06, "finish_rate": 0.997, "comp_len": 344.8, "t_data_s": 0.1, "t_rollout_s": 0.0, "t_step_s": 58.0, "frames": {"chat": 348}, "mem_gb": 9.59}
+{"step": 3, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 6.581489293889205, "tokens": 120000, "cumulative_loss_tokens": 360000, "grad_norm": 66.0, "lr": 1.2e-05, "finish_rate": 0.997, "comp_len": 324.3, "t_data_s": 0.1, "t_rollout_s": 0.0, "t_step_s": 60.6, "frames": {"chat": 370}, "mem_gb": 9.74}
+{"step": 4, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 4.8654761122624075, "tokens": 120000, "cumulative_loss_tokens": 480000, "grad_norm": 57.0, "lr": 1.5e-05, "finish_rate": 0.852, "comp_len": 491.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 44.3, "frames": {"chat": 244}, "mem_gb": 9.97}
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+{"step": 6, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 4.5225257198294, "tokens": 120000, "cumulative_loss_tokens": 720000, "grad_norm": 37.0, "lr": 2.1e-05, "finish_rate": 0.997, "comp_len": 317.5, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 61.4, "frames": {"chat": 378}, "mem_gb": 9.49}
+{"step": 7, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 4.168571872454882, "tokens": 120000, "cumulative_loss_tokens": 840000, "grad_norm": 44.25, "lr": 2.4e-05, "finish_rate": 0.997, "comp_len": 312.5, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 64.8, "frames": {"chat": 384}, "mem_gb": 9.98}
+{"step": 8, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 3.3614308606823284, "tokens": 120000, "cumulative_loss_tokens": 960000, "grad_norm": 35.75, "lr": 2.7000000000000002e-05, "finish_rate": 0.997, "comp_len": 370.4, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 60.9, "frames": {"chat": 324}, "mem_gb": 9.83}
+{"step": 9, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.9847333917876084, "tokens": 120000, "cumulative_loss_tokens": 1080000, "grad_norm": 23.75, "lr": 3e-05, "finish_rate": 0.891, "comp_len": 502.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 50.3, "frames": {"chat": 239}, "mem_gb": 10.0}
+{"step": 10, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 2.441437253133456, "tokens": 120000, "cumulative_loss_tokens": 1200000, "grad_norm": 16.625, "lr": 3e-05, "finish_rate": 0.961, "comp_len": 392.2, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 57.4, "frames": {"chat": 306}, "mem_gb": 9.74}
+[eval step 10] sample: 'Compute the rank of the given 4x4 matrix:\n[[12, -16, 4, 16],\n[--9, 11, -1, -10],\n[-0, 1, -2, -10],\n[-0, 4, -8, -8]}\n\nCompute'
+{"step": 11, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 2.360720735937357, "tokens": 120000, "cumulative_loss_tokens": 1320000, "grad_norm": 13.6875, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 364.7, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 61.7, "frames": {"chat": 329}, "mem_gb": 9.77}
+{"step": 12, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.3320580815384786, "tokens": 120000, "cumulative_loss_tokens": 1440000, "grad_norm": 7.03125, "lr": 3e-05, "finish_rate": 0.882, "comp_len": 472.4, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 50.5, "frames": {"chat": 254}, "mem_gb": 10.0}
+{"step": 13, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.7477209083815415, "tokens": 120000, "cumulative_loss_tokens": 1560000, "grad_norm": 6.5625, "lr": 3e-05, "finish_rate": 0.974, "comp_len": 439.6, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 56.1, "frames": {"chat": 273}, "mem_gb": 9.98}
+{"step": 14, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.905339217838645, "tokens": 120000, "cumulative_loss_tokens": 1680000, "grad_norm": 4.40625, "lr": 3e-05, "finish_rate": 0.873, "comp_len": 508.5, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 47.3, "frames": {"chat": 236}, "mem_gb": 9.9}
+{"step": 15, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.894308428629736, "tokens": 120000, "cumulative_loss_tokens": 1800000, "grad_norm": 4.34375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 315.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 67.4, "frames": {"chat": 381}, "mem_gb": 9.63}
+{"step": 16, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.8942556741168102, "tokens": 120000, "cumulative_loss_tokens": 1920000, "grad_norm": 2.53125, "lr": 3e-05, "finish_rate": 0.925, "comp_len": 451.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 50.6, "frames": {"chat": 266}, "mem_gb": 9.83}
+{"step": 17, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.2078631315603852, "tokens": 120000, "cumulative_loss_tokens": 2040000, "grad_norm": 2.671875, "lr": 3e-05, "finish_rate": 0.961, "comp_len": 389.6, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 59.4, "frames": {"chat": 308}, "mem_gb": 9.82}
+{"step": 18, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.9374638251105945, "tokens": 120000, "cumulative_loss_tokens": 2160000, "grad_norm": 1.9375, "lr": 3e-05, "finish_rate": 0.853, "comp_len": 439.6, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 52.9, "frames": {"chat": 273}, "mem_gb": 10.0}
+{"step": 19, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.447662654795746, "tokens": 120000, "cumulative_loss_tokens": 2280000, "grad_norm": 2.5, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 326.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 65.0, "frames": {"chat": 368}, "mem_gb": 9.63}
+{"step": 20, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.6041733922436834, "tokens": 120000, "cumulative_loss_tokens": 2400000, "grad_norm": 2.46875, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 354.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 64.4, "frames": {"chat": 339}, "mem_gb": 9.87}
+[eval step 20] sample: 'To solve this problem, we need to determine the rank of the given 4x4 matrix. The rank of a matrix is the maximum number of columns in the matrix.\n\nGiven matrix:\n\\[\n\\begin{bmatrix}\n12 & -16 & 4 & 16 \\'
+{"step": 21, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.9353083563171327, "tokens": 120000, "cumulative_loss_tokens": 2520000, "grad_norm": 1.78125, "lr": 3e-05, "finish_rate": 0.963, "comp_len": 402.7, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 57.6, "frames": {"chat": 298}, "mem_gb": 9.8}
+{"step": 22, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.3508124507144093, "tokens": 120000, "cumulative_loss_tokens": 2640000, "grad_norm": 2.640625, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 342.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 65.4, "frames": {"chat": 350}, "mem_gb": 9.52}
+{"step": 23, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.7905151057086885, "tokens": 120000, "cumulative_loss_tokens": 2760000, "grad_norm": 1.421875, "lr": 3e-05, "finish_rate": 0.858, "comp_len": 487.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 53.7, "frames": {"chat": 246}, "mem_gb": 9.97}
+{"step": 24, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.2881518234315017, "tokens": 120000, "cumulative_loss_tokens": 2880000, "grad_norm": 1.5859375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 320.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 67.5, "frames": {"chat": 375}, "mem_gb": 9.7}
+{"step": 25, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.305539540750285, "tokens": 120000, "cumulative_loss_tokens": 3000000, "grad_norm": 1.390625, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 340.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 66.4, "frames": {"chat": 352}, "mem_gb": 9.75}
+{"step": 26, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.27691397592475, "tokens": 120000, "cumulative_loss_tokens": 3120000, "grad_norm": 1.390625, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 303.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 71.0, "frames": {"chat": 396}, "mem_gb": 9.71}
+{"step": 27, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.0702742205070952, "tokens": 120000, "cumulative_loss_tokens": 3240000, "grad_norm": 1.3203125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 331.5, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 66.8, "frames": {"chat": 362}, "mem_gb": 9.56}
+{"step": 28, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.3215352043059965, "tokens": 120000, "cumulative_loss_tokens": 3360000, "grad_norm": 1.3828125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 325.2, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 69.2, "frames": {"chat": 369}, "mem_gb": 9.77}
+{"step": 29, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.310362931372722, "tokens": 120000, "cumulative_loss_tokens": 3480000, "grad_norm": 1.359375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 300.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 70.4, "frames": {"chat": 399}, "mem_gb": 9.62}
+{"step": 30, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.6964659744882956, "tokens": 120000, "cumulative_loss_tokens": 3600000, "grad_norm": 1.3984375, "lr": 3e-05, "finish_rate": 0.911, "comp_len": 485.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 52.5, "frames": {"chat": 247}, "mem_gb": 9.89}
+[eval step 30] sample: "To solve this problem, we need to find the rank of the given 4x4 matrix. The rank of a matrix is the maximum number of linearly independent columns in the matrix.\n\nLet's break down the matrix step-by-"
+{"step": 31, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.3749437816654642, "tokens": 120000, "cumulative_loss_tokens": 3720000, "grad_norm": 1.1796875, "lr": 3e-05, "finish_rate": 0.891, "comp_len": 521.7, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 45.9, "frames": {"chat": 230}, "mem_gb": 9.83}
+{"step": 32, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.6364695584422598, "tokens": 120000, "cumulative_loss_tokens": 3840000, "grad_norm": 1.2421875, "lr": 3e-05, "finish_rate": 0.905, "comp_len": 456.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 58.9, "frames": {"chat": 263}, "mem_gb": 10.0}
+{"step": 33, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.6319580947947999, "tokens": 120000, "cumulative_loss_tokens": 3960000, "grad_norm": 1.0859375, "lr": 3e-05, "finish_rate": 0.848, "comp_len": 454.5, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 54.5, "frames": {"chat": 264}, "mem_gb": 9.99}
+{"step": 34, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.0467302889599155, "tokens": 120000, "cumulative_loss_tokens": 4080000, "grad_norm": 1.359375, "lr": 3e-05, "finish_rate": 0.991, "comp_len": 365.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 68.0, "frames": {"chat": 328}, "mem_gb": 9.85}
+{"step": 35, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.5760055315182855, "tokens": 120000, "cumulative_loss_tokens": 4200000, "grad_norm": 1.078125, "lr": 3e-05, "finish_rate": 0.925, "comp_len": 427.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 57.7, "frames": {"chat": 281}, "mem_gb": 9.92}
+{"step": 36, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.4677317438756426, "tokens": 120000, "cumulative_loss_tokens": 4320000, "grad_norm": 1.0, "lr": 3e-05, "finish_rate": 0.861, "comp_len": 506.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 50.3, "frames": {"chat": 237}, "mem_gb": 9.91}
+{"step": 37, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.5605393174094458, "tokens": 120000, "cumulative_loss_tokens": 4440000, "grad_norm": 0.83203125, "lr": 3e-05, "finish_rate": 0.91, "comp_len": 431.7, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 53.2, "frames": {"chat": 278}, "mem_gb": 10.0}
+{"step": 38, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.0259025651920586, "tokens": 120000, "cumulative_loss_tokens": 4560000, "grad_norm": 1.4765625, "lr": 3e-05, "finish_rate": 0.955, "comp_len": 387.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 59.6, "frames": {"chat": 310}, "mem_gb": 9.87}
+{"step": 39, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.6856813535400667, "tokens": 120000, "cumulative_loss_tokens": 4680000, "grad_norm": 0.87890625, "lr": 3e-05, "finish_rate": 0.959, "comp_len": 406.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 55.9, "frames": {"chat": 295}, "mem_gb": 9.81}
+{"step": 40, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.7459171565882862, "tokens": 120000, "cumulative_loss_tokens": 4800000, "grad_norm": 0.953125, "lr": 3e-05, "finish_rate": 0.957, "comp_len": 364.7, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 58.8, "frames": {"chat": 329}, "mem_gb": 9.82}
+[eval step 40] sample: 'To find the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix.\n\nGiven the matrix:\n\n\\[\nA = \\begin{bmatrix}\n12 & -16 & 4 & 16 \\\\\n-9 & 11 & -'
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+[eval step 50] sample: 'To determine the rank of a matrix, we need to understand that the rank of a matrix is the maximum number of linearly independent rows or columns. \n\nGiven the matrix:\n\n\\[\n\\begin{bmatrix}\n12 & -16 & 4 &'
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+[eval step 90] sample: "To compute the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. Here's how we can do it step-by-step using Python and the sympy library:"
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+{"step": 145, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.5657251183000703, "tokens": 120000, "cumulative_loss_tokens": 17400000, "grad_norm": 0.62109375, "lr": 3e-05, "finish_rate": 0.941, "comp_len": 441.2, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 47.5, "frames": {"chat": 272}, "mem_gb": 9.73}
+{"step": 146, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.9686935982390618, "tokens": 120000, "cumulative_loss_tokens": 17520000, "grad_norm": 0.8125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 326.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 62.9, "frames": {"chat": 368}, "mem_gb": 9.65}
+{"step": 147, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.7965175971987347, "tokens": 120000, "cumulative_loss_tokens": 17640000, "grad_norm": 0.7109375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 365.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 57.9, "frames": {"chat": 328}, "mem_gb": 9.68}
+{"step": 148, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.7893025688592344, "tokens": 120000, "cumulative_loss_tokens": 17760000, "grad_norm": 0.71484375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 368.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 57.0, "frames": {"chat": 326}, "mem_gb": 9.9}
+{"step": 149, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.8914329555117836, "tokens": 120000, "cumulative_loss_tokens": 17880000, "grad_norm": 0.76953125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 329.7, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 61.8, "frames": {"chat": 364}, "mem_gb": 9.83}
+{"step": 150, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.5755241934151699, "tokens": 120000, "cumulative_loss_tokens": 18000000, "grad_norm": 0.609375, "lr": 3e-05, "finish_rate": 0.97, "comp_len": 405.4, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 50.3, "frames": {"chat": 296}, "mem_gb": 9.88}
+[eval step 150] sample: "To compute the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. Here's how we can approach this problem step-by-step using Python and th"
+checkpoint snapshot queued -> outputs/healed/grid_general/reap_keep25_s1224/step0150
+wandb: updating run metadata
+wandb: uploading summary, console lines 167-168; uploading output.log; uploading wandb-summary.json; uploading config.yaml
+wandb: 
+wandb: Run history:
+wandb:               comp_len ▅▂▆▂▆▃▂▁▆▅▄▃▃▅▃▄▁▅▅▃▁▂▂▂▄▄▂▃▁█▇▂▃▄▂▂▁▂▄▃
+wandb: cumulative_loss_tokens ▁▁▁▁▁▂▂▂▂▂▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▅▅▆▆▆▇▇▇▇▇▇███
+wandb:                  epoch ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
+wandb:            finish_rate ▆██▄██▂██▂▆███▃▇▄███▅▇▇██▇█▁▇▆▂▇████▆▃██
+wandb:        forward_topk_kl ██▇▆▂▃▂▂▂▂▂▂▂▂▂▁▂▂▂▂▂▂▂▂▂▁▁▂▁▂▂▂▂▂▂▂▁▂▂▂
+wandb:              grad_norm █▇▅▃▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
+wandb:                     lr ▁▂▅▆████████████████████████████████████
+wandb:                 mem_gb ▂▇▇▆▇▄▃▁▅▄▆▄▆▅▅▇▆▄▅▂▃█▄▅▃▇▇▇▆▄▃▃▆▁▄▂▆█▂▅
+wandb:                   step ▁▁▁▂▂▂▂▂▂▂▃▃▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▇▇█████
+wandb:               t_data_s █▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
+wandb:                     +3 ...
+wandb: 
+wandb: Run summary:
+wandb:               comp_len 405.4
+wandb: cumulative_loss_tokens 18000000
+wandb:                  epoch 0
+wandb:            finish_rate 0.97
+wandb:        forward_topk_kl 0.57552
+wandb:              grad_norm 0.60938
+wandb:                     lr 3e-05
+wandb:                 mem_gb 9.88
+wandb:                   step 150
+wandb:               t_data_s 0
+wandb:                     +4 ...
+wandb: 
+wandb: 🚀 View run reap_keep25_s1224 at: https://wandb.ai/hbfreed/glean-general-grid/runs/ydhbbk1z
+wandb: ⭐️ View project at: https://wandb.ai/hbfreed/glean-general-grid
+wandb: Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)
+wandb: Find logs at: outputs/healed/grid_general/reap_keep25_s1224/wandb/run-20260717_150109-ydhbbk1z/logs

healed/grid_general/reap_keep25_s1225.console.log

@@ -0,0 +1,213 @@
+/home/henry/Documents/PythonProjects/variable-reap/.venv/lib/python3.12/site-packages/megablocks/grouped_gemm_util.py:10: UserWarning: Grouped GEMM not available.
+  warnings.warn('Grouped GEMM not available.')
+wandb: [wandb.login()] Loaded credentials for https://api.wandb.ai from /home/henry/.netrc.
+wandb: Currently logged in as: hbfreed to https://api.wandb.ai. Use `wandb login --relogin` to force relogin
+wandb: Tracking run with wandb version 0.28.0
+wandb: Run data is saved locally in outputs/healed/grid_general/reap_keep25_s1225/wandb/run-20260717_145438-hv788gr3
+wandb: Run `wandb offline` to turn off syncing.
+wandb: Syncing run reap_keep25_s1225
+wandb: ⭐️ View project at https://wandb.ai/hbfreed/glean-general-grid
+wandb: 🚀 View run at https://wandb.ai/hbfreed/glean-general-grid/runs/hv788gr3
+58360 cached top-128 chat trajectories / 22,295,631 unique tokens | 185 steps/epoch | 150 total steps | student params 2.09B | teacher overlap=False
+{"step": 1, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 7.128950115287304, "tokens": 120000, "cumulative_loss_tokens": 120000, "grad_norm": 74.0, "lr": 6e-06, "finish_rate": 0.997, "comp_len": 362.5, "t_data_s": 0.1, "t_rollout_s": 0.0, "t_step_s": 64.4, "frames": {"chat": 331}, "mem_gb": 9.8}
+The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.
+[eval step 1] sample: '$0 Cisory1 are not to be$ inovy_$nos_frP-loadingD = "ny\'\'els|[m_e_O(aude_of$Thurs.dips]{\n    in_tw. Common\n    posaki.po\'\'%$_'
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+{"step": 9, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 3.1154985622068248, "tokens": 120000, "cumulative_loss_tokens": 1080000, "grad_norm": 19.125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 339.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 62.7, "frames": {"chat": 354}, "mem_gb": 9.84}
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+[eval step 10] sample: '```\n```\n```\n```\n```\n```\n```\n```\n```\n```\n```\n```\n```\n```\n```\n```\n```\n```\n```\n```\n```\n```\n```\n```\n```\n```\n```\n```\n```\n```\n```\n```\n'
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+{"step": 20, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.8951119984914859, "tokens": 120000, "cumulative_loss_tokens": 2400000, "grad_norm": 1.8125, "lr": 3e-05, "finish_rate": 0.939, "comp_len": 408.2, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 53.2, "frames": {"chat": 294}, "mem_gb": 9.99}
+[eval step 20] sample: 'To solve this problem, we need to determine the number of months it takes for the savings to achieve the goal of $1,000,000$ given that the savings are at an average interest of $300 per month$.\n\nHere'
+{"step": 21, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.3273236130041381, "tokens": 120000, "cumulative_loss_tokens": 2520000, "grad_norm": 2.6875, "lr": 3e-05, "finish_rate": 0.98, "comp_len": 401.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 58.3, "frames": {"chat": 299}, "mem_gb": 10.0}
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+{"step": 28, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.165311704517901, "tokens": 120000, "cumulative_loss_tokens": 3360000, "grad_norm": 1.828125, "lr": 3e-05, "finish_rate": 0.967, "comp_len": 390.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 57.2, "frames": {"chat": 307}, "mem_gb": 9.98}
+{"step": 29, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.8248838950661321, "tokens": 120000, "cumulative_loss_tokens": 3480000, "grad_norm": 1.2265625, "lr": 3e-05, "finish_rate": 0.926, "comp_len": 425.5, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 51.9, "frames": {"chat": 282}, "mem_gb": 9.85}
+{"step": 30, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.1389709412602087, "tokens": 120000, "cumulative_loss_tokens": 3600000, "grad_norm": 1.3203125, "lr": 3e-05, "finish_rate": 0.979, "comp_len": 356.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 59.6, "frames": {"chat": 337}, "mem_gb": 9.88}
+[eval step 30] sample: "To solve this problem, we need to calculate the total savings after a month when the savings are $2,500 and the interest is $300.\n\nLet's break down the steps:\n\n1. Calculate the monthly savings:\n   \\[\n"
+{"step": 31, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.7580995838927725, "tokens": 120000, "cumulative_loss_tokens": 3720000, "grad_norm": 1.1796875, "lr": 3e-05, "finish_rate": 0.891, "comp_len": 436.4, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 52.9, "frames": {"chat": 275}, "mem_gb": 9.94}
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+[eval step 40] sample: 'To solve this problem, we need to determine the number of months Jamie will need to save to reach their goal of $1,000,000 from their current savings of $50,000, given their monthly salary of $2,500.\n'
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+[eval step 50] sample: 'To determine how many months it will take for Jamie to reach their goal of $1,000,000, we need to account for both the savings and the investment account.\n\n**Step 1: Calculate the total savings needed'
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+[eval step 60] sample: 'To solve this problem, we need to determine how many months Jamie will spend to reach their financial goal of $1,000,000.\n\n1. **Calculate the monthly savings:**\n   Jamie saves $2,500 each month.\n\n2. *'
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+[eval step 80] sample: 'To solve this problem, we need to determine how many months Jamie will need to save to reach their goal of $1,000,000.\n\n**Step 1: Calculate the total savings needed.**\n\nJamie wants to save a total of '
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+{"step": 136, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.85121752653718, "tokens": 120000, "cumulative_loss_tokens": 16320000, "grad_norm": 0.73828125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 330.6, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 65.8, "frames": {"chat": 363}, "mem_gb": 9.82}
+{"step": 137, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.7743317221516123, "tokens": 120000, "cumulative_loss_tokens": 16440000, "grad_norm": 0.74609375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 339.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 65.0, "frames": {"chat": 354}, "mem_gb": 9.68}
+{"step": 138, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.44354114747531714, "tokens": 120000, "cumulative_loss_tokens": 16560000, "grad_norm": 0.578125, "lr": 3e-05, "finish_rate": 0.93, "comp_len": 444.4, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 51.6, "frames": {"chat": 270}, "mem_gb": 9.94}
+{"step": 139, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.7892652089830487, "tokens": 120000, "cumulative_loss_tokens": 16680000, "grad_norm": 0.70703125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 349.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 60.2, "frames": {"chat": 343}, "mem_gb": 9.72}
+{"step": 140, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.23516690385279557, "tokens": 120000, "cumulative_loss_tokens": 16800000, "grad_norm": 0.490234375, "lr": 3e-05, "finish_rate": 0.845, "comp_len": 517.2, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 44.4, "frames": {"chat": 232}, "mem_gb": 9.91}
+[eval step 140] sample: 'To solve this problem, we need to determine how many months Jamie will need to save to reach their goal of $1,000,000, given their savings plan.\n\n**Step 1: Calculate the monthly savings from the salar'
+{"step": 141, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.7412611307131748, "tokens": 120000, "cumulative_loss_tokens": 16920000, "grad_norm": 0.69921875, "lr": 3e-05, "finish_rate": 0.98, "comp_len": 346.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 61.1, "frames": {"chat": 346}, "mem_gb": 9.86}
+{"step": 142, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.5961444109828522, "tokens": 120000, "cumulative_loss_tokens": 17040000, "grad_norm": 0.6171875, "lr": 3e-05, "finish_rate": 0.951, "comp_len": 392.2, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 53.9, "frames": {"chat": 306}, "mem_gb": 9.94}
+{"step": 143, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.41770563813019546, "tokens": 120000, "cumulative_loss_tokens": 17160000, "grad_norm": 0.546875, "lr": 3e-05, "finish_rate": 0.923, "comp_len": 439.6, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 48.7, "frames": {"chat": 273}, "mem_gb": 9.99}
+{"step": 144, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.45850400070821246, "tokens": 120000, "cumulative_loss_tokens": 17280000, "grad_norm": 0.62109375, "lr": 3e-05, "finish_rate": 0.959, "comp_len": 451.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 47.9, "frames": {"chat": 266}, "mem_gb": 9.81}
+{"step": 145, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.6739403530483444, "tokens": 120000, "cumulative_loss_tokens": 17400000, "grad_norm": 0.6640625, "lr": 3e-05, "finish_rate": 0.936, "comp_len": 401.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 53.4, "frames": {"chat": 299}, "mem_gb": 10.01}
+{"step": 146, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.48320755306941765, "tokens": 120000, "cumulative_loss_tokens": 17520000, "grad_norm": 0.62109375, "lr": 3e-05, "finish_rate": 0.88, "comp_len": 463.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 50.8, "frames": {"chat": 259}, "mem_gb": 10.09}
+{"step": 147, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.5405310631979257, "tokens": 120000, "cumulative_loss_tokens": 17640000, "grad_norm": 0.6796875, "lr": 3e-05, "finish_rate": 0.966, "comp_len": 369.2, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 54.7, "frames": {"chat": 325}, "mem_gb": 9.83}
+{"step": 148, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.48942058433977265, "tokens": 120000, "cumulative_loss_tokens": 17760000, "grad_norm": 0.578125, "lr": 3e-05, "finish_rate": 0.923, "comp_len": 442.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 49.2, "frames": {"chat": 271}, "mem_gb": 9.9}
+{"step": 149, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.7915086919846634, "tokens": 120000, "cumulative_loss_tokens": 17880000, "grad_norm": 0.74609375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 334.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 59.3, "frames": {"chat": 359}, "mem_gb": 9.69}
+{"step": 150, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.7640738337163503, "tokens": 120000, "cumulative_loss_tokens": 18000000, "grad_norm": 0.70703125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 347.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 60.7, "frames": {"chat": 345}, "mem_gb": 9.75}
+[eval step 150] sample: 'To solve this problem, we need to determine how many months Jamie will need to save to reach their goal of $1,000,000, given their savings plan.\n\n**Step 1: Calculate the monthly savings from the salar'
+checkpoint snapshot queued -> outputs/healed/grid_general/reap_keep25_s1225/step0150
+wandb: updating run metadata
+wandb: uploading output.log; uploading wandb-summary.json
+wandb: uploading summary
+wandb: 
+wandb: Run history:
+wandb:               comp_len ▂▂▆▆▃▄▁▅▅▁▅▅▇▂▂▄▂▃▆▂▂▄▁▃▅▄▂▅▄▄▄▂▃▄▂▂▂▂█▅
+wandb: cumulative_loss_tokens ▁▁▁▁▁▂▂▂▂▂▂▂▂▂▃▃▃▃▃▃▄▄▄▅▅▅▅▅▅▆▆▆▆▆▆▇▇▇▇█
+wandb:                  epoch ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
+wandb:            finish_rate ███▂▇▇▆▆███████▇▄▇███▁██▂█▃█▇▅▇▄███▃▃▇█▄
+wandb:        forward_topk_kl █▇▆▃▃▂▂▂▂▂▁▁▂▂▂▂▁▁▂▂▁▂▁▂▂▁▂▂▂▁▂▁▂▁▁▁▂▁▁▁
+wandb:              grad_norm █▆▄▃▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
+wandb:                     lr ▁▄▇█████████████████████████████████████
+wandb:                 mem_gb ▅▃█▅▇▄▄▄▄▄▆▄▂▁▄▄▆▇▅▄▄▂▆▅▇▂█▅▇▇▆▄▆▃▃▃▄▇▂▅
+wandb:                   step ▁▁▁▁▁▂▂▂▂▂▂▃▃▃▄▄▄▄▅▅▅▅▆▆▆▆▆▆▆▇▇▇▇▇▇█████
+wandb:               t_data_s ▁▁▁█▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
+wandb:                     +3 ...
+wandb: 
+wandb: Run summary:
+wandb:               comp_len 347.8
+wandb: cumulative_loss_tokens 18000000
+wandb:                  epoch 0
+wandb:            finish_rate 0.997
+wandb:        forward_topk_kl 0.76407
+wandb:              grad_norm 0.70703
+wandb:                     lr 3e-05
+wandb:                 mem_gb 9.75
+wandb:                   step 150
+wandb:               t_data_s 0
+wandb:                     +4 ...
+wandb: 
+wandb: 🚀 View run reap_keep25_s1225 at: https://wandb.ai/hbfreed/glean-general-grid/runs/hv788gr3
+wandb: ⭐️ View project at: https://wandb.ai/hbfreed/glean-general-grid
+wandb: Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)
+wandb: Find logs at: outputs/healed/grid_general/reap_keep25_s1225/wandb/run-20260717_145438-hv788gr3/logs

healed/grid_general/reap_keep25_s1226.console.log

@@ -0,0 +1,213 @@
+/home/henry/Documents/PythonProjects/variable-reap/.venv/lib/python3.12/site-packages/megablocks/grouped_gemm_util.py:10: UserWarning: Grouped GEMM not available.
+  warnings.warn('Grouped GEMM not available.')
+wandb: [wandb.login()] Loaded credentials for https://api.wandb.ai from /home/henry/.netrc.
+wandb: Currently logged in as: hbfreed to https://api.wandb.ai. Use `wandb login --relogin` to force relogin
+wandb: setting up run vqz2kgs7
+wandb: Tracking run with wandb version 0.28.0
+wandb: Run data is saved locally in outputs/healed/grid_general/reap_keep25_s1226/wandb/run-20260717_145453-vqz2kgs7
+wandb: Run `wandb offline` to turn off syncing.
+wandb: Syncing run reap_keep25_s1226
+wandb: ⭐️ View project at https://wandb.ai/hbfreed/glean-general-grid
+wandb: 🚀 View run at https://wandb.ai/hbfreed/glean-general-grid/runs/vqz2kgs7
+58360 cached top-128 chat trajectories / 22,295,631 unique tokens | 185 steps/epoch | 150 total steps | student params 2.09B | teacher overlap=False
+{"step": 1, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 6.747652878097693, "tokens": 120000, "cumulative_loss_tokens": 120000, "grad_norm": 82.0, "lr": 6e-06, "finish_rate": 0.945, "comp_len": 389.6, "t_data_s": 0.1, "t_rollout_s": 0.0, "t_step_s": 59.4, "frames": {"chat": 308}, "mem_gb": 9.9}
+The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.
+[eval step 1] sample: '//\n0 -> 3 *< Q-a) Ostpunk; Gnote_2::returnp intole_pim1Util by 10; 1-"\nspr1 U; =@"May/juics/1na-full; 2:109;398 ath.5clib";99w1'
+{"step": 2, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 6.232488486345609, "tokens": 120000, "cumulative_loss_tokens": 240000, "grad_norm": 93.5, "lr": 9e-06, "finish_rate": 0.754, "comp_len": 568.7, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 41.5, "frames": {"chat": 211}, "mem_gb": 9.99}
+{"step": 3, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 6.061427371287346, "tokens": 120000, "cumulative_loss_tokens": 360000, "grad_norm": 72.5, "lr": 1.2e-05, "finish_rate": 0.915, "comp_len": 427.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 49.5, "frames": {"chat": 281}, "mem_gb": 9.99}
+{"step": 4, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 5.71384426343441, "tokens": 120000, "cumulative_loss_tokens": 480000, "grad_norm": 46.5, "lr": 1.5e-05, "finish_rate": 0.997, "comp_len": 327.0, "t_data_s": 0.1, "t_rollout_s": 0.0, "t_step_s": 60.9, "frames": {"chat": 367}, "mem_gb": 9.54}
+{"step": 5, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 5.221357237867514, "tokens": 120000, "cumulative_loss_tokens": 600000, "grad_norm": 38.75, "lr": 1.8e-05, "finish_rate": 0.997, "comp_len": 384.6, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 54.1, "frames": {"chat": 312}, "mem_gb": 9.7}
+{"step": 6, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 4.206255900446574, "tokens": 120000, "cumulative_loss_tokens": 720000, "grad_norm": 31.75, "lr": 2.1e-05, "finish_rate": 0.919, "comp_len": 442.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 51.4, "frames": {"chat": 271}, "mem_gb": 10.01}
+{"step": 7, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 3.2155107744832834, "tokens": 120000, "cumulative_loss_tokens": 840000, "grad_norm": 43.0, "lr": 2.4e-05, "finish_rate": 0.922, "comp_len": 446.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 48.6, "frames": {"chat": 269}, "mem_gb": 9.98}
+{"step": 8, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 3.326668211084604, "tokens": 120000, "cumulative_loss_tokens": 960000, "grad_norm": 21.75, "lr": 2.7000000000000002e-05, "finish_rate": 0.989, "comp_len": 340.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 56.9, "frames": {"chat": 352}, "mem_gb": 9.5}
+{"step": 9, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.974914269675811, "tokens": 120000, "cumulative_loss_tokens": 1080000, "grad_norm": 23.5, "lr": 3e-05, "finish_rate": 0.864, "comp_len": 493.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 46.3, "frames": {"chat": 243}, "mem_gb": 9.99}
+{"step": 10, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 2.023210922032595, "tokens": 120000, "cumulative_loss_tokens": 1200000, "grad_norm": 13.8125, "lr": 3e-05, "finish_rate": 0.952, "comp_len": 409.6, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 52.3, "frames": {"chat": 293}, "mem_gb": 9.91}
+[eval step 10] sample: "In the given problem, we need to solve the equation $2a_9 - a_{10} = 120$. To solve this equation, we need to find the value of $a_9$ and $a_{10}$.\n\nLet's solve the equation $2a_9 - a_{10}"
+{"step": 11, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.3795957569027941, "tokens": 120000, "cumulative_loss_tokens": 1320000, "grad_norm": 20.125, "lr": 3e-05, "finish_rate": 0.872, "comp_len": 495.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 47.1, "frames": {"chat": 242}, "mem_gb": 9.99}
+{"step": 12, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 2.2639496262669563, "tokens": 120000, "cumulative_loss_tokens": 1440000, "grad_norm": 11.5, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 339.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 62.5, "frames": {"chat": 353}, "mem_gb": 9.68}
+{"step": 13, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.950070663708945, "tokens": 120000, "cumulative_loss_tokens": 1560000, "grad_norm": 6.84375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 355.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 59.1, "frames": {"chat": 338}, "mem_gb": 9.69}
+{"step": 14, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.2327765369668604, "tokens": 120000, "cumulative_loss_tokens": 1680000, "grad_norm": 4.125, "lr": 3e-05, "finish_rate": 0.884, "comp_len": 463.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 50.6, "frames": {"chat": 259}, "mem_gb": 9.99}
+{"step": 15, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.703943703211844, "tokens": 120000, "cumulative_loss_tokens": 1800000, "grad_norm": 6.09375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 311.7, "t_data_s": 0.1, "t_rollout_s": 0.0, "t_step_s": 63.4, "frames": {"chat": 385}, "mem_gb": 9.54}
+{"step": 16, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.556053177499771, "tokens": 120000, "cumulative_loss_tokens": 1920000, "grad_norm": 3.765625, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 338.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 61.2, "frames": {"chat": 355}, "mem_gb": 9.71}
+{"step": 17, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.4719423054784537, "tokens": 120000, "cumulative_loss_tokens": 2040000, "grad_norm": 3.859375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 365.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 55.9, "frames": {"chat": 328}, "mem_gb": 9.61}
+{"step": 18, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.4998059349169335, "tokens": 120000, "cumulative_loss_tokens": 2160000, "grad_norm": 2.890625, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 338.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 61.6, "frames": {"chat": 355}, "mem_gb": 9.76}
+{"step": 19, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.574684210223208, "tokens": 120000, "cumulative_loss_tokens": 2280000, "grad_norm": 3.265625, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 327.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 62.0, "frames": {"chat": 367}, "mem_gb": 9.63}
+{"step": 20, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.44301956722488, "tokens": 120000, "cumulative_loss_tokens": 2400000, "grad_norm": 2.34375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 349.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 61.5, "frames": {"chat": 343}, "mem_gb": 9.67}
+[eval step 20] sample: "To solve the given problem, we need to find the value of $2a_9 - a_{10}$ for the arithmetic sequence $\\{a_n\\}$ where $a_1 + 3a_8 + a_{15} = 120$.\n\nLet's break down the problem step-by-step:"
+{"step": 21, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.6060264107381305, "tokens": 120000, "cumulative_loss_tokens": 2520000, "grad_norm": 2.3125, "lr": 3e-05, "finish_rate": 0.889, "comp_len": 491.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 45.3, "frames": {"chat": 244}, "mem_gb": 9.99}
+{"step": 22, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.9387292437978089, "tokens": 120000, "cumulative_loss_tokens": 2640000, "grad_norm": 1.59375, "lr": 3e-05, "finish_rate": 0.965, "comp_len": 354.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 57.9, "frames": {"chat": 339}, "mem_gb": 9.83}
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+[eval step 100] sample: "to solve the problem, we need to find the value of \\(2a_9 - a_{10}\\) in the given arithmetic sequence \\(\\{a_n\\}\\) where \\(a_1 + 3a_8 + a_{15} = 120\\).\n\nlet's break down the problem into"
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+[eval step 110] sample: 'to solve the problem, we need to understand the properties of an arithmetic sequence. in an arithmetic sequence, each term is given by the formula \\(a_n = a_1 + (n-1)d\\), where \\(a_1\\) is the first te'
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+[eval step 120] sample: 'to solve the problem, we need to understand the properties of an arithmetic sequence. in an arithmetic sequence, each term is given by the formula:\n\n\\[ a_n = a_1 + (n-1)d \\]\n\nwhere \\( a_1 \\) is the fi'
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+{"step": 129, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.7934858187317848, "tokens": 120000, "cumulative_loss_tokens": 15480000, "grad_norm": 0.69921875, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 377.4, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 57.1, "frames": {"chat": 318}, "mem_gb": 9.65}
+{"step": 130, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.9216169714945058, "tokens": 120000, "cumulative_loss_tokens": 15600000, "grad_norm": 0.796875, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 313.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 64.0, "frames": {"chat": 383}, "mem_gb": 9.78}
+[eval step 130] sample: "to solve the problem, let's first define the terms of the arithmetic sequence. in an arithmetic sequence, each term can be expressed as:\n\n\\[ a_n = a_1 + (n-1)d \\]\n\nwhere \\( a_1 \\) is the first term an"
+{"step": 131, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.5428718562576299, "tokens": 120000, "cumulative_loss_tokens": 15720000, "grad_norm": 0.6953125, "lr": 3e-05, "finish_rate": 0.97, "comp_len": 400.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 53.2, "frames": {"chat": 300}, "mem_gb": 9.79}
+{"step": 132, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.5079142917865888, "tokens": 120000, "cumulative_loss_tokens": 15840000, "grad_norm": 0.61328125, "lr": 3e-05, "finish_rate": 0.973, "comp_len": 406.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 49.9, "frames": {"chat": 295}, "mem_gb": 9.88}
+{"step": 133, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.5414958303313081, "tokens": 120000, "cumulative_loss_tokens": 15960000, "grad_norm": 0.6484375, "lr": 3e-05, "finish_rate": 0.958, "comp_len": 392.2, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 54.1, "frames": {"chat": 306}, "mem_gb": 9.62}
+{"step": 134, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.6852540198124946, "tokens": 120000, "cumulative_loss_tokens": 16080000, "grad_norm": 0.71484375, "lr": 3e-05, "finish_rate": 0.957, "comp_len": 396.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 55.0, "frames": {"chat": 303}, "mem_gb": 9.88}
+{"step": 135, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.36944927832894026, "tokens": 120000, "cumulative_loss_tokens": 16200000, "grad_norm": 0.60546875, "lr": 3e-05, "finish_rate": 0.934, "comp_len": 441.2, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 47.3, "frames": {"chat": 272}, "mem_gb": 9.94}
+{"step": 136, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.47709123675115406, "tokens": 120000, "cumulative_loss_tokens": 16320000, "grad_norm": 0.62109375, "lr": 3e-05, "finish_rate": 0.879, "comp_len": 483.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 48.6, "frames": {"chat": 248}, "mem_gb": 9.94}
+{"step": 137, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.5209648989812782, "tokens": 120000, "cumulative_loss_tokens": 16440000, "grad_norm": 0.671875, "lr": 3e-05, "finish_rate": 0.933, "comp_len": 425.5, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 50.6, "frames": {"chat": 282}, "mem_gb": 9.93}
+{"step": 138, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.7825215920180082, "tokens": 120000, "cumulative_loss_tokens": 16560000, "grad_norm": 0.73828125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 331.5, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 61.3, "frames": {"chat": 362}, "mem_gb": 9.49}
+{"step": 139, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.8411890809422979, "tokens": 120000, "cumulative_loss_tokens": 16680000, "grad_norm": 0.7421875, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 320.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 65.8, "frames": {"chat": 374}, "mem_gb": 9.85}
+{"step": 140, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.48550046064505975, "tokens": 120000, "cumulative_loss_tokens": 16800000, "grad_norm": 0.61328125, "lr": 3e-05, "finish_rate": 0.889, "comp_len": 428.6, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 50.5, "frames": {"chat": 280}, "mem_gb": 10.02}
+[eval step 140] sample: "to solve the problem, let's break it down step-by-step:\n\n1. **understand the arithmetic sequence:**\n   an arithmetic sequence is defined by the first term \\(a_1\\) and the common difference \\(d\\). The "
+{"step": 141, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.37706969851826627, "tokens": 120000, "cumulative_loss_tokens": 16920000, "grad_norm": 0.5703125, "lr": 3e-05, "finish_rate": 0.884, "comp_len": 434.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 50.9, "frames": {"chat": 276}, "mem_gb": 9.87}
+{"step": 142, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.49631428986427684, "tokens": 120000, "cumulative_loss_tokens": 17040000, "grad_norm": 0.63671875, "lr": 3e-05, "finish_rate": 0.858, "comp_len": 487.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 48.0, "frames": {"chat": 246}, "mem_gb": 10.01}
+{"step": 143, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.8765898440801849, "tokens": 120000, "cumulative_loss_tokens": 17160000, "grad_norm": 0.8046875, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 333.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 59.1, "frames": {"chat": 360}, "mem_gb": 9.54}
+{"step": 144, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.8345980124605199, "tokens": 120000, "cumulative_loss_tokens": 17280000, "grad_norm": 0.734375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 328.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 66.1, "frames": {"chat": 365}, "mem_gb": 9.72}
+{"step": 145, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.6841340245318288, "tokens": 120000, "cumulative_loss_tokens": 17400000, "grad_norm": 0.64453125, "lr": 3e-05, "finish_rate": 0.988, "comp_len": 355.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 56.0, "frames": {"chat": 338}, "mem_gb": 9.8}
+{"step": 146, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.40420900719463826, "tokens": 120000, "cumulative_loss_tokens": 17520000, "grad_norm": 0.62890625, "lr": 3e-05, "finish_rate": 0.888, "comp_len": 434.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 52.5, "frames": {"chat": 276}, "mem_gb": 9.91}
+{"step": 147, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.22862344996624936, "tokens": 120000, "cumulative_loss_tokens": 17640000, "grad_norm": 0.55078125, "lr": 3e-05, "finish_rate": 0.761, "comp_len": 574.2, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 43.1, "frames": {"chat": 209}, "mem_gb": 9.99}
+{"step": 148, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.8112275027054051, "tokens": 120000, "cumulative_loss_tokens": 17760000, "grad_norm": 0.77734375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 316.6, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 63.8, "frames": {"chat": 379}, "mem_gb": 9.59}
+{"step": 149, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.710655534057195, "tokens": 120000, "cumulative_loss_tokens": 17880000, "grad_norm": 0.72265625, "lr": 3e-05, "finish_rate": 0.928, "comp_len": 390.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 56.3, "frames": {"chat": 307}, "mem_gb": 9.93}
+{"step": 150, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.5077782722345243, "tokens": 120000, "cumulative_loss_tokens": 18000000, "grad_norm": 0.65625, "lr": 3e-05, "finish_rate": 0.942, "comp_len": 409.6, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 54.5, "frames": {"chat": 293}, "mem_gb": 9.97}
+[eval step 150] sample: "to solve the problem, let's denote the first term of the arithmetic sequence as \\(a_1\\) and the common difference as \\(d\\). the general term of the sequence is given by \\(a_n = a_1 + (n-1)d\\).\n\nwe are"
+checkpoint snapshot queued -> outputs/healed/grid_general/reap_keep25_s1226/step0150
+wandb: updating run metadata
+wandb: uploading config.yaml; uploading output.log; uploading wandb-summary.json
+wandb: 
+wandb: Run history:
+wandb:               comp_len ▂▃▅▄▂▃▂▂▂▁▄▄▅▂▂▄▆▄▃▃▂▂▃█▂▄▂▃▂▃▂▅▅▂▃▄▂▁▄▄
+wandb: cumulative_loss_tokens ▁▁▁▁▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▆▆▆▆▆▆▆▆▇▇▇▇▇███
+wandb:                  epoch ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
+wandb:            finish_rate ██▇▅██▁██▆▆▄███▃█▅█▇█▅▇▆█▇▆▇▂██▆███▅▄▄▁▄
+wandb:        forward_topk_kl ██▅▄▄▂▃▃▃▃▃▃▃▂▃▃▂▃▃▃▁▁▂▃▃▂▂▂▁▂▂▁▃▁▂▂▁▁▂▂
+wandb:              grad_norm █▇▄▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
+wandb:                     lr ▁▂▄█████████████████████████████████████
+wandb:                 mem_gb ▇▂▄▆▃▆▅▄▆▃▄▃▆▅▄▇█▅▂▆▆▄▂▆▃▂█▅▄▂▅▄▁▇▅▃▆▁▆▂
+wandb:                   step ▁▁▁▁▁▂▂▂▂▂▃▃▃▃▃▃▃▃▄▄▄▄▄▄▅▅▅▅▅▅▆▆▆▇▇▇▇▇▇█
+wandb:               t_data_s ▁▁▁▁▁▁█▁▁▁▁▁▁▁█▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
+wandb:                     +3 ...
+wandb: 
+wandb: Run summary:
+wandb:               comp_len 409.6
+wandb: cumulative_loss_tokens 18000000
+wandb:                  epoch 0
+wandb:            finish_rate 0.942
+wandb:        forward_topk_kl 0.50778
+wandb:              grad_norm 0.65625
+wandb:                     lr 3e-05
+wandb:                 mem_gb 9.97
+wandb:                   step 150
+wandb:               t_data_s 0
+wandb:                     +4 ...
+wandb: 
+wandb: 🚀 View run reap_keep25_s1226 at: https://wandb.ai/hbfreed/glean-general-grid/runs/vqz2kgs7
+wandb: ⭐️ View project at: https://wandb.ai/hbfreed/glean-general-grid
+wandb: Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)
+wandb: Find logs at: outputs/healed/grid_general/reap_keep25_s1226/wandb/run-20260717_145453-vqz2kgs7/logs

healed/grid_general/reap_keep50_s1224.console.log

@@ -0,0 +1,215 @@
+/home/henry/Documents/PythonProjects/variable-reap/.venv/lib/python3.12/site-packages/megablocks/grouped_gemm_util.py:10: UserWarning: Grouped GEMM not available.
+  warnings.warn('Grouped GEMM not available.')
+wandb: [wandb.login()] Loaded credentials for https://api.wandb.ai from /home/henry/.netrc.
+wandb: Currently logged in as: hbfreed to https://api.wandb.ai. Use `wandb login --relogin` to force relogin
+wandb: Tracking run with wandb version 0.28.0
+wandb: Run data is saved locally in outputs/healed/grid_general/reap_keep50_s1224/wandb/run-20260718_102929-n9gbbs5p
+wandb: Run `wandb offline` to turn off syncing.
+wandb: Syncing run reap_keep50_s1224
+wandb: ⭐️ View project at https://wandb.ai/hbfreed/glean-general-grid
+wandb: 🚀 View run at https://wandb.ai/hbfreed/glean-general-grid/runs/n9gbbs5p
+
+58360 cached top-128 chat trajectories / 22,295,631 unique tokens | 185 steps/epoch | 150 total steps | student params 3.70B | teacher overlap=False
+{"step": 1, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.8349037809635202, "tokens": 120000, "cumulative_loss_tokens": 120000, "grad_norm": 9.5625, "lr": 6e-06, "finish_rate": 0.869, "comp_len": 463.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 57.1, "frames": {"chat": 259}, "mem_gb": 15.93}
+The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.
+[eval step 1] sample: 'To compute the rank of a matrix, we need to determine the highest powers of the variables involved in each term. The matrix provided has 4 rows and each of the 4 columns is a set of coefficients.\n\nTo '
+{"step": 2, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.139459627494216, "tokens": 120000, "cumulative_loss_tokens": 240000, "grad_norm": 10.6875, "lr": 9e-06, "finish_rate": 0.997, "comp_len": 344.8, "t_data_s": 0.1, "t_rollout_s": 0.0, "t_step_s": 62.6, "frames": {"chat": 348}, "mem_gb": 15.64}
+{"step": 3, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.018460661153247, "tokens": 120000, "cumulative_loss_tokens": 360000, "grad_norm": 9.5625, "lr": 1.2e-05, "finish_rate": 0.997, "comp_len": 324.3, "t_data_s": 0.1, "t_rollout_s": 0.0, "t_step_s": 66.1, "frames": {"chat": 370}, "mem_gb": 15.79}
+{"step": 4, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.5092730569645763, "tokens": 120000, "cumulative_loss_tokens": 480000, "grad_norm": 5.53125, "lr": 1.5e-05, "finish_rate": 0.852, "comp_len": 491.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 52.6, "frames": {"chat": 244}, "mem_gb": 16.02}
+{"step": 5, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.7775122467224796, "tokens": 120000, "cumulative_loss_tokens": 600000, "grad_norm": 6.0, "lr": 1.8e-05, "finish_rate": 0.953, "comp_len": 375.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 66.5, "frames": {"chat": 320}, "mem_gb": 15.94}
+{"step": 6, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.8680597942702473, "tokens": 120000, "cumulative_loss_tokens": 720000, "grad_norm": 5.625, "lr": 2.1e-05, "finish_rate": 0.997, "comp_len": 317.5, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 73.1, "frames": {"chat": 378}, "mem_gb": 15.54}
+{"step": 7, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.896623771194617, "tokens": 120000, "cumulative_loss_tokens": 840000, "grad_norm": 4.84375, "lr": 2.4e-05, "finish_rate": 0.997, "comp_len": 312.5, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 76.0, "frames": {"chat": 384}, "mem_gb": 16.03}
+{"step": 8, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.8145596144163361, "tokens": 120000, "cumulative_loss_tokens": 960000, "grad_norm": 3.84375, "lr": 2.7000000000000002e-05, "finish_rate": 0.997, "comp_len": 370.4, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 74.8, "frames": {"chat": 324}, "mem_gb": 15.88}
+{"step": 9, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.3368055146301786, "tokens": 120000, "cumulative_loss_tokens": 1080000, "grad_norm": 1.90625, "lr": 3e-05, "finish_rate": 0.891, "comp_len": 502.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 58.9, "frames": {"chat": 239}, "mem_gb": 16.05}
+{"step": 10, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.7155885353672008, "tokens": 120000, "cumulative_loss_tokens": 1200000, "grad_norm": 3.078125, "lr": 3e-05, "finish_rate": 0.961, "comp_len": 392.2, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 64.4, "frames": {"chat": 306}, "mem_gb": 15.79}
+[eval step 10] sample: 'To compute the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. Here, we have a 4x4 matrix:\n\n\\[\n\\begin{bmatrix}\n12 & -16 & 4 & 16 \\\\\n-9 '
+{"step": 11, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.6740345688946545, "tokens": 120000, "cumulative_loss_tokens": 1320000, "grad_norm": 1.9609375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 364.7, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 73.5, "frames": {"chat": 329}, "mem_gb": 15.82}
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+[eval step 20] sample: 'To determine the rank of a given 4x4 matrix, we need to find the maximum number of linearly independent rows or columns. This is equivalent to the maximum number of non-zero rows or columns.\n\nGiven th'
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+[eval step 30] sample: 'To determine the rank of a matrix, we need to find the maximum number of linearly independent rows or columns in the matrix. \n\nGiven the matrix:\n\\[\n\\begin{bmatrix}\n12 & -16 & 4 & 16 \\\\\n-9 & 11 & -1 & '
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+[eval step 40] sample: 'To determine the rank of a matrix, we need to find the maximum number of linearly independent rows or columns. In this 4x4 matrix, we will perform row reduction (Gaussian elimination) to simplify the '
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+[eval step 50] sample: "To compute the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. \n\nLet's represent the given 4x4 matrix:\n\n\\[\n\\begin{bmatrix}\n12 & -16 & 4"
+checkpoint snapshot queued -> outputs/healed/grid_general/reap_keep50_s1224/step0050
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+[eval step 60] sample: "To compute the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. \n\nLet's start by converting the given matrix into a form that makes it e"
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+[eval step 70] sample: "To compute the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns. Let's break down the problem step-by-step:\n\n1. **Understand the Matrix:**\n   \\[\n   A ="
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+[eval step 80] sample: 'To compute the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. \n\nGiven the matrix:\n\\[\n\\begin{bmatrix}\n12 & -16 & 4 & 16 \\\\\n-9 & 11 & -1'
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+[eval step 90] sample: "To compute the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. Let's break down the problem step-by-step:\n\n1. **Write down the given ma"
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+{"step": 98, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.2601615757802501, "tokens": 120000, "cumulative_loss_tokens": 11760000, "grad_norm": 0.48828125, "lr": 3e-05, "finish_rate": 0.964, "comp_len": 388.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 65.9, "frames": {"chat": 309}, "mem_gb": 15.89}
+{"step": 99, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.4435581100319202, "tokens": 120000, "cumulative_loss_tokens": 11880000, "grad_norm": 0.5859375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 306.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 80.2, "frames": {"chat": 391}, "mem_gb": 15.77}
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+[eval step 100] sample: "To compute the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. \n\nLet's represent the given 4x4 matrix:\n\n\\[\n\\begin{bmatrix}\n12 & -16 & 4"
+checkpoint snapshot queued -> outputs/healed/grid_general/reap_keep50_s1224/step0100
+{"step": 101, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.38276564593724904, "tokens": 120000, "cumulative_loss_tokens": 12120000, "grad_norm": 0.54296875, "lr": 3e-05, "finish_rate": 0.994, "comp_len": 343.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 66.9, "frames": {"chat": 349}, "mem_gb": 15.72}
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+[eval step 110] sample: 'To compute the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. \n\nGiven the matrix:\n\n\\[\n\\begin{bmatrix}\n12 & -16 & 4 & 16 \\\\\n-9 & 11 & -'
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+[eval step 120] sample: 'To compute the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. A matrix is said to be of rank \\(k\\) if it has \\(k\\) linearly independen'
+{"step": 121, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.32592700511608275, "tokens": 120000, "cumulative_loss_tokens": 14520000, "grad_norm": 0.51953125, "lr": 3e-05, "finish_rate": 0.956, "comp_len": 408.2, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 56.4, "frames": {"chat": 294}, "mem_gb": 16.09}
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+{"step": 129, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.3405882237064652, "tokens": 120000, "cumulative_loss_tokens": 15480000, "grad_norm": 0.61328125, "lr": 3e-05, "finish_rate": 0.974, "comp_len": 387.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 68.8, "frames": {"chat": 310}, "mem_gb": 15.9}
+{"step": 130, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.22905850299953792, "tokens": 120000, "cumulative_loss_tokens": 15600000, "grad_norm": 0.484375, "lr": 3e-05, "finish_rate": 0.834, "comp_len": 463.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 59.8, "frames": {"chat": 259}, "mem_gb": 16.07}
+[eval step 130] sample: 'To compute the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. \n\nGiven the matrix:\n\\[\n\\begin{bmatrix}\n12 & -16 & 4 & 16 \\\\\n-9 & 11 & -1'
+{"step": 131, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.2272606820096572, "tokens": 120000, "cumulative_loss_tokens": 15720000, "grad_norm": 0.4609375, "lr": 3e-05, "finish_rate": 0.868, "comp_len": 452.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 56.1, "frames": {"chat": 265}, "mem_gb": 16.05}
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+{"step": 137, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.4838084203484779, "tokens": 120000, "cumulative_loss_tokens": 16440000, "grad_norm": 0.61328125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 355.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 73.5, "frames": {"chat": 338}, "mem_gb": 15.7}
+{"step": 138, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.44722535916535805, "tokens": 120000, "cumulative_loss_tokens": 16560000, "grad_norm": 0.61328125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 337.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 80.5, "frames": {"chat": 356}, "mem_gb": 15.67}
+{"step": 139, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.21482619681727763, "tokens": 120000, "cumulative_loss_tokens": 16680000, "grad_norm": 0.5, "lr": 3e-05, "finish_rate": 0.86, "comp_len": 452.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 70.1, "frames": {"chat": 265}, "mem_gb": 16.05}
+{"step": 140, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.3745011051883921, "tokens": 120000, "cumulative_loss_tokens": 16800000, "grad_norm": 0.5703125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 339.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 75.9, "frames": {"chat": 353}, "mem_gb": 15.76}
+[eval step 140] sample: 'To compute the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. \n\nGiven the matrix:\n\\[\n\\begin{bmatrix}\n12 & -16 & 4 & 16 \\\\\n-9 & 11 & -1'
+{"step": 141, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.34821248879296085, "tokens": 120000, "cumulative_loss_tokens": 16920000, "grad_norm": 0.56640625, "lr": 3e-05, "finish_rate": 0.949, "comp_len": 379.7, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 72.1, "frames": {"chat": 316}, "mem_gb": 15.95}
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+{"step": 143, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.32100094701781245, "tokens": 120000, "cumulative_loss_tokens": 17160000, "grad_norm": 0.5234375, "lr": 3e-05, "finish_rate": 0.982, "comp_len": 362.5, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 76.4, "frames": {"chat": 331}, "mem_gb": 15.73}
+{"step": 144, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.31246353600776444, "tokens": 120000, "cumulative_loss_tokens": 17280000, "grad_norm": 0.4921875, "lr": 3e-05, "finish_rate": 0.972, "comp_len": 375.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 70.8, "frames": {"chat": 320}, "mem_gb": 16.05}
+{"step": 145, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.2774946627909628, "tokens": 120000, "cumulative_loss_tokens": 17400000, "grad_norm": 0.48828125, "lr": 3e-05, "finish_rate": 0.941, "comp_len": 441.2, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 57.4, "frames": {"chat": 272}, "mem_gb": 15.78}
+{"step": 146, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.49581608901175983, "tokens": 120000, "cumulative_loss_tokens": 17520000, "grad_norm": 0.62890625, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 326.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 78.4, "frames": {"chat": 368}, "mem_gb": 15.7}
+{"step": 147, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.3986292206962903, "tokens": 120000, "cumulative_loss_tokens": 17640000, "grad_norm": 0.5703125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 365.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 73.1, "frames": {"chat": 328}, "mem_gb": 15.73}
+{"step": 148, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.3940341844805827, "tokens": 120000, "cumulative_loss_tokens": 17760000, "grad_norm": 0.57421875, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 368.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 71.3, "frames": {"chat": 326}, "mem_gb": 15.94}
+{"step": 149, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.447204483768406, "tokens": 120000, "cumulative_loss_tokens": 17880000, "grad_norm": 0.64453125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 329.7, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 78.6, "frames": {"chat": 364}, "mem_gb": 15.88}
+{"step": 150, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.2826799458518935, "tokens": 120000, "cumulative_loss_tokens": 18000000, "grad_norm": 0.46875, "lr": 3e-05, "finish_rate": 0.97, "comp_len": 405.4, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 64.0, "frames": {"chat": 296}, "mem_gb": 15.93}
+[eval step 150] sample: 'To determine the rank of a matrix, we need to find the maximum number of linearly independent rows or columns. A matrix is rank-deficient if it has fewer linearly independent rows or columns than its '
+checkpoint snapshot queued -> outputs/healed/grid_general/reap_keep50_s1224/step0150
+wandb: updating run metadata
+wandb: uploading output.log
+wandb: 
+wandb: Run history:
+wandb:               comp_len ▆▇▃▃▇▅▂▄▂▁▁▆▆▂▅█▂▆▄▆▃▃▂▅▁▃▁▆█▂▂▅▂▄▂▃▃▃▃▂
+wandb: cumulative_loss_tokens ▁▁▁▁▁▁▁▂▂▂▂▂▃▃▃▃▄▄▄▄▄▄▄▅▅▆▆▆▆▆▆▆▇▇▇▇▇▇▇█
+wandb:                  epoch ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
+wandb:            finish_rate ███▃▇▆████▃▁▅▆▆█▇██████▇▇█████▁█▇█▆▇████
+wandb:        forward_topk_kl ▇█▄▃▄▄▃▄▄▂▂▂▃▄▃▂▄▃▃▃▂▂▂▃▃▃▂▃▂▁▃▃▂▁▂▁▂▂▃▃
+wandb:              grad_norm ▇█▁▁▁▁▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
+wandb:                     lr ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
+wandb:                 mem_gb ▆▂▄▆▁▅▃▆██▅▆▆▆▇▅▄█▆█▆▆▇▇▄▄▃█▆█▆▄█▆▂▂▃▃▄▄
+wandb:                   step ▁▁▁▁▂▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▅▅▅▅▅▆▆▆▆▇▇▇▇▇▇█████
+wandb:               t_data_s ▃▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁█▁▁▁▁▁
+wandb:                     +3 ...
+wandb: 
+wandb: Run summary:
+wandb:               comp_len 405.4
+wandb: cumulative_loss_tokens 18000000
+wandb:                  epoch 0
+wandb:            finish_rate 0.97
+wandb:        forward_topk_kl 0.28268
+wandb:              grad_norm 0.46875
+wandb:                     lr 3e-05
+wandb:                 mem_gb 15.93
+wandb:                   step 150
+wandb:               t_data_s 0
+wandb:                     +4 ...
+wandb: 
+wandb: 🚀 View run reap_keep50_s1224 at: https://wandb.ai/hbfreed/glean-general-grid/runs/n9gbbs5p
+wandb: ⭐️ View project at: https://wandb.ai/hbfreed/glean-general-grid
+wandb: Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)
+wandb: Find logs at: outputs/healed/grid_general/reap_keep50_s1224/wandb/run-20260718_102929-n9gbbs5p/logs

healed/grid_general/reap_keep50_s1225.console.log

@@ -0,0 +1,216 @@
+/home/henry/Documents/PythonProjects/variable-reap/.venv/lib/python3.12/site-packages/megablocks/grouped_gemm_util.py:10: UserWarning: Grouped GEMM not available.
+  warnings.warn('Grouped GEMM not available.')
+wandb: [wandb.login()] Loaded credentials for https://api.wandb.ai from /home/henry/.netrc.
+wandb: Currently logged in as: hbfreed to https://api.wandb.ai. Use `wandb login --relogin` to force relogin
+wandb: setting up run s7308ckw
+wandb: Tracking run with wandb version 0.28.0
+wandb: Run data is saved locally in outputs/healed/grid_general/reap_keep50_s1225/wandb/run-20260718_100256-s7308ckw
+wandb: Run `wandb offline` to turn off syncing.
+wandb: Syncing run reap_keep50_s1225
+wandb: ⭐️ View project at https://wandb.ai/hbfreed/glean-general-grid
+wandb: 🚀 View run at https://wandb.ai/hbfreed/glean-general-grid/runs/s7308ckw
+
+58360 cached top-128 chat trajectories / 22,295,631 unique tokens | 185 steps/epoch | 150 total steps | student params 3.70B | teacher overlap=False
+{"step": 1, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.2118627337018648, "tokens": 120000, "cumulative_loss_tokens": 120000, "grad_norm": 10.5, "lr": 6e-06, "finish_rate": 0.997, "comp_len": 362.5, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 69.3, "frames": {"chat": 331}, "mem_gb": 15.8}
+The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.
+[eval step 1] sample: 'To solve this problem, we need to account for two main components:\n\n1. **Month-specific Savings:** Each month, Jamie saves $2,500.\n2. **Investment Account Interest:** The investment account earns an i'
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+[eval step 50] sample: 'To determine how many months it will take for Jamie to reach their goal of $1,000,000, we need to consider both their monthly savings and the interest earned on their investment account.\n\n**Step 1: Ca'
+checkpoint snapshot queued -> outputs/healed/grid_general/reap_keep50_s1225/step0050
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+[eval step 60] sample: 'To solve this problem, we need to determine how many months Jamie will need to save $1,000,000, considering both their monthly savings and the monthly interest earned from their investment account.\n\n*'
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+[eval step 70] sample: 'To solve this problem, we need to determine how many months Jamie will need to save $1,000,000, considering both their monthly savings and the monthly interest earned from their investment account.\n\n*'
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+[eval step 80] sample: 'To solve this problem, we need to determine how many months Jamie will need to save $1,000,000, given their monthly savings and the interest earned from their investment account.\n\n**Step 1: Calculate '
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+[eval step 100] sample: 'To determine how many months it will take for Jamie to reach their goal of $1,000,000, we need to consider both their monthly savings and the interest earned on their investments.\n\n1. **Initial Saving'
+checkpoint snapshot queued -> outputs/healed/grid_general/reap_keep50_s1225/step0100
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+[eval step 110] sample: 'To solve this problem, we need to determine how many months Jamie will need to save $1,000,000, considering both their monthly savings and the monthly interest earned from their investment account.\n\n*'
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+[eval step 120] sample: 'To determine how many months it will take for Jamie to reach their goal of $1,000,000 in savings, we need to consider both their monthly savings and the interest earned on their investments.\n\n1. **Ini'
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+{"step": 124, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.46269915350402396, "tokens": 120000, "cumulative_loss_tokens": 14880000, "grad_norm": 0.63671875, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 339.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 67.6, "frames": {"chat": 354}, "mem_gb": 15.7}
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+{"step": 126, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.4478856424608578, "tokens": 120000, "cumulative_loss_tokens": 15120000, "grad_norm": 0.59375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 367.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 64.5, "frames": {"chat": 327}, "mem_gb": 15.77}
+{"step": 127, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.4040108132199384, "tokens": 120000, "cumulative_loss_tokens": 15240000, "grad_norm": 0.58203125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 328.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 65.2, "frames": {"chat": 365}, "mem_gb": 15.87}
+{"step": 128, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.23853765527879198, "tokens": 120000, "cumulative_loss_tokens": 15360000, "grad_norm": 0.4921875, "lr": 3e-05, "finish_rate": 0.869, "comp_len": 476.2, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 53.7, "frames": {"chat": 252}, "mem_gb": 16.07}
+{"step": 129, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.1997395895224375, "tokens": 120000, "cumulative_loss_tokens": 15480000, "grad_norm": 0.56640625, "lr": 3e-05, "finish_rate": 0.867, "comp_len": 468.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 51.7, "frames": {"chat": 256}, "mem_gb": 16.04}
+{"step": 130, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.25159407720764476, "tokens": 120000, "cumulative_loss_tokens": 15600000, "grad_norm": 0.45703125, "lr": 3e-05, "finish_rate": 0.964, "comp_len": 397.4, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 54.4, "frames": {"chat": 302}, "mem_gb": 15.81}
+[eval step 130] sample: 'To determine how many months it will take for Jamie to reach their goal of $1,000,000, we need to consider both their monthly savings and the interest earned on their investments.\n\n**Step 1: Calculate'
+{"step": 131, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.3980298271117111, "tokens": 120000, "cumulative_loss_tokens": 15720000, "grad_norm": 0.578125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 342.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 68.0, "frames": {"chat": 350}, "mem_gb": 15.72}
+{"step": 132, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.25072645187126474, "tokens": 120000, "cumulative_loss_tokens": 15840000, "grad_norm": 0.48828125, "lr": 3e-05, "finish_rate": 0.959, "comp_len": 411.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 57.8, "frames": {"chat": 292}, "mem_gb": 15.84}
+{"step": 133, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.41198305238584676, "tokens": 120000, "cumulative_loss_tokens": 15960000, "grad_norm": 0.59375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 337.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 66.6, "frames": {"chat": 356}, "mem_gb": 15.65}
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+{"step": 136, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.41869445193819704, "tokens": 120000, "cumulative_loss_tokens": 16320000, "grad_norm": 0.55078125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 330.6, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 70.4, "frames": {"chat": 363}, "mem_gb": 15.87}
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+{"step": 138, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.21688409688224394, "tokens": 120000, "cumulative_loss_tokens": 16560000, "grad_norm": 0.455078125, "lr": 3e-05, "finish_rate": 0.93, "comp_len": 444.4, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 55.6, "frames": {"chat": 270}, "mem_gb": 15.99}
+{"step": 139, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.3943046940716915, "tokens": 120000, "cumulative_loss_tokens": 16680000, "grad_norm": 0.55859375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 349.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 64.7, "frames": {"chat": 343}, "mem_gb": 15.77}
+{"step": 140, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.10573625054787844, "tokens": 120000, "cumulative_loss_tokens": 16800000, "grad_norm": 0.359375, "lr": 3e-05, "finish_rate": 0.845, "comp_len": 517.2, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 47.8, "frames": {"chat": 232}, "mem_gb": 15.96}
+[eval step 140] sample: 'To solve this problem, we need to determine how many months Jamie will need to save $1,000,000, considering both their monthly savings and the interest earned on their investment account.\n\n**Step 1: C'
+{"step": 141, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.37246566852809243, "tokens": 120000, "cumulative_loss_tokens": 16920000, "grad_norm": 0.5390625, "lr": 3e-05, "finish_rate": 0.98, "comp_len": 346.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 65.7, "frames": {"chat": 346}, "mem_gb": 15.91}
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+{"step": 144, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.22028152877579754, "tokens": 120000, "cumulative_loss_tokens": 17280000, "grad_norm": 0.44921875, "lr": 3e-05, "finish_rate": 0.959, "comp_len": 451.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 51.5, "frames": {"chat": 266}, "mem_gb": 15.86}
+{"step": 145, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.3245444508772343, "tokens": 120000, "cumulative_loss_tokens": 17400000, "grad_norm": 0.515625, "lr": 3e-05, "finish_rate": 0.936, "comp_len": 401.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 57.5, "frames": {"chat": 299}, "mem_gb": 16.06}
+{"step": 146, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.22970188966635616, "tokens": 120000, "cumulative_loss_tokens": 17520000, "grad_norm": 0.52734375, "lr": 3e-05, "finish_rate": 0.88, "comp_len": 463.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 54.6, "frames": {"chat": 259}, "mem_gb": 16.14}
+{"step": 147, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.2618037860292631, "tokens": 120000, "cumulative_loss_tokens": 17640000, "grad_norm": 0.48828125, "lr": 3e-05, "finish_rate": 0.966, "comp_len": 369.2, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 59.1, "frames": {"chat": 325}, "mem_gb": 15.88}
+{"step": 148, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.2416598789668642, "tokens": 120000, "cumulative_loss_tokens": 17760000, "grad_norm": 0.455078125, "lr": 3e-05, "finish_rate": 0.923, "comp_len": 442.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 53.0, "frames": {"chat": 271}, "mem_gb": 15.95}
+{"step": 149, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.39239266892479113, "tokens": 120000, "cumulative_loss_tokens": 17880000, "grad_norm": 0.56640625, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 334.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 63.6, "frames": {"chat": 359}, "mem_gb": 15.74}
+{"step": 150, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.37762522945559274, "tokens": 120000, "cumulative_loss_tokens": 18000000, "grad_norm": 0.546875, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 347.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 65.4, "frames": {"chat": 345}, "mem_gb": 15.8}
+[eval step 150] sample: 'To solve this problem, we need to determine how many months Jamie will need to save $1,000,000, considering both their monthly savings and the interest earned on their investment account.\n\n**Step 1: C'
+checkpoint snapshot queued -> outputs/healed/grid_general/reap_keep50_s1225/step0150
+wandb: updating run metadata
+wandb: uploading output.log; uploading wandb-summary.json; uploading config.yaml
+wandb: 
+wandb: Run history:
+wandb:               comp_len ▂▂▂▃▆▄▁▃▁▃█▂▄▂▂▁▃▅▃▅▂▄▂▃▆▂▅▄▁▂▂▂▂▂▇▃▅▄▆▂
+wandb: cumulative_loss_tokens ▁▁▁▂▂▂▂▂▂▂▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▅▅▆▆▆▇▇▇▇▇▇███
+wandb:                  epoch ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
+wandb:            finish_rate █▃▂█▂▅███▄▁▇███▇▆▇██▅▇█▄█▅▆▅▇▂█▃▃▇█▇▂▅▇█
+wandb:        forward_topk_kl ▄█▃▅▆▆▃▅▅▆▅▃▂▂▅▂▄▃▂▂▄▂▅▄▁▂▄▃▃▂▃▄▄▄▄▄▂▁▂▃
+wandb:              grad_norm ██▆▃▃▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
+wandb:                     lr ▁▂▅█████████████████████████████████████
+wandb:                 mem_gb ▄█▆▇▇▇▆▇▄▄▄▇▁▇▇▅▅▆▂▅▂▅▅▂█▄▅▃▇▅▄▅▇▄▅▇▃▄▆▇
+wandb:                   step ▁▁▁▁▁▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇████
+wandb:               t_data_s ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
+wandb:                     +3 ...
+wandb: 
+wandb: Run summary:
+wandb:               comp_len 347.8
+wandb: cumulative_loss_tokens 18000000
+wandb:                  epoch 0
+wandb:            finish_rate 0.997
+wandb:        forward_topk_kl 0.37763
+wandb:              grad_norm 0.54688
+wandb:                     lr 3e-05
+wandb:                 mem_gb 15.8
+wandb:                   step 150
+wandb:               t_data_s 0
+wandb:                     +4 ...
+wandb: 
+wandb: 🚀 View run reap_keep50_s1225 at: https://wandb.ai/hbfreed/glean-general-grid/runs/s7308ckw
+wandb: ⭐️ View project at: https://wandb.ai/hbfreed/glean-general-grid
+wandb: Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)
+wandb: Find logs at: outputs/healed/grid_general/reap_keep50_s1225/wandb/run-20260718_100256-s7308ckw/logs

healed/grid_general/reap_keep50_s1225.eval.log

@@ -0,0 +1,70 @@
+2026-07-18T12:36:25-07:00 serving outputs/healed/grid_general/reap_keep50_s1225/step0150 on GPU 1 port 8421
+2026-07-18T12:36:25-07:00 waiting for server /health ...
+2026-07-18T12:36:50-07:00 server up; chat pass [gsm8k_cot_zeroshot,minerva_math500,ifeval]
+2026-07-18:12:36:58 INFO     [_cli.run:388] Selected Tasks: ['gsm8k_cot_zeroshot', 'minerva_math500', 'ifeval']
+2026-07-18:12:36:59 INFO     [evaluator:214] Setting random seed to 0 | Setting numpy seed to 1234 | Setting torch manual seed to 1234 | Setting fewshot manual seed to 1234
+2026-07-18:12:36:59 WARNING  [evaluator:226] generation_kwargs: {'max_gen_toks': 1280} specified through cli, these settings will update set parameters in yaml tasks. Ensure 'do_sample=True' for non-greedy decoding!
+2026-07-18:12:36:59 INFO     [evaluator:239] Initializing local-chat-completions model, with arguments: {'model': 'student', 'base_url': 'http://127.0.0.1:8421/v1/chat/completions', 'num_concurrent': 48, 'tokenized_requests': False, 'max_retries': 3}
+2026-07-18:12:36:59 INFO     [models.api_models:179] Using max length 2048 - 1
+2026-07-18:12:36:59 INFO     [models.api_models:200] Using tokenizer None
+2026-07-18:12:37:04 INFO     [evaluator_utils:446] Selected tasks:
+2026-07-18:12:37:04 INFO     [evaluator_utils:480] Task: gsm8k_cot_zeroshot (gsm8k/gsm8k-cot-zeroshot.yaml)
+2026-07-18:12:37:04 INFO     [evaluator_utils:480] Task: ifeval (ifeval/ifeval.yaml)
+2026-07-18:12:37:04 INFO     [evaluator_utils:480] Task: minerva_math500 (minerva_math/minerva_math500.yaml)
+2026-07-18:12:37:04 INFO     [evaluator:314] gsm8k_cot_zeroshot: Using gen_kwargs: {'until': ['Q:', '</s>', '<|im_end|>'], 'do_sample': False, 'max_gen_toks': 1280}
+2026-07-18:12:37:04 INFO     [evaluator:314] minerva_math500: Using gen_kwargs: {'until': ['Problem:'], 'do_sample': False, 'temperature': 0.0, 'max_gen_toks': 1280}
+2026-07-18:12:37:04 INFO     [evaluator:314] ifeval: Using gen_kwargs: {'until': [], 'do_sample': False, 'temperature': 0.0, 'max_gen_toks': 1280}
+2026-07-18:12:37:04 INFO     [api.task:312] Building contexts for gsm8k_cot_zeroshot on rank 0...
+
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+2026-07-18:12:37:05 INFO     [api.task:312] Building contexts for minerva_math500 on rank 0...
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+2026-07-18:12:37:06 INFO     [api.task:312] Building contexts for ifeval on rank 0...
+
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+2026-07-18:12:37:06 INFO     [evaluator:585] Running generate_until requests
+2026-07-18:12:37:06 INFO     [models.api_models:747] Tokenized requests are disabled. Context + generation length is not checked.
+
+
+
+2026-07-18:12:42:34 INFO     [loggers.evaluation_tracker:247] Saving results aggregated
+2026-07-18:12:42:34 INFO     [loggers.evaluation_tracker:119] Saving per-task samples to outputs/evals/general_suite/healed/reap_keep50_s1225/student/*.jsonl
+local-chat-completions ({'model': 'student', 'base_url': 'http://127.0.0.1:8421/v1/chat/completions', 'num_concurrent': 48, 'tokenized_requests': False, 'max_retries': 3}), gen_kwargs: ({'max_gen_toks': 1280}), limit: None, num_fewshot: None, batch_size: 1
+|      Tasks       |Version|     Filter     |n-shot|        Metric         |   |Value |   |Stderr|
+|------------------|------:|----------------|-----:|-----------------------|---|-----:|---|------|
+|gsm8k_cot_zeroshot|      3|flexible-extract|     0|exact_match            |↑  |0.3184|±  |0.0128|
+|                  |       |strict-match    |     0|exact_match            |↑  |0.0106|±  |0.0028|
+|ifeval            |      4|none            |     0|inst_level_loose_acc   |↑  |0.6655|±  |   N/A|
+|                  |       |none            |     0|inst_level_strict_acc  |↑  |0.6355|±  |   N/A|
+|                  |       |none            |     0|prompt_level_loose_acc |↑  |0.5453|±  |0.0214|
+|                  |       |none            |     0|prompt_level_strict_acc|↑  |0.5102|±  |0.0215|
+|minerva_math500   |      3|none            |     4|exact_match            |↑  |0.0900|±  |0.0128|
+|                  |       |none            |     4|math_verify            |↑  |0.1440|±  |0.0157|
+
+2026-07-18T12:42:36-07:00 code pass [humaneval,mbpp] via /v1/completions (function-continuation)
+2026-07-18:12:42:43 INFO     [_cli.run:388] Selected Tasks: ['humaneval', 'mbpp']
+2026-07-18:12:42:44 INFO     [evaluator:214] Setting random seed to 0 | Setting numpy seed to 1234 | Setting torch manual seed to 1234 | Setting fewshot manual seed to 1234
+2026-07-18:12:42:44 INFO     [evaluator:239] Initializing local-completions model, with arguments: {'model': 'student', 'base_url': 'http://127.0.0.1:8421/v1/completions', 'tokenizer': 'outputs/healed/grid_general/reap_keep50_s1225/step0150', 'num_concurrent': 48, 'tokenized_requests': False, 'max_retries': 3}
+2026-07-18:12:42:44 INFO     [models.openai_completions:42] Remote tokenizer not supported. Using huggingface tokenizer backend.
+2026-07-18:12:42:44 INFO     [models.api_models:179] Using max length 2048 - 1
+2026-07-18:12:42:44 INFO     [models.api_models:200] Using tokenizer huggingface
+2026-07-18:12:42:52 INFO     [evaluator_utils:446] Selected tasks:
+2026-07-18:12:42:52 INFO     [evaluator_utils:480] Task: humaneval (humaneval/humaneval.yaml)
+2026-07-18:12:42:52 INFO     [evaluator_utils:480] Task: mbpp (mbpp/mbpp.yaml)
+2026-07-18:12:42:52 INFO     [evaluator:314] humaneval: Using gen_kwargs: {'until': ['\nclass', '\ndef', '\n#', '\nif', '\nprint'], 'max_gen_toks': 1024, 'do_sample': False}
+2026-07-18:12:42:52 INFO     [evaluator:314] mbpp: Using gen_kwargs: {'until': ['[DONE]'], 'do_sample': False}
+2026-07-18:12:42:52 INFO     [api.task:312] Building contexts for humaneval on rank 0...
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+2026-07-18:12:42:52 INFO     [api.task:312] Building contexts for mbpp on rank 0...
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+2026-07-18:12:42:55 INFO     [evaluator:585] Running generate_until requests
+2026-07-18:12:42:55 INFO     [models.api_models:747] Tokenized requests are disabled. Context + generation length is not checked.
+
+
+2026-07-18:12:46:31 INFO     [loggers.evaluation_tracker:247] Saving results aggregated
+2026-07-18:12:46:31 INFO     [loggers.evaluation_tracker:119] Saving per-task samples to outputs/evals/general_suite/healed/reap_keep50_s1225/student/*.jsonl
+local-completions ({'model': 'student', 'base_url': 'http://127.0.0.1:8421/v1/completions', 'tokenizer': 'outputs/healed/grid_general/reap_keep50_s1225/step0150', 'num_concurrent': 48, 'tokenized_requests': False, 'max_retries': 3}), gen_kwargs: ({}), limit: None, num_fewshot: None, batch_size: 1
+|  Tasks  |Version|  Filter   |n-shot| Metric  |   |Value |   |Stderr|
+|---------|------:|-----------|-----:|---------|---|-----:|---|-----:|
+|humaneval|      1|create_test|     0|pass@1   |↑  |0.2378|±  |0.0333|
+|mbpp     |      1|none       |     3|pass_at_1|↑  |0.2180|±  |0.0185|
+
+2026-07-18T12:46:32-07:00 lm_eval exit=0 -> outputs/evals/general_suite/healed/reap_keep50_s1225

healed/grid_general/reap_keep50_s1226.console.log

@@ -0,0 +1,217 @@
+/home/henry/Documents/PythonProjects/variable-reap/.venv/lib/python3.12/site-packages/megablocks/grouped_gemm_util.py:10: UserWarning: Grouped GEMM not available.
+  warnings.warn('Grouped GEMM not available.')
+wandb: [wandb.login()] Loaded credentials for https://api.wandb.ai from /home/henry/.netrc.
+wandb: Currently logged in as: hbfreed to https://api.wandb.ai. Use `wandb login --relogin` to force relogin
+wandb: setting up run o0n3wlu6
+wandb: Tracking run with wandb version 0.28.0
+wandb: Run data is saved locally in outputs/healed/grid_general/reap_keep50_s1226/wandb/run-20260718_100342-o0n3wlu6
+wandb: Run `wandb offline` to turn off syncing.
+wandb: Syncing run reap_keep50_s1226
+wandb: ⭐️ View project at https://wandb.ai/hbfreed/glean-general-grid
+wandb: 🚀 View run at https://wandb.ai/hbfreed/glean-general-grid/runs/o0n3wlu6
+
+58360 cached top-128 chat trajectories / 22,295,631 unique tokens | 185 steps/epoch | 150 total steps | student params 3.70B | teacher overlap=False
+{"step": 1, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.8803540725124379, "tokens": 120000, "cumulative_loss_tokens": 120000, "grad_norm": 9.625, "lr": 6e-06, "finish_rate": 0.945, "comp_len": 389.6, "t_data_s": 0.1, "t_rollout_s": 0.0, "t_step_s": 63.5, "frames": {"chat": 308}, "mem_gb": 15.9}
+The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.
+[eval step 1] sample: 'To solve the given problem, we need to understand the properties of an arithmetic sequence. \n\n1. **Under the given condition:**\n   \\[a_1 + 3a_8 + a_{15} = 120\\]\n\n2. **Objective:**\n   Find \\(2a_9 - a'
+{"step": 2, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.5245416656317811, "tokens": 120000, "cumulative_loss_tokens": 240000, "grad_norm": 7.5625, "lr": 9e-06, "finish_rate": 0.754, "comp_len": 568.7, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 44.9, "frames": {"chat": 211}, "mem_gb": 16.04}
+{"step": 3, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.7376569056948026, "tokens": 120000, "cumulative_loss_tokens": 360000, "grad_norm": 7.9375, "lr": 1.2e-05, "finish_rate": 0.915, "comp_len": 427.0, "t_data_s": 0.1, "t_rollout_s": 0.0, "t_step_s": 53.6, "frames": {"chat": 281}, "mem_gb": 16.04}
+{"step": 4, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.0425827999557058, "tokens": 120000, "cumulative_loss_tokens": 480000, "grad_norm": 8.0, "lr": 1.5e-05, "finish_rate": 0.997, "comp_len": 327.0, "t_data_s": 0.1, "t_rollout_s": 0.0, "t_step_s": 65.9, "frames": {"chat": 367}, "mem_gb": 15.59}
+{"step": 5, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.0140658880367874, "tokens": 120000, "cumulative_loss_tokens": 600000, "grad_norm": 7.8125, "lr": 1.8e-05, "finish_rate": 0.997, "comp_len": 384.6, "t_data_s": 0.1, "t_rollout_s": 0.0, "t_step_s": 58.5, "frames": {"chat": 312}, "mem_gb": 15.75}
+{"step": 6, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.7786726121418178, "tokens": 120000, "cumulative_loss_tokens": 720000, "grad_norm": 5.46875, "lr": 2.1e-05, "finish_rate": 0.919, "comp_len": 442.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 55.5, "frames": {"chat": 271}, "mem_gb": 16.06}
+{"step": 7, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.5371215664693465, "tokens": 120000, "cumulative_loss_tokens": 840000, "grad_norm": 3.390625, "lr": 2.4e-05, "finish_rate": 0.922, "comp_len": 446.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 52.8, "frames": {"chat": 269}, "mem_gb": 16.03}
+{"step": 8, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.8027348423482229, "tokens": 120000, "cumulative_loss_tokens": 960000, "grad_norm": 3.53125, "lr": 2.7000000000000002e-05, "finish_rate": 0.989, "comp_len": 340.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 62.0, "frames": {"chat": 352}, "mem_gb": 15.55}
+{"step": 9, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.38005650374094646, "tokens": 120000, "cumulative_loss_tokens": 1080000, "grad_norm": 1.515625, "lr": 3e-05, "finish_rate": 0.864, "comp_len": 493.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 50.0, "frames": {"chat": 243}, "mem_gb": 16.04}
+{"step": 10, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.4923551563156769, "tokens": 120000, "cumulative_loss_tokens": 1200000, "grad_norm": 1.5703125, "lr": 3e-05, "finish_rate": 0.952, "comp_len": 409.6, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 56.5, "frames": {"chat": 293}, "mem_gb": 15.96}
+[eval step 10] sample: 'To solve the problem, we need to understand the properties of an arithmetic sequence. In an arithmetic sequence, each term can be expressed as:\n\\[ a_n = a_1 + (n-1)d \\]\nwhere \\( a_1 \\) is the first te'
+{"step": 11, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.30476141565479337, "tokens": 120000, "cumulative_loss_tokens": 1320000, "grad_norm": 1.203125, "lr": 3e-05, "finish_rate": 0.872, "comp_len": 495.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 50.5, "frames": {"chat": 242}, "mem_gb": 16.04}
+{"step": 12, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.7059471693091095, "tokens": 120000, "cumulative_loss_tokens": 1440000, "grad_norm": 1.546875, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 339.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 66.8, "frames": {"chat": 353}, "mem_gb": 15.73}
+{"step": 13, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.6465659825122605, "tokens": 120000, "cumulative_loss_tokens": 1560000, "grad_norm": 1.375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 355.0, "t_data_s": 0.1, "t_rollout_s": 0.0, "t_step_s": 63.0, "frames": {"chat": 338}, "mem_gb": 15.74}
+{"step": 14, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.34863743216705817, "tokens": 120000, "cumulative_loss_tokens": 1680000, "grad_norm": 0.875, "lr": 3e-05, "finish_rate": 0.884, "comp_len": 463.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 54.2, "frames": {"chat": 259}, "mem_gb": 16.04}
+{"step": 15, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.5902533355700473, "tokens": 120000, "cumulative_loss_tokens": 1800000, "grad_norm": 1.125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 311.7, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 67.7, "frames": {"chat": 385}, "mem_gb": 15.59}
+{"step": 16, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.5514633375123144, "tokens": 120000, "cumulative_loss_tokens": 1920000, "grad_norm": 0.91015625, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 338.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 65.3, "frames": {"chat": 355}, "mem_gb": 15.75}
+{"step": 17, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.5126457059609393, "tokens": 120000, "cumulative_loss_tokens": 2040000, "grad_norm": 0.79296875, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 365.9, "t_data_s": 0.1, "t_rollout_s": 0.0, "t_step_s": 59.9, "frames": {"chat": 328}, "mem_gb": 15.66}
+{"step": 18, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.5420009215599547, "tokens": 120000, "cumulative_loss_tokens": 2160000, "grad_norm": 0.82421875, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 338.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 65.6, "frames": {"chat": 355}, "mem_gb": 15.81}
+{"step": 19, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.6401449499430756, "tokens": 120000, "cumulative_loss_tokens": 2280000, "grad_norm": 0.9140625, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 327.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 66.4, "frames": {"chat": 367}, "mem_gb": 15.68}
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+[eval step 20] sample: 'in an arithmetic sequence, the terms can be expressed as follows:\n\na_n = a_1 + (n-1)d, where a_1 is the first term and d is the common difference.\n\ngiven: a_1 + 3a_8 + a_15 = 120.'
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+[eval step 30] sample: 'in an arithmetic sequence, the terms can be expressed as follows:\n\na_n = a_1 + (n-1)d, where a_1 is the first term and d is the common difference.\n\ngiven: a_1 + 3a_8 + a_15 = 120.'
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+[eval step 40] sample: 'in an arithmetic sequence, the terms can be expressed as follows: a_n = a_1 + (n-1)d, where a_1 is the first term and d is the common difference. given the equation a_1 + 3a_8 + a_15 = 120, we need to'
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+[eval step 50] sample: 'in an arithmetic sequence, the terms can be represented as follows: a_n = a_1 + (n-1)d, where a_1 is the first term and d is the common difference. given the equation a_1 + 3a_8 + a_15 = 120, we need '
+checkpoint snapshot queued -> outputs/healed/grid_general/reap_keep50_s1226/step0050
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+[eval step 70] sample: 'in an arithmetic sequence, the nth term can be expressed as a_n = a_1 + (n-1)d, where a_1 is the first term and d is the common difference. given the equation a_1 + 3a_8 + a_15 = 120, we need to'
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+[eval step 80] sample: 'in an arithmetic sequence, each term is the sum of the previous term and a constant difference, denoted as a_n = a_1 + (n-1)d, where a_1 is the first term and d is the common difference.\n\ngiven: a_1 +'
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+[eval step 90] sample: 'in an arithmetic sequence, each term is obtained by adding a constant difference to the previous term. let the first term be $a_1$ and the common difference be $d$. then, the terms of the sequence can'
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+[eval step 100] sample: 'in an arithmetic sequence, each term is given by the formula \\( a_n = a_1 + (n-1)d \\), where \\( a_1 \\) is the first term and \\( d \\) is the common difference.\n\nwe are given the equation \\( a_1 + 3a'
+checkpoint snapshot queued -> outputs/healed/grid_general/reap_keep50_s1226/step0100
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+[eval step 110] sample: 'in an arithmetic sequence, each term is given by the formula \\( a_n = a_1 + (n-1)d \\), where \\( a_1 \\) is the first term and \\( d \\) is the common difference.\n\nwe are given the equation \\( a_1 + 3a'
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+[eval step 120] sample: 'in an arithmetic sequence, each term is obtained by adding a constant difference to the previous term. let the first term be $a_1$ and the common difference be $d$. the $n$-th term of the sequence can'
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+[eval step 130] sample: 'in an arithmetic sequence, each term can be expressed as $a_n = a_1 + (n-1)d$, where $a_1$ is the first term and $d$ is the common difference. given the equation $a_1 + 3a_8 + a_{15} ='
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+[eval step 140] sample: "to solve the problem, let's first understand the properties of an arithmetic sequence. in an arithmetic sequence, each term is the previous term plus a constant difference, denoted as $d$. the $n$-th "
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+{"step": 148, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.4000309775177389, "tokens": 120000, "cumulative_loss_tokens": 17760000, "grad_norm": 0.58203125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 316.6, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 68.4, "frames": {"chat": 379}, "mem_gb": 15.64}
+{"step": 149, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.3480989918136969, "tokens": 120000, "cumulative_loss_tokens": 17880000, "grad_norm": 0.5625, "lr": 3e-05, "finish_rate": 0.928, "comp_len": 390.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 60.4, "frames": {"chat": 307}, "mem_gb": 15.98}
+{"step": 150, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.24423585990754268, "tokens": 120000, "cumulative_loss_tokens": 18000000, "grad_norm": 0.458984375, "lr": 3e-05, "finish_rate": 0.942, "comp_len": 409.6, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 58.4, "frames": {"chat": 293}, "mem_gb": 16.02}
+[eval step 150] sample: "to solve the problem, let's first understand the properties of an arithmetic sequence. in an arithmetic sequence, each term is the sum of the previous two terms. if \\(a_1\\) is the first term and \\(d\\)"
+checkpoint snapshot queued -> outputs/healed/grid_general/reap_keep50_s1226/step0150
+wandb: updating run metadata
+wandb: uploading output.log; uploading wandb-summary.json; uploading config.yaml
+wandb: uploading data
+wandb: 
+wandb: Run history:
+wandb:               comp_len █▄▅▆▄▂▁▂▂▂▃▃▄▁▄▃▃▆▃▄▇▃▂▂▂▂▃▂▆▂▃▁▂▄▂▄▄▄▂▄
+wandb: cumulative_loss_tokens ▁▁▁▁▁▂▂▂▂▂▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇████
+wandb:                  epoch ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
+wandb:            finish_rate ██▁██▆█▃█▇██▆██▄▇█▃██▇█▁▇█▆▇▃██▆▇▇█▆▅███
+wandb:        forward_topk_kl ▇▅█▃▅▃▆▅▅▅▆▃▆▃▅▃▃▃▁▄▁▃▄▅▁▄▃▁▅▄▂▂▄▄▅▃▂▂▁▃
+wandb:              grad_norm █▆▇▇▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
+wandb:                     lr ▁▅▆█████████████████████████████████████
+wandb:                 mem_gb █▁█▇█▂▅█▅▄▅▇▅▇▇▅▆▇▆▆▂▇▆▄▅▃▃▂▆▅▇▃▇▅▅▁▃▆▂▅
+wandb:                   step ▁▁▁▁▁▂▂▂▂▂▃▃▃▃▃▄▄▄▅▅▅▅▅▆▆▆▆▆▆▆▇▇▇▇▇▇▇▇██
+wandb:               t_data_s █▁▁▁█▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
+wandb:                     +3 ...
+wandb: 
+wandb: Run summary:
+wandb:               comp_len 409.6
+wandb: cumulative_loss_tokens 18000000
+wandb:                  epoch 0
+wandb:            finish_rate 0.942
+wandb:        forward_topk_kl 0.24424
+wandb:              grad_norm 0.45898
+wandb:                     lr 3e-05
+wandb:                 mem_gb 16.02
+wandb:                   step 150
+wandb:               t_data_s 0
+wandb:                     +4 ...
+wandb: 
+wandb: 🚀 View run reap_keep50_s1226 at: https://wandb.ai/hbfreed/glean-general-grid/runs/o0n3wlu6
+wandb: ⭐️ View project at: https://wandb.ai/hbfreed/glean-general-grid
+wandb: Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)
+wandb: Find logs at: outputs/healed/grid_general/reap_keep50_s1226/wandb/run-20260718_100342-o0n3wlu6/logs

healed/grid_general/reap_keep75_s1224.console.log

@@ -0,0 +1,216 @@
+/home/henry/Documents/PythonProjects/variable-reap/.venv/lib/python3.12/site-packages/megablocks/grouped_gemm_util.py:10: UserWarning: Grouped GEMM not available.
+  warnings.warn('Grouped GEMM not available.')
+wandb: [wandb.login()] Loaded credentials for https://api.wandb.ai from /home/henry/.netrc.
+wandb: Currently logged in as: hbfreed to https://api.wandb.ai. Use `wandb login --relogin` to force relogin
+wandb: Tracking run with wandb version 0.28.0
+wandb: Run data is saved locally in outputs/healed/grid_general/reap_keep75_s1224/wandb/run-20260718_191947-wgcis861
+wandb: Run `wandb offline` to turn off syncing.
+wandb: Syncing run reap_keep75_s1224
+wandb: ⭐️ View project at https://wandb.ai/hbfreed/glean-general-grid
+wandb: 🚀 View run at https://wandb.ai/hbfreed/glean-general-grid/runs/wgcis861
+
+58360 cached top-128 chat trajectories / 22,295,631 unique tokens | 185 steps/epoch | 150 total steps | student params 5.31B | teacher overlap=False
+{"step": 1, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.1873658725475582, "tokens": 120000, "cumulative_loss_tokens": 120000, "grad_norm": 2.484375, "lr": 6e-06, "finish_rate": 0.869, "comp_len": 463.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 61.9, "frames": {"chat": 259}, "mem_gb": 21.93}
+The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.
+[eval step 1] sample: 'To find the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns. We can do this by performing row reduction (or Gaussian elimination) to transform the mat'
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+[eval step 10] sample: 'To compute the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns. A matrix is said to be full rank if it has rank equal to the minimum of the number of '
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+[eval step 20] sample: 'To determine the rank of a given 4x4 matrix, we need to find the maximum number of linearly independent row vectors. This involves reducing the matrix to row-echelon form (REF) and identifying the lea'
+{"step": 21, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.12363467963938601, "tokens": 120000, "cumulative_loss_tokens": 2520000, "grad_norm": 0.48828125, "lr": 3e-05, "finish_rate": 0.963, "comp_len": 402.7, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 62.1, "frames": {"chat": 298}, "mem_gb": 21.9}
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+[eval step 30] sample: "To find the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns. Here's how we can do it step-by-step:\n\n1. **Row Reduction to Row Echelon Form (REF)**:\n  "
+{"step": 31, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.04471613788570588, "tokens": 120000, "cumulative_loss_tokens": 3720000, "grad_norm": 0.466796875, "lr": 3e-05, "finish_rate": 0.891, "comp_len": 521.7, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 50.0, "frames": {"chat": 230}, "mem_gb": 21.93}
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+{"step": 39, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.09410194004089571, "tokens": 120000, "cumulative_loss_tokens": 4680000, "grad_norm": 0.408203125, "lr": 3e-05, "finish_rate": 0.959, "comp_len": 406.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 58.9, "frames": {"chat": 295}, "mem_gb": 21.91}
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+[eval step 40] sample: "To compute the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. \n\nLet's represent the given matrix \\( A \\) as:\n\\[ A = \\begin{pmatrix}\n12"
+{"step": 41, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.10687232487322762, "tokens": 120000, "cumulative_loss_tokens": 4920000, "grad_norm": 0.47265625, "lr": 3e-05, "finish_rate": 0.905, "comp_len": 456.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 58.6, "frames": {"chat": 263}, "mem_gb": 22.04}
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+{"step": 48, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.1499260142687553, "tokens": 120000, "cumulative_loss_tokens": 5760000, "grad_norm": 0.50390625, "lr": 3e-05, "finish_rate": 0.974, "comp_len": 385.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 65.6, "frames": {"chat": 311}, "mem_gb": 21.79}
+{"step": 49, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.1233547725815326, "tokens": 120000, "cumulative_loss_tokens": 5880000, "grad_norm": 0.4375, "lr": 3e-05, "finish_rate": 0.876, "comp_len": 463.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 55.7, "frames": {"chat": 259}, "mem_gb": 22.1}
+{"step": 50, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.1450087027960457, "tokens": 120000, "cumulative_loss_tokens": 6000000, "grad_norm": 0.5234375, "lr": 3e-05, "finish_rate": 0.944, "comp_len": 393.4, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 65.1, "frames": {"chat": 305}, "mem_gb": 22.08}
+[eval step 50] sample: "To compute the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. \n\nLet's break down the given 4x4 matrix:\n\n\\[\n\\begin{bmatrix}\n12 & -16 & "
+checkpoint snapshot queued -> outputs/healed/grid_general/reap_keep75_s1224/step0050
+{"step": 51, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.12619501014033643, "tokens": 120000, "cumulative_loss_tokens": 6120000, "grad_norm": 0.478515625, "lr": 3e-05, "finish_rate": 0.946, "comp_len": 402.7, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 63.6, "frames": {"chat": 298}, "mem_gb": 22.06}
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+[eval step 60] sample: 'To compute the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns. A matrix is rank-deficient if it has fewer linearly independent rows or columns than i'
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+[eval step 70] sample: "To compute the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns. Here's how we can do it step-by-step:\n\n1. **Row Reduction (Gaussian Elimination):**\n  "
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+[eval step 80] sample: 'To find the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns. A matrix is said to be of rank \\( r \\) if it has \\( r \\) linearly independent rows or col'
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+[eval step 90] sample: 'To compute the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. A matrix is said to be of full rank if all its rows (or columns) are lin'
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+[eval step 100] sample: "To find the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. \n\nLet's represent the given 4x4 matrix:\n\n\\[\n\\begin{bmatrix}\n12 & -16 & 4 & "
+checkpoint snapshot queued -> outputs/healed/grid_general/reap_keep75_s1224/step0100
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+[eval step 110] sample: 'To find the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. A matrix is said to be of full rank if all its rows (or columns) are linear'
+{"step": 111, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.1750133204760806, "tokens": 120000, "cumulative_loss_tokens": 13320000, "grad_norm": 0.515625, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 327.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 73.1, "frames": {"chat": 366}, "mem_gb": 21.7}
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+[eval step 120] sample: "To find the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns. \n\nLet's represent the given matrix:\n\n\\[\n\\begin{bmatrix}\n12 & -16 & 4 & 16 \\\\\n-9 & 11 & -1"
+{"step": 121, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.11816860233771925, "tokens": 120000, "cumulative_loss_tokens": 14520000, "grad_norm": 0.431640625, "lr": 3e-05, "finish_rate": 0.956, "comp_len": 408.2, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 61.9, "frames": {"chat": 294}, "mem_gb": 22.14}
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+{"step": 125, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.10110068176521454, "tokens": 120000, "cumulative_loss_tokens": 15000000, "grad_norm": 1.484375, "lr": 3e-05, "finish_rate": 0.968, "comp_len": 387.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 67.4, "frames": {"chat": 310}, "mem_gb": 21.76}
+{"step": 126, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.1936153787978304, "tokens": 120000, "cumulative_loss_tokens": 15120000, "grad_norm": 0.546875, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 335.2, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 76.7, "frames": {"chat": 358}, "mem_gb": 21.78}
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+{"step": 128, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.06792385750228229, "tokens": 120000, "cumulative_loss_tokens": 15360000, "grad_norm": 0.353515625, "lr": 3e-05, "finish_rate": 0.9, "comp_len": 446.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 61.4, "frames": {"chat": 269}, "mem_gb": 22.09}
+{"step": 129, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.12652876620485137, "tokens": 120000, "cumulative_loss_tokens": 15480000, "grad_norm": 0.486328125, "lr": 3e-05, "finish_rate": 0.974, "comp_len": 387.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 70.9, "frames": {"chat": 310}, "mem_gb": 21.95}
+{"step": 130, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.08150330542761366, "tokens": 120000, "cumulative_loss_tokens": 15600000, "grad_norm": 0.423828125, "lr": 3e-05, "finish_rate": 0.834, "comp_len": 463.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 62.3, "frames": {"chat": 259}, "mem_gb": 22.12}
+[eval step 130] sample: "To find the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns. \n\nLet's represent the given matrix:\n\n\\[\n\\begin{bmatrix}\n12 & -16 & 4 & 16 \\\\\n-9 & 11 & -1"
+{"step": 131, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.07982180994842202, "tokens": 120000, "cumulative_loss_tokens": 15720000, "grad_norm": 0.412109375, "lr": 3e-05, "finish_rate": 0.868, "comp_len": 452.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 60.5, "frames": {"chat": 265}, "mem_gb": 22.1}
+{"step": 132, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.17891147567131557, "tokens": 120000, "cumulative_loss_tokens": 15840000, "grad_norm": 0.53125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 356.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 73.7, "frames": {"chat": 337}, "mem_gb": 21.65}
+{"step": 133, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.08981655380092561, "tokens": 120000, "cumulative_loss_tokens": 15960000, "grad_norm": 0.4140625, "lr": 3e-05, "finish_rate": 0.972, "comp_len": 413.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 65.8, "frames": {"chat": 290}, "mem_gb": 21.91}
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+{"step": 135, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.09113996521788649, "tokens": 120000, "cumulative_loss_tokens": 16200000, "grad_norm": 0.392578125, "lr": 3e-05, "finish_rate": 0.928, "comp_len": 412.4, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 65.0, "frames": {"chat": 291}, "mem_gb": 21.94}
+{"step": 136, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.1560885852477203, "tokens": 120000, "cumulative_loss_tokens": 16320000, "grad_norm": 0.51953125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 350.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 74.5, "frames": {"chat": 342}, "mem_gb": 21.81}
+{"step": 137, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.19789823096484566, "tokens": 120000, "cumulative_loss_tokens": 16440000, "grad_norm": 0.54296875, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 355.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 71.9, "frames": {"chat": 338}, "mem_gb": 21.75}
+{"step": 138, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.1761994122691918, "tokens": 120000, "cumulative_loss_tokens": 16560000, "grad_norm": 0.51171875, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 337.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 78.7, "frames": {"chat": 356}, "mem_gb": 21.71}
+{"step": 139, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.07923250444449174, "tokens": 120000, "cumulative_loss_tokens": 16680000, "grad_norm": 0.388671875, "lr": 3e-05, "finish_rate": 0.86, "comp_len": 452.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 67.1, "frames": {"chat": 265}, "mem_gb": 22.09}
+{"step": 140, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.1345104466229522, "tokens": 120000, "cumulative_loss_tokens": 16800000, "grad_norm": 0.474609375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 339.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 77.2, "frames": {"chat": 353}, "mem_gb": 21.81}
+[eval step 140] sample: 'To compute the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. \n\nGiven the matrix:\n\\[\n\\begin{bmatrix}\n12 & -16 & 4 & 16 \\\\\n-9 & 11 & -1'
+{"step": 141, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.13114069964168593, "tokens": 120000, "cumulative_loss_tokens": 16920000, "grad_norm": 0.46484375, "lr": 3e-05, "finish_rate": 0.949, "comp_len": 379.7, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 70.4, "frames": {"chat": 316}, "mem_gb": 22.0}
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+{"step": 143, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.1138644701910826, "tokens": 120000, "cumulative_loss_tokens": 17160000, "grad_norm": 0.443359375, "lr": 3e-05, "finish_rate": 0.982, "comp_len": 362.5, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 73.5, "frames": {"chat": 331}, "mem_gb": 21.78}
+{"step": 144, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.10702105035733742, "tokens": 120000, "cumulative_loss_tokens": 17280000, "grad_norm": 0.40625, "lr": 3e-05, "finish_rate": 0.972, "comp_len": 375.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 69.2, "frames": {"chat": 320}, "mem_gb": 22.1}
+{"step": 145, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.10355366102789218, "tokens": 120000, "cumulative_loss_tokens": 17400000, "grad_norm": 0.37890625, "lr": 3e-05, "finish_rate": 0.941, "comp_len": 441.2, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 60.1, "frames": {"chat": 272}, "mem_gb": 21.83}
+{"step": 146, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.19566748774093576, "tokens": 120000, "cumulative_loss_tokens": 17520000, "grad_norm": 0.58203125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 326.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 79.4, "frames": {"chat": 368}, "mem_gb": 21.75}
+{"step": 147, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.15470146789173594, "tokens": 120000, "cumulative_loss_tokens": 17640000, "grad_norm": 0.4921875, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 365.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 68.0, "frames": {"chat": 328}, "mem_gb": 21.78}
+{"step": 148, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.1427797764064744, "tokens": 120000, "cumulative_loss_tokens": 17760000, "grad_norm": 0.47265625, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 368.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 70.2, "frames": {"chat": 326}, "mem_gb": 21.99}
+{"step": 149, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.1717408910106557, "tokens": 120000, "cumulative_loss_tokens": 17880000, "grad_norm": 0.494140625, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 329.7, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 72.8, "frames": {"chat": 364}, "mem_gb": 21.93}
+{"step": 150, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.11370671968573394, "tokens": 120000, "cumulative_loss_tokens": 18000000, "grad_norm": 0.427734375, "lr": 3e-05, "finish_rate": 0.97, "comp_len": 405.4, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 59.8, "frames": {"chat": 296}, "mem_gb": 21.98}
+[eval step 150] sample: 'To compute the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. \n\nGiven the matrix:\n\\[\n\\begin{bmatrix}\n12 & -16 & 4 & 16 \\\\\n-9 & 11 & -1'
+checkpoint snapshot queued -> outputs/healed/grid_general/reap_keep75_s1224/step0150
+wandb: updating run metadata
+wandb: uploading config.yaml; uploading output.log; uploading wandb-summary.json
+wandb: uploading summary, console lines 171-171
+wandb: 
+wandb: Run history:
+wandb:               comp_len ▂▇▃▂▂▁▂▁▃▆▄▄▃▂▄▃▄▃▅▅▃▄▂▃▄▅▆█▃▅▂▂▂▆▅▂▄▃▂▄
+wandb: cumulative_loss_tokens ▁▁▁▂▂▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▅▅▅▅▆▆▆▆▆▆▇▇▇▇▇▇████
+wandb:                  epoch ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
+wandb:            finish_rate ▁▆▃▂█▁██▄▃▅▁▄▇▇█▇▆▇▇▄▆▇█▇████▇█▅███▅███▇
+wandb:        forward_topk_kl ▆█▃▇█▆▆▅▆▁▂▃▄▅▅▃▅▅▅▁▆▅▃▅▃▅▅▄▂▁▃▅▄▄▂▂▃▆▃▄
+wandb:              grad_norm █▅▅▃▂▂▁▂▂▂▂▂▂▂▂▂▁▂▁▁▁▁▁▁▂▁▁▁▂▂▂▁▂▂▁▁▂▁▁▁
+wandb:                     lr ▁▂▅█████████████████████████████████████
+wandb:                 mem_gb ▄█▁█▇█▇█▆▄▇▄▃▄▆▆▆▄▁▆▆▃▄█▆▄██▆▂▅▇█▅▄▃▃▄█▆
+wandb:                   step ▁▁▁▁▁▂▂▃▃▃▄▄▄▄▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇▇▇██
+wandb:               t_data_s ▁▁█▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
+wandb:                     +3 ...
+wandb: 
+wandb: Run summary:
+wandb:               comp_len 405.4
+wandb: cumulative_loss_tokens 18000000
+wandb:                  epoch 0
+wandb:            finish_rate 0.97
+wandb:        forward_topk_kl 0.11371
+wandb:              grad_norm 0.42773
+wandb:                     lr 3e-05
+wandb:                 mem_gb 21.98
+wandb:                   step 150
+wandb:               t_data_s 0
+wandb:                     +4 ...
+wandb: 
+wandb: 🚀 View run reap_keep75_s1224 at: https://wandb.ai/hbfreed/glean-general-grid/runs/wgcis861
+wandb: ⭐️ View project at: https://wandb.ai/hbfreed/glean-general-grid
+wandb: Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)
+wandb: Find logs at: outputs/healed/grid_general/reap_keep75_s1224/wandb/run-20260718_191947-wgcis861/logs

healed/grid_general/reap_keep75_s1225.console.log

@@ -0,0 +1,392 @@
+/home/henry/Documents/PythonProjects/variable-reap/.venv/lib/python3.12/site-packages/megablocks/grouped_gemm_util.py:10: UserWarning: Grouped GEMM not available.
+  warnings.warn('Grouped GEMM not available.')
+wandb: [wandb.login()] Loaded credentials for https://api.wandb.ai from /home/henry/.netrc.
+wandb: Currently logged in as: hbfreed to https://api.wandb.ai. Use `wandb login --relogin` to force relogin
+wandb: setting up run eyetgo3a
+wandb: Tracking run with wandb version 0.28.0
+wandb: Run data is saved locally in outputs/healed/grid_general/reap_keep75_s1225/wandb/run-20260718_224025-eyetgo3a
+wandb: Run `wandb offline` to turn off syncing.
+wandb: Syncing run reap_keep75_s1225
+wandb: ⭐️ View project at https://wandb.ai/hbfreed/glean-general-grid
+wandb: 🚀 View run at https://wandb.ai/hbfreed/glean-general-grid/runs/eyetgo3a
+
+58360 cached top-128 chat trajectories / 22,295,631 unique tokens | 185 steps/epoch | 150 total steps | student params 5.31B | teacher overlap=False
+{"step": 1, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.32691152300952625, "tokens": 120000, "cumulative_loss_tokens": 120000, "grad_norm": 3.09375, "lr": 6e-06, "finish_rate": 0.997, "comp_len": 362.5, "t_data_s": 0.1, "t_rollout_s": 0.0, "t_step_s": 72.9, "frames": {"chat": 331}, "mem_gb": 21.8}
+The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.
+[eval step 1] sample: 'To determine how many months it will take for Jamie to reach the savings goal of $1,000,000, we need to consider both the monthly savings and the interest earned.\n\n1. **Initial Savings:**\n   - Jamie s'
+{"step": 2, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.3199042237383003, "tokens": 120000, "cumulative_loss_tokens": 240000, "grad_norm": 3.28125, "lr": 9e-06, "finish_rate": 0.997, "comp_len": 350.9, "t_data_s": 0.1, "t_rollout_s": 0.0, "t_step_s": 66.3, "frames": {"chat": 342}, "mem_gb": 21.72}
+{"step": 3, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.3276988255869287, "tokens": 120000, "cumulative_loss_tokens": 360000, "grad_norm": 3.171875, "lr": 1.2e-05, "finish_rate": 0.997, "comp_len": 335.2, "t_data_s": 0.1, "t_rollout_s": 0.0, "t_step_s": 68.1, "frames": {"chat": 358}, "mem_gb": 21.76}
+{"step": 4, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.2653813190323611, "tokens": 120000, "cumulative_loss_tokens": 480000, "grad_norm": 2.359375, "lr": 1.5e-05, "finish_rate": 0.997, "comp_len": 348.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 70.5, "frames": {"chat": 344}, "mem_gb": 21.84}
+{"step": 5, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.16577702154777943, "tokens": 120000, "cumulative_loss_tokens": 600000, "grad_norm": 1.421875, "lr": 1.8e-05, "finish_rate": 0.861, "comp_len": 449.4, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 58.0, "frames": {"chat": 267}, "mem_gb": 22.16}
+{"step": 6, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.07628021648065186, "tokens": 120000, "cumulative_loss_tokens": 720000, "grad_norm": 0.87109375, "lr": 2.1e-05, "finish_rate": 0.835, "comp_len": 483.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 50.1, "frames": {"chat": 248}, "mem_gb": 21.99}
+{"step": 7, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.22926093437794284, "tokens": 120000, "cumulative_loss_tokens": 840000, "grad_norm": 1.328125, "lr": 2.4e-05, "finish_rate": 0.979, "comp_len": 364.7, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 63.4, "frames": {"chat": 329}, "mem_gb": 21.98}
+{"step": 8, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.23873148627749954, "tokens": 120000, "cumulative_loss_tokens": 960000, "grad_norm": 1.265625, "lr": 2.7000000000000002e-05, "finish_rate": 0.997, "comp_len": 354.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 68.0, "frames": {"chat": 339}, "mem_gb": 21.94}
+{"step": 9, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.24488673688516333, "tokens": 120000, "cumulative_loss_tokens": 1080000, "grad_norm": 1.1015625, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 339.0, "t_data_s": 0.1, "t_rollout_s": 0.0, "t_step_s": 68.8, "frames": {"chat": 354}, "mem_gb": 21.94}
+{"step": 10, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.14254065898017337, "tokens": 120000, "cumulative_loss_tokens": 1200000, "grad_norm": 0.66796875, "lr": 3e-05, "finish_rate": 0.943, "comp_len": 430.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 54.8, "frames": {"chat": 279}, "mem_gb": 22.07}
+[eval step 10] sample: 'To determine how many months it will take for Jamie to reach their goal of $1,000,000, we need to consider both their monthly savings and the interest earned from their investment account.\n\n**Step 1: '
+{"step": 11, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.17979874870991333, "tokens": 120000, "cumulative_loss_tokens": 1320000, "grad_norm": 0.72265625, "lr": 3e-05, "finish_rate": 0.959, "comp_len": 377.4, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 62.9, "frames": {"chat": 318}, "mem_gb": 22.0}
+{"step": 12, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.12242380664556597, "tokens": 120000, "cumulative_loss_tokens": 1440000, "grad_norm": 0.5859375, "lr": 3e-05, "finish_rate": 0.875, "comp_len": 452.8, "t_data_s": 0.1, "t_rollout_s": 0.0, "t_step_s": 55.3, "frames": {"chat": 265}, "mem_gb": 22.09}
+{"step": 13, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.10949978814693168, "tokens": 120000, "cumulative_loss_tokens": 1560000, "grad_norm": 0.546875, "lr": 3e-05, "finish_rate": 0.85, "comp_len": 472.4, "t_data_s": 0.1, "t_rollout_s": 0.0, "t_step_s": 54.2, "frames": {"chat": 254}, "mem_gb": 22.03}
+{"step": 14, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.16167643672154905, "tokens": 120000, "cumulative_loss_tokens": 1680000, "grad_norm": 0.609375, "lr": 3e-05, "finish_rate": 0.962, "comp_len": 381.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 65.4, "frames": {"chat": 315}, "mem_gb": 22.06}
+{"step": 15, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.23289925298504532, "tokens": 120000, "cumulative_loss_tokens": 1800000, "grad_norm": 0.69140625, "lr": 3e-05, "finish_rate": 0.977, "comp_len": 400.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 60.7, "frames": {"chat": 300}, "mem_gb": 21.86}
+{"step": 16, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.14982427687635647, "tokens": 120000, "cumulative_loss_tokens": 1920000, "grad_norm": 0.625, "lr": 3e-05, "finish_rate": 0.924, "comp_len": 434.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 60.9, "frames": {"chat": 276}, "mem_gb": 22.06}
+{"step": 17, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.23245559696989754, "tokens": 120000, "cumulative_loss_tokens": 2040000, "grad_norm": 0.67578125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 337.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 73.0, "frames": {"chat": 356}, "mem_gb": 22.0}
+{"step": 18, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.19985329620527725, "tokens": 120000, "cumulative_loss_tokens": 2160000, "grad_norm": 0.609375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 394.7, "t_data_s": 0.1, "t_rollout_s": 0.0, "t_step_s": 61.2, "frames": {"chat": 304}, "mem_gb": 21.81}
+{"step": 19, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.20904093375516433, "tokens": 120000, "cumulative_loss_tokens": 2280000, "grad_norm": 0.625, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 320.0, "t_data_s": 0.1, "t_rollout_s": 0.0, "t_step_s": 73.0, "frames": {"chat": 375}, "mem_gb": 21.73}
+{"step": 20, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.11546105957857022, "tokens": 120000, "cumulative_loss_tokens": 2400000, "grad_norm": 0.50390625, "lr": 3e-05, "finish_rate": 0.939, "comp_len": 408.2, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 60.4, "frames": {"chat": 294}, "mem_gb": 22.09}
+[eval step 20] sample: 'To determine how many months it will take for Jamie to reach their savings goal of $1,000,000, we need to consider both their monthly savings and the interest earned from their investment account.\n\n**'
+{"step": 21, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.19831337091407428, "tokens": 120000, "cumulative_loss_tokens": 2520000, "grad_norm": 0.56640625, "lr": 3e-05, "finish_rate": 0.98, "comp_len": 401.3, "t_data_s": 0.1, "t_rollout_s": 0.0, "t_step_s": 65.2, "frames": {"chat": 299}, "mem_gb": 22.1}
+{"step": 22, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.14949486692990177, "tokens": 120000, "cumulative_loss_tokens": 2640000, "grad_norm": 0.54296875, "lr": 3e-05, "finish_rate": 0.98, "comp_len": 393.4, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 63.7, "frames": {"chat": 305}, "mem_gb": 21.88}
+{"step": 23, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.09090689937220886, "tokens": 120000, "cumulative_loss_tokens": 2760000, "grad_norm": 0.427734375, "lr": 3e-05, "finish_rate": 0.931, "comp_len": 458.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 54.8, "frames": {"chat": 262}, "mem_gb": 21.93}
+{"step": 24, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.12780333354215448, "tokens": 120000, "cumulative_loss_tokens": 2880000, "grad_norm": 0.51171875, "lr": 3e-05, "finish_rate": 0.95, "comp_len": 396.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 58.0, "frames": {"chat": 303}, "mem_gb": 21.99}
+{"step": 25, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.21190356762927645, "tokens": 120000, "cumulative_loss_tokens": 3000000, "grad_norm": 0.6328125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 315.8, "t_data_s": 0.1, "t_rollout_s": 0.0, "t_step_s": 70.9, "frames": {"chat": 380}, "mem_gb": 21.87}
+{"step": 26, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.09783557223896495, "tokens": 120000, "cumulative_loss_tokens": 3120000, "grad_norm": 0.478515625, "lr": 3e-05, "finish_rate": 0.95, "comp_len": 425.5, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 57.9, "frames": {"chat": 282}, "mem_gb": 21.83}
+{"step": 27, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.18497481243492414, "tokens": 120000, "cumulative_loss_tokens": 3240000, "grad_norm": 0.5703125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 331.5, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 72.7, "frames": {"chat": 362}, "mem_gb": 21.84}
+{"step": 28, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.1856384761373202, "tokens": 120000, "cumulative_loss_tokens": 3360000, "grad_norm": 0.609375, "lr": 3e-05, "finish_rate": 0.967, "comp_len": 390.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 62.1, "frames": {"chat": 307}, "mem_gb": 22.08}
+{"step": 29, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.12597426218503777, "tokens": 120000, "cumulative_loss_tokens": 3480000, "grad_norm": 0.51953125, "lr": 3e-05, "finish_rate": 0.926, "comp_len": 425.5, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 56.4, "frames": {"chat": 282}, "mem_gb": 21.95}
+{"step": 30, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.1817345579418664, "tokens": 120000, "cumulative_loss_tokens": 3600000, "grad_norm": 0.56640625, "lr": 3e-05, "finish_rate": 0.979, "comp_len": 356.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 65.0, "frames": {"chat": 337}, "mem_gb": 21.98}
+[eval step 30] sample: 'To determine how many months it will take for Jamie to reach their savings goal of $1,000,000, we need to consider both their monthly savings and the interest earned from their investment account.\n\n**'
+{"step": 31, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.11014159770294403, "tokens": 120000, "cumulative_loss_tokens": 3720000, "grad_norm": 0.45703125, "lr": 3e-05, "finish_rate": 0.891, "comp_len": 436.4, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 57.5, "frames": {"chat": 275}, "mem_gb": 22.04}
+{"step": 32, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.18914651696265986, "tokens": 120000, "cumulative_loss_tokens": 3840000, "grad_norm": 0.5625, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 377.4, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 63.7, "frames": {"chat": 318}, "mem_gb": 21.85}
+{"step": 33, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.23652538478126128, "tokens": 120000, "cumulative_loss_tokens": 3960000, "grad_norm": 0.6640625, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 311.7, "t_data_s": 0.1, "t_rollout_s": 0.0, "t_step_s": 70.6, "frames": {"chat": 385}, "mem_gb": 21.87}
+{"step": 34, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.23049329001403723, "tokens": 120000, "cumulative_loss_tokens": 4080000, "grad_norm": 0.6171875, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 379.7, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 62.6, "frames": {"chat": 316}, "mem_gb": 21.83}
+{"step": 35, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.09107681101333971, "tokens": 120000, "cumulative_loss_tokens": 4200000, "grad_norm": 0.470703125, "lr": 3e-05, "finish_rate": 0.887, "comp_len": 451.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 60.4, "frames": {"chat": 266}, "mem_gb": 22.12}
+{"step": 36, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.19664210549537092, "tokens": 120000, "cumulative_loss_tokens": 4320000, "grad_norm": 0.5625, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 323.5, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 74.5, "frames": {"chat": 371}, "mem_gb": 21.85}
+{"step": 37, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.10261952416292236, "tokens": 120000, "cumulative_loss_tokens": 4440000, "grad_norm": 0.43359375, "lr": 3e-05, "finish_rate": 0.878, "comp_len": 458.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 58.6, "frames": {"chat": 262}, "mem_gb": 22.06}
+{"step": 38, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.21949122376873467, "tokens": 120000, "cumulative_loss_tokens": 4560000, "grad_norm": 0.5859375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 344.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 71.2, "frames": {"chat": 348}, "mem_gb": 21.88}
+{"step": 39, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.04237283055443938, "tokens": 120000, "cumulative_loss_tokens": 4680000, "grad_norm": 0.408203125, "lr": 3e-05, "finish_rate": 0.811, "comp_len": 540.5, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 49.3, "frames": {"chat": 222}, "mem_gb": 22.12}
+{"step": 40, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.1793475973547126, "tokens": 120000, "cumulative_loss_tokens": 4800000, "grad_norm": 0.6171875, "lr": 3e-05, "finish_rate": 0.99, "comp_len": 294.1, "t_data_s": 0.1, "t_rollout_s": 0.0, "t_step_s": 75.8, "frames": {"chat": 408}, "mem_gb": 21.68}
+[eval step 40] sample: 'To determine how many months it will take for Jamie to reach their savings goal of $1,000,000, we need to consider both their monthly savings and the interest earned from their investment account.\n\n1.'
+{"step": 41, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.17456044655498118, "tokens": 120000, "cumulative_loss_tokens": 4920000, "grad_norm": 0.52734375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 359.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 66.5, "frames": {"chat": 334}, "mem_gb": 21.75}
+{"step": 42, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.08869157117024686, "tokens": 120000, "cumulative_loss_tokens": 5040000, "grad_norm": 0.400390625, "lr": 3e-05, "finish_rate": 0.968, "comp_len": 384.6, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 60.1, "frames": {"chat": 312}, "mem_gb": 21.8}
+{"step": 43, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.1764846066198622, "tokens": 120000, "cumulative_loss_tokens": 5160000, "grad_norm": 0.51171875, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 340.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 70.0, "frames": {"chat": 352}, "mem_gb": 21.78}
+{"step": 44, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.14268789846193977, "tokens": 120000, "cumulative_loss_tokens": 5280000, "grad_norm": 0.5, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 323.5, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 70.8, "frames": {"chat": 371}, "mem_gb": 21.59}
+{"step": 45, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.10607967147635451, "tokens": 120000, "cumulative_loss_tokens": 5400000, "grad_norm": 0.443359375, "lr": 3e-05, "finish_rate": 0.898, "comp_len": 422.5, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 61.1, "frames": {"chat": 284}, "mem_gb": 22.04}
+{"step": 46, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.07848575267903507, "tokens": 120000, "cumulative_loss_tokens": 5520000, "grad_norm": 0.396484375, "lr": 3e-05, "finish_rate": 0.923, "comp_len": 418.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 57.0, "frames": {"chat": 287}, "mem_gb": 22.1}
+{"step": 47, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.07426931333943891, "tokens": 120000, "cumulative_loss_tokens": 5640000, "grad_norm": 0.373046875, "lr": 3e-05, "finish_rate": 0.916, "comp_len": 421.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 57.1, "frames": {"chat": 285}, "mem_gb": 22.0}
+{"step": 48, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.0474071029479693, "tokens": 120000, "cumulative_loss_tokens": 5760000, "grad_norm": 0.3203125, "lr": 3e-05, "finish_rate": 0.873, "comp_len": 476.2, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 53.2, "frames": {"chat": 252}, "mem_gb": 22.09}
+{"step": 49, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.17111980931263726, "tokens": 120000, "cumulative_loss_tokens": 5880000, "grad_norm": 0.53125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 333.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 69.7, "frames": {"chat": 360}, "mem_gb": 21.88}
+{"step": 50, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.19600136266113258, "tokens": 120000, "cumulative_loss_tokens": 6000000, "grad_norm": 0.5859375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 334.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 69.5, "frames": {"chat": 359}, "mem_gb": 21.87}
+[eval step 50] sample: 'To determine how many months it will take for Jamie to reach their goal of $1,000,000, we need to consider both their monthly savings and the interest earned from their investment account.\n\n**Step 1: '
+checkpoint snapshot queued -> outputs/healed/grid_general/reap_keep75_s1225/step0050
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+[eval step 60] sample: 'To solve this problem, we need to determine how many months it will take for Jamie to reach their savings goal of $1,000,000, considering both their monthly savings and the interest earned from their '
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+[eval step 70] sample: 'To solve this problem, we need to determine how many months it will take Jamie to reach their savings goal of $1,000,000, given their monthly savings and the interest earned from their investment acco'
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+{"step": 79, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.1458736981579568, "tokens": 120000, "cumulative_loss_tokens": 9480000, "grad_norm": 0.4609375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 363.6, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 64.2, "frames": {"chat": 330}, "mem_gb": 21.89}
+{"step": 80, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.15538068436005464, "tokens": 120000, "cumulative_loss_tokens": 9600000, "grad_norm": 0.482421875, "lr": 3e-05, "finish_rate": 0.991, "comp_len": 379.7, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 61.1, "frames": {"chat": 316}, "mem_gb": 21.92}
+[eval step 80] sample: 'To solve this problem, we need to determine how many months it will take for Jamie to reach their savings goal of $1,000,000, given their monthly savings and the interest earned from their investment '
+{"step": 81, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.10480584769328125, "tokens": 120000, "cumulative_loss_tokens": 9720000, "grad_norm": 0.451171875, "lr": 3e-05, "finish_rate": 0.891, "comp_len": 434.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 59.1, "frames": {"chat": 276}, "mem_gb": 22.1}
+{"step": 82, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.08371606114126431, "tokens": 120000, "cumulative_loss_tokens": 9840000, "grad_norm": 0.3984375, "lr": 3e-05, "finish_rate": 0.833, "comp_len": 500.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 53.9, "frames": {"chat": 240}, "mem_gb": 22.04}
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+{"step": 88, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.13745868044605788, "tokens": 120000, "cumulative_loss_tokens": 10560000, "grad_norm": 0.46484375, "lr": 3e-05, "finish_rate": 0.923, "comp_len": 404.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 62.8, "frames": {"chat": 297}, "mem_gb": 22.09}
+{"step": 89, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.05132270212679481, "tokens": 120000, "cumulative_loss_tokens": 10680000, "grad_norm": 0.5234375, "lr": 3e-05, "finish_rate": 0.799, "comp_len": 547.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 47.8, "frames": {"chat": 219}, "mem_gb": 22.11}
+{"step": 90, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.1766696895766693, "tokens": 120000, "cumulative_loss_tokens": 10800000, "grad_norm": 0.515625, "lr": 3e-05, "finish_rate": 0.99, "comp_len": 392.2, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 62.2, "frames": {"chat": 306}, "mem_gb": 21.81}
+[eval step 90] sample: 'To solve this problem, we need to determine how many months it will take for Jamie to reach their savings goal of $1,000,000, considering both their monthly savings and the interest earned from their '
+{"step": 91, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.17392939229660356, "tokens": 120000, "cumulative_loss_tokens": 10920000, "grad_norm": 0.51953125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 349.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 65.0, "frames": {"chat": 343}, "mem_gb": 21.75}
+wandb: updating run metadata
+wandb: uploading wandb-summary.json
+wandb: 
+wandb: Run history:
+wandb:               comp_len ▅▃▅▅▆▂▃▄▃▃▂▂▃▁▅▅▂█▂▁▄▄▆▂▂▄▃▁▂▁▄▇▃▅▂▁▅▄▅█
+wandb: cumulative_loss_tokens ▁▁▁▁▁▂▂▂▂▂▂▂▃▃▃▃▃▃▃▄▄▄▅▅▅▅▅▅▅▅▆▆▆▆▇▇████
+wandb:                  epoch ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
+wandb:            finish_rate █▇█▆▅██▆▇▅██▅▇▄█▃█▁▇▅▃███▇██▇▃▇█▆▇█▄▂▆██
+wandb:        forward_topk_kl █▂▇▅▄▆▆▃▆▄▃▅▅▄▅▇▃▃▁▅▄▃▂▁▅▃▆▄▆▄▄▅▆▄▅▂▃▃▅▅
+wandb:              grad_norm █▆▄▂▂▂▂▂▁▁▂▂▁▂▁▁▂▁▁▁▂▂▁▂▁▂▁▁▁▁▂▂▂▂▁▁▁▂▂▂
+wandb:                     lr ▁▄▇█████████████████████████████████████
+wandb:                 mem_gb ▃▄▆▆▅▆▄▇▆▃▆▄▇▅▆▄█▇▄██▄▄▂▃▄▆▆▂▅▁▄█▇▅▄███▂
+wandb:                   step ▁▁▂▂▂▂▂▂▂▃▃▃▄▄▄▄▄▅▅▅▅▅▅▅▆▆▆▆▆▆▇▇▇▇▇▇████
+wandb:               t_data_s █▁▁▁▁█▁▁▁▁▁▁▁▁▁█▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
+wandb:                     +3 ...
+wandb: 
+wandb: Run summary:
+wandb:               comp_len 349.9
+wandb: cumulative_loss_tokens 10920000
+wandb:                  epoch 0
+wandb:            finish_rate 0.997
+wandb:        forward_topk_kl 0.17393
+wandb:              grad_norm 0.51953
+wandb:                     lr 3e-05
+wandb:                 mem_gb 21.75
+wandb:                   step 91
+wandb:               t_data_s 0
+wandb:                     +4 ...
+wandb: 
+wandb: 🚀 View run reap_keep75_s1225 at: https://wandb.ai/hbfreed/glean-general-grid/runs/eyetgo3a
+wandb: ⭐️ View project at: https://wandb.ai/hbfreed/glean-general-grid
+wandb: Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)
+wandb: Find logs at: outputs/healed/grid_general/reap_keep75_s1225/wandb/run-20260718_224025-eyetgo3a/logs
+Traceback (most recent call last):
+  File "/home/henry/Documents/PythonProjects/variable-reap/scripts/11_distill_on_policy.py", line 1099, in <module>
+    main()
+  File "/home/henry/Documents/PythonProjects/variable-reap/scripts/11_distill_on_policy.py", line 905, in main
+    opt.step()
+  File "/home/henry/Documents/PythonProjects/variable-reap/.venv/lib/python3.12/site-packages/torch/optim/lr_scheduler.py", line 166, in wrapper
+    return func.__get__(opt, opt.__class__)(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/home/henry/Documents/PythonProjects/variable-reap/.venv/lib/python3.12/site-packages/torch/optim/optimizer.py", line 535, in wrapper
+    out = func(*args, **kwargs)
+          ^^^^^^^^^^^^^^^^^^^^^
+  File "/home/henry/Documents/PythonProjects/variable-reap/.venv/lib/python3.12/site-packages/torch/utils/_contextlib.py", line 124, in decorate_context
+    return func(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^
+  File "/home/henry/Documents/PythonProjects/variable-reap/.venv/lib/python3.12/site-packages/bitsandbytes/optim/optimizer.py", line 329, in step
+    sync_gpu(p)
+  File "/home/henry/Documents/PythonProjects/variable-reap/.venv/lib/python3.12/site-packages/bitsandbytes/utils.py", line 203, in sync_gpu
+    torch.cuda.synchronize()
+  File "/home/henry/Documents/PythonProjects/variable-reap/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py", line 1181, in synchronize
+    return torch._C._cuda_synchronize()
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+torch.AcceleratorError: CUDA error: an illegal memory access was encountered
+Search for `cudaErrorIllegalAddress' in https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__TYPES.html for more information.
+CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
+For debugging consider passing CUDA_LAUNCH_BLOCKING=1
+Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.
+
+/home/henry/Documents/PythonProjects/variable-reap/.venv/lib/python3.12/site-packages/megablocks/grouped_gemm_util.py:10: UserWarning: Grouped GEMM not available.
+  warnings.warn('Grouped GEMM not available.')
+wandb: [wandb.login()] Loaded credentials for https://api.wandb.ai from /home/henry/.netrc.
+wandb: Currently logged in as: hbfreed to https://api.wandb.ai. Use `wandb login --relogin` to force relogin
+wandb: setting up run 726qmwib
+wandb: Tracking run with wandb version 0.28.0
+wandb: Run data is saved locally in outputs/healed/grid_general/reap_keep75_s1225/wandb/run-20260719_075731-726qmwib
+wandb: Run `wandb offline` to turn off syncing.
+wandb: Syncing run reap_keep75_s1225
+wandb: ⭐️ View project at https://wandb.ai/hbfreed/glean-general-grid
+wandb: 🚀 View run at https://wandb.ai/hbfreed/glean-general-grid/runs/726qmwib
+
+58360 cached top-128 chat trajectories / 22,295,631 unique tokens | 185 steps/epoch | 150 total steps | student params 5.31B | teacher overlap=False
+{"step": 1, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.32691152300952625, "tokens": 120000, "cumulative_loss_tokens": 120000, "grad_norm": 3.09375, "lr": 6e-06, "finish_rate": 0.997, "comp_len": 362.5, "t_data_s": 0.1, "t_rollout_s": 0.0, "t_step_s": 73.0, "frames": {"chat": 331}, "mem_gb": 21.8}
+The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.
+[eval step 1] sample: 'To determine how many months it will take for Jamie to reach the savings goal of $1,000,000, we need to consider both the monthly savings and the interest earned.\n\n1. **Initial Savings:**\n   - Jamie s'
+{"step": 2, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.3204413974587495, "tokens": 120000, "cumulative_loss_tokens": 240000, "grad_norm": 3.21875, "lr": 9e-06, "finish_rate": 0.997, "comp_len": 350.9, "t_data_s": 0.1, "t_rollout_s": 0.0, "t_step_s": 66.1, "frames": {"chat": 342}, "mem_gb": 21.72}
+{"step": 3, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.3277132033892907, "tokens": 120000, "cumulative_loss_tokens": 360000, "grad_norm": 3.046875, "lr": 1.2e-05, "finish_rate": 0.997, "comp_len": 335.2, "t_data_s": 0.1, "t_rollout_s": 0.0, "t_step_s": 67.9, "frames": {"chat": 358}, "mem_gb": 21.76}
+{"step": 4, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.2655149221446986, "tokens": 120000, "cumulative_loss_tokens": 480000, "grad_norm": 2.328125, "lr": 1.5e-05, "finish_rate": 0.997, "comp_len": 348.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 70.4, "frames": {"chat": 344}, "mem_gb": 21.84}
+{"step": 5, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.16589509377960737, "tokens": 120000, "cumulative_loss_tokens": 600000, "grad_norm": 1.734375, "lr": 1.8e-05, "finish_rate": 0.861, "comp_len": 449.4, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 58.1, "frames": {"chat": 267}, "mem_gb": 22.16}
+{"step": 6, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.07710085937269032, "tokens": 120000, "cumulative_loss_tokens": 720000, "grad_norm": 0.890625, "lr": 2.1e-05, "finish_rate": 0.835, "comp_len": 483.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 49.9, "frames": {"chat": 248}, "mem_gb": 21.99}
+{"step": 7, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.22985986243913262, "tokens": 120000, "cumulative_loss_tokens": 840000, "grad_norm": 1.3359375, "lr": 2.4e-05, "finish_rate": 0.979, "comp_len": 364.7, "t_data_s": 0.1, "t_rollout_s": 0.0, "t_step_s": 63.1, "frames": {"chat": 329}, "mem_gb": 21.98}
+{"step": 8, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.23923230728444952, "tokens": 120000, "cumulative_loss_tokens": 960000, "grad_norm": 1.265625, "lr": 2.7000000000000002e-05, "finish_rate": 0.997, "comp_len": 354.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 67.7, "frames": {"chat": 339}, "mem_gb": 21.94}
+{"step": 9, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.24544233199153095, "tokens": 120000, "cumulative_loss_tokens": 1080000, "grad_norm": 1.09375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 339.0, "t_data_s": 0.1, "t_rollout_s": 0.0, "t_step_s": 68.5, "frames": {"chat": 354}, "mem_gb": 21.94}
+{"step": 10, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.14266173163352844, "tokens": 120000, "cumulative_loss_tokens": 1200000, "grad_norm": 0.6640625, "lr": 3e-05, "finish_rate": 0.943, "comp_len": 430.1, "t_data_s": 0.1, "t_rollout_s": 0.0, "t_step_s": 54.4, "frames": {"chat": 279}, "mem_gb": 22.07}
+[eval step 10] sample: 'To determine how many months it will take for Jamie to reach their goal of $1,000,000, we need to consider both the monthly savings and the interest earned from the investment account.\n\n1. **Initial S'
+{"step": 11, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.17983991835253935, "tokens": 120000, "cumulative_loss_tokens": 1320000, "grad_norm": 0.73828125, "lr": 3e-05, "finish_rate": 0.959, "comp_len": 377.4, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 65.1, "frames": {"chat": 318}, "mem_gb": 22.0}
+{"step": 12, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.1222650872319316, "tokens": 120000, "cumulative_loss_tokens": 1440000, "grad_norm": 0.5859375, "lr": 3e-05, "finish_rate": 0.875, "comp_len": 452.8, "t_data_s": 0.1, "t_rollout_s": 0.0, "t_step_s": 56.6, "frames": {"chat": 265}, "mem_gb": 22.09}
+{"step": 13, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.10940296062845736, "tokens": 120000, "cumulative_loss_tokens": 1560000, "grad_norm": 0.5546875, "lr": 3e-05, "finish_rate": 0.85, "comp_len": 472.4, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 53.2, "frames": {"chat": 254}, "mem_gb": 22.03}
+{"step": 14, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.1617978121754403, "tokens": 120000, "cumulative_loss_tokens": 1680000, "grad_norm": 0.61328125, "lr": 3e-05, "finish_rate": 0.962, "comp_len": 381.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 65.2, "frames": {"chat": 315}, "mem_gb": 22.06}
+{"step": 15, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.23280616224833453, "tokens": 120000, "cumulative_loss_tokens": 1800000, "grad_norm": 0.70703125, "lr": 3e-05, "finish_rate": 0.977, "comp_len": 400.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 60.7, "frames": {"chat": 300}, "mem_gb": 21.86}
+{"step": 16, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.14960211009665703, "tokens": 120000, "cumulative_loss_tokens": 1920000, "grad_norm": 0.62109375, "lr": 3e-05, "finish_rate": 0.924, "comp_len": 434.8, "t_data_s": 0.1, "t_rollout_s": 0.0, "t_step_s": 60.9, "frames": {"chat": 276}, "mem_gb": 22.06}
+{"step": 17, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.23242386436782156, "tokens": 120000, "cumulative_loss_tokens": 2040000, "grad_norm": 0.68359375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 337.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 72.9, "frames": {"chat": 356}, "mem_gb": 22.0}
+{"step": 18, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.19974000631744662, "tokens": 120000, "cumulative_loss_tokens": 2160000, "grad_norm": 0.61328125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 394.7, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 61.4, "frames": {"chat": 304}, "mem_gb": 21.81}
+{"step": 19, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.20908848262146737, "tokens": 120000, "cumulative_loss_tokens": 2280000, "grad_norm": 0.625, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 320.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 73.0, "frames": {"chat": 375}, "mem_gb": 21.73}
+{"step": 20, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.11517996143201988, "tokens": 120000, "cumulative_loss_tokens": 2400000, "grad_norm": 0.49609375, "lr": 3e-05, "finish_rate": 0.939, "comp_len": 408.2, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 60.5, "frames": {"chat": 294}, "mem_gb": 22.09}
+[eval step 20] sample: 'To determine how many months it will take for Jamie to reach their savings goal of $1,000,000, we need to consider both their monthly savings and the interest earned from their investment account.\n\n**'
+{"step": 21, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.1984305429960601, "tokens": 120000, "cumulative_loss_tokens": 2520000, "grad_norm": 0.578125, "lr": 3e-05, "finish_rate": 0.98, "comp_len": 401.3, "t_data_s": 0.1, "t_rollout_s": 0.0, "t_step_s": 65.3, "frames": {"chat": 299}, "mem_gb": 22.1}
+{"step": 22, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.14950802131282787, "tokens": 120000, "cumulative_loss_tokens": 2640000, "grad_norm": 0.53515625, "lr": 3e-05, "finish_rate": 0.98, "comp_len": 393.4, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 64.0, "frames": {"chat": 305}, "mem_gb": 21.88}
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+[eval step 30] sample: 'To determine how many months it will take for Jamie to reach their savings goal of $1,000,000, we need to consider both their monthly savings and the interest earned from their investment account.\n\n**'
+{"step": 31, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.11021925988085568, "tokens": 120000, "cumulative_loss_tokens": 3720000, "grad_norm": 0.45703125, "lr": 3e-05, "finish_rate": 0.891, "comp_len": 436.4, "t_data_s": 0.1, "t_rollout_s": 0.0, "t_step_s": 57.5, "frames": {"chat": 275}, "mem_gb": 22.04}
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+[eval step 40] sample: 'To determine how many months it will take for Jamie to reach their savings goal of $1,000,000, we need to consider both their monthly savings and the interest earned from their investment account.\n\n1.'
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+[eval step 100] sample: 'To solve this problem, we need to determine how many months it will take for Jamie to reach their savings goal of $1,000,000, considering both their monthly savings and the interest earned from their '
+checkpoint snapshot queued -> outputs/healed/grid_general/reap_keep75_s1225/step0100
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+{"step": 109, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.1100006240118683, "tokens": 120000, "cumulative_loss_tokens": 13080000, "grad_norm": 0.4140625, "lr": 3e-05, "finish_rate": 0.959, "comp_len": 382.2, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 62.5, "frames": {"chat": 314}, "mem_gb": 21.98}
+{"step": 110, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.17935109357194354, "tokens": 120000, "cumulative_loss_tokens": 13200000, "grad_norm": 0.546875, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 408.2, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 64.9, "frames": {"chat": 294}, "mem_gb": 21.91}
+[eval step 110] sample: 'To solve this problem, we need to determine how many months it will take for Jamie to reach their savings goal of $1,000,000, considering both their monthly savings and the interest earned from their '
+{"step": 111, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.08094486156906157, "tokens": 120000, "cumulative_loss_tokens": 13320000, "grad_norm": 0.392578125, "lr": 3e-05, "finish_rate": 0.894, "comp_len": 438.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 61.2, "frames": {"chat": 274}, "mem_gb": 22.04}
+{"step": 112, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.14713477355417173, "tokens": 120000, "cumulative_loss_tokens": 13440000, "grad_norm": 0.498046875, "lr": 3e-05, "finish_rate": 0.995, "comp_len": 328.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 73.7, "frames": {"chat": 365}, "mem_gb": 21.76}
+{"step": 113, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.09244580864451515, "tokens": 120000, "cumulative_loss_tokens": 13560000, "grad_norm": 0.388671875, "lr": 3e-05, "finish_rate": 0.972, "comp_len": 371.5, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 67.1, "frames": {"chat": 323}, "mem_gb": 21.82}
+{"step": 114, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.06983842617548071, "tokens": 120000, "cumulative_loss_tokens": 13680000, "grad_norm": 0.62109375, "lr": 3e-05, "finish_rate": 0.829, "comp_len": 512.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 55.5, "frames": {"chat": 234}, "mem_gb": 22.1}
+{"step": 115, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.09373808058573244, "tokens": 120000, "cumulative_loss_tokens": 13800000, "grad_norm": 0.451171875, "lr": 3e-05, "finish_rate": 0.891, "comp_len": 449.4, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 58.4, "frames": {"chat": 267}, "mem_gb": 22.11}
+{"step": 116, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.10429944481253624, "tokens": 120000, "cumulative_loss_tokens": 13920000, "grad_norm": 0.4140625, "lr": 3e-05, "finish_rate": 0.961, "comp_len": 394.7, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 64.4, "frames": {"chat": 304}, "mem_gb": 21.87}
+{"step": 117, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.16862349049216135, "tokens": 120000, "cumulative_loss_tokens": 14040000, "grad_norm": 0.515625, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 321.7, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 77.0, "frames": {"chat": 373}, "mem_gb": 21.8}
+{"step": 118, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.10003006101381033, "tokens": 120000, "cumulative_loss_tokens": 14160000, "grad_norm": 0.427734375, "lr": 3e-05, "finish_rate": 0.87, "comp_len": 400.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 67.4, "frames": {"chat": 300}, "mem_gb": 22.08}
+{"step": 119, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.17443383389748632, "tokens": 120000, "cumulative_loss_tokens": 14280000, "grad_norm": 0.60546875, "lr": 3e-05, "finish_rate": 0.982, "comp_len": 362.5, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 73.8, "frames": {"chat": 331}, "mem_gb": 21.92}
+{"step": 120, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.0799219426064752, "tokens": 120000, "cumulative_loss_tokens": 14400000, "grad_norm": 0.5546875, "lr": 3e-05, "finish_rate": 0.876, "comp_len": 463.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 60.4, "frames": {"chat": 259}, "mem_gb": 22.06}
+[eval step 120] sample: 'To solve this problem, we need to determine how many months it will take for Jamie to reach their savings goal of $1,000,000, considering both their monthly savings and the interest earned from their '
+{"step": 121, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.1439664171350499, "tokens": 120000, "cumulative_loss_tokens": 14520000, "grad_norm": 0.49609375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 335.2, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 72.8, "frames": {"chat": 358}, "mem_gb": 21.67}
+{"step": 122, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.18628301387413715, "tokens": 120000, "cumulative_loss_tokens": 14640000, "grad_norm": 0.5546875, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 339.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 72.1, "frames": {"chat": 354}, "mem_gb": 21.93}
+{"step": 123, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.08841487010531128, "tokens": 120000, "cumulative_loss_tokens": 14760000, "grad_norm": 0.40625, "lr": 3e-05, "finish_rate": 0.939, "comp_len": 430.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 62.8, "frames": {"chat": 279}, "mem_gb": 22.0}
+{"step": 124, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.17705591263951112, "tokens": 120000, "cumulative_loss_tokens": 14880000, "grad_norm": 0.5546875, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 339.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 72.6, "frames": {"chat": 354}, "mem_gb": 21.75}
+{"step": 125, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.16324466195267937, "tokens": 120000, "cumulative_loss_tokens": 15000000, "grad_norm": 0.494140625, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 359.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 70.3, "frames": {"chat": 334}, "mem_gb": 21.95}
+{"step": 126, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.16651589091358085, "tokens": 120000, "cumulative_loss_tokens": 15120000, "grad_norm": 0.5234375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 367.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 70.5, "frames": {"chat": 327}, "mem_gb": 21.82}
+{"step": 127, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.15119830299920092, "tokens": 120000, "cumulative_loss_tokens": 15240000, "grad_norm": 0.5234375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 328.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 71.4, "frames": {"chat": 365}, "mem_gb": 21.92}
+{"step": 128, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.08960195419403413, "tokens": 120000, "cumulative_loss_tokens": 15360000, "grad_norm": 0.419921875, "lr": 3e-05, "finish_rate": 0.869, "comp_len": 476.2, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 59.7, "frames": {"chat": 252}, "mem_gb": 22.12}
+{"step": 129, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.07137346615381539, "tokens": 120000, "cumulative_loss_tokens": 15480000, "grad_norm": 0.40234375, "lr": 3e-05, "finish_rate": 0.867, "comp_len": 468.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 58.0, "frames": {"chat": 256}, "mem_gb": 22.09}
+{"step": 130, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.10397039002155264, "tokens": 120000, "cumulative_loss_tokens": 15600000, "grad_norm": 0.416015625, "lr": 3e-05, "finish_rate": 0.964, "comp_len": 397.4, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 60.7, "frames": {"chat": 302}, "mem_gb": 21.86}
+[eval step 130] sample: 'To determine how many months it will take for Jamie to reach their savings goal of $1,000,000, we need to consider both their monthly savings and the interest earned from their investment account.\n\n**'
+{"step": 131, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.1565368466370739, "tokens": 120000, "cumulative_loss_tokens": 15720000, "grad_norm": 0.5, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 342.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 74.1, "frames": {"chat": 350}, "mem_gb": 21.77}
+{"step": 132, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.08637186304094115, "tokens": 120000, "cumulative_loss_tokens": 15840000, "grad_norm": 0.400390625, "lr": 3e-05, "finish_rate": 0.959, "comp_len": 411.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 63.8, "frames": {"chat": 292}, "mem_gb": 21.89}
+{"step": 133, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.16638688029507176, "tokens": 120000, "cumulative_loss_tokens": 15960000, "grad_norm": 0.5234375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 337.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 72.8, "frames": {"chat": 356}, "mem_gb": 21.7}
+{"step": 134, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.10226424861007835, "tokens": 120000, "cumulative_loss_tokens": 16080000, "grad_norm": 0.419921875, "lr": 3e-05, "finish_rate": 0.977, "comp_len": 345.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 68.8, "frames": {"chat": 347}, "mem_gb": 21.86}
+{"step": 135, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.10496823356381307, "tokens": 120000, "cumulative_loss_tokens": 16200000, "grad_norm": 0.412109375, "lr": 3e-05, "finish_rate": 0.976, "comp_len": 363.6, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 67.6, "frames": {"chat": 330}, "mem_gb": 22.06}
+{"step": 136, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.15377609162183167, "tokens": 120000, "cumulative_loss_tokens": 16320000, "grad_norm": 0.478515625, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 330.6, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 76.6, "frames": {"chat": 363}, "mem_gb": 21.92}
+{"step": 137, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.14029487463639428, "tokens": 120000, "cumulative_loss_tokens": 16440000, "grad_norm": 0.474609375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 339.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 75.7, "frames": {"chat": 354}, "mem_gb": 21.78}
+{"step": 138, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.07951566191191474, "tokens": 120000, "cumulative_loss_tokens": 16560000, "grad_norm": 0.37890625, "lr": 3e-05, "finish_rate": 0.93, "comp_len": 444.4, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 55.9, "frames": {"chat": 270}, "mem_gb": 22.04}
+{"step": 139, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.15175273126789057, "tokens": 120000, "cumulative_loss_tokens": 16680000, "grad_norm": 0.494140625, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 349.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 68.1, "frames": {"chat": 343}, "mem_gb": 21.82}
+{"step": 140, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.03845967609772148, "tokens": 120000, "cumulative_loss_tokens": 16800000, "grad_norm": 0.296875, "lr": 3e-05, "finish_rate": 0.845, "comp_len": 517.2, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 50.8, "frames": {"chat": 232}, "mem_gb": 22.01}
+[eval step 140] sample: 'To determine how many months it will take for Jamie to reach their savings goal of $1,000,000, we need to consider both their monthly savings and the interest earned from their investment account.\n\n**'
+{"step": 141, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.14632255258143415, "tokens": 120000, "cumulative_loss_tokens": 16920000, "grad_norm": 0.470703125, "lr": 3e-05, "finish_rate": 0.98, "comp_len": 346.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 69.0, "frames": {"chat": 346}, "mem_gb": 21.96}
+{"step": 142, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.11298904120076138, "tokens": 120000, "cumulative_loss_tokens": 17040000, "grad_norm": 0.423828125, "lr": 3e-05, "finish_rate": 0.951, "comp_len": 392.2, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 60.9, "frames": {"chat": 306}, "mem_gb": 22.04}
+{"step": 143, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.06970870715503891, "tokens": 120000, "cumulative_loss_tokens": 17160000, "grad_norm": 0.349609375, "lr": 3e-05, "finish_rate": 0.923, "comp_len": 439.6, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 55.5, "frames": {"chat": 273}, "mem_gb": 22.09}
+{"step": 144, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.08088071228267314, "tokens": 120000, "cumulative_loss_tokens": 17280000, "grad_norm": 0.36328125, "lr": 3e-05, "finish_rate": 0.959, "comp_len": 451.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 54.4, "frames": {"chat": 266}, "mem_gb": 21.91}
+{"step": 145, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.12068498143667045, "tokens": 120000, "cumulative_loss_tokens": 17400000, "grad_norm": 0.4375, "lr": 3e-05, "finish_rate": 0.936, "comp_len": 401.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 62.2, "frames": {"chat": 299}, "mem_gb": 22.11}
+{"step": 146, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.07787700651586056, "tokens": 120000, "cumulative_loss_tokens": 17520000, "grad_norm": 0.3828125, "lr": 3e-05, "finish_rate": 0.88, "comp_len": 463.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 55.0, "frames": {"chat": 259}, "mem_gb": 22.19}
+{"step": 147, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.09737688761447401, "tokens": 120000, "cumulative_loss_tokens": 17640000, "grad_norm": 0.412109375, "lr": 3e-05, "finish_rate": 0.966, "comp_len": 369.2, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 60.1, "frames": {"chat": 325}, "mem_gb": 21.93}
+{"step": 148, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.09278960750096787, "tokens": 120000, "cumulative_loss_tokens": 17760000, "grad_norm": 0.384765625, "lr": 3e-05, "finish_rate": 0.923, "comp_len": 442.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 53.8, "frames": {"chat": 271}, "mem_gb": 22.0}
+{"step": 149, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.148417767790736, "tokens": 120000, "cumulative_loss_tokens": 17880000, "grad_norm": 0.45703125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 334.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 64.9, "frames": {"chat": 359}, "mem_gb": 21.79}
+{"step": 150, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.13472050697378193, "tokens": 120000, "cumulative_loss_tokens": 18000000, "grad_norm": 0.4453125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 347.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 66.1, "frames": {"chat": 345}, "mem_gb": 21.85}
+[eval step 150] sample: 'To solve this problem, we need to determine how many months it will take for Jamie to reach their savings goal of $1,000,000, considering both their monthly savings and the interest earned from their '
+checkpoint snapshot queued -> outputs/healed/grid_general/reap_keep75_s1225/step0150
+wandb: updating run metadata
+wandb: uploading config.yaml; uploading output.log; uploading wandb-summary.json
+wandb: 
+wandb: Run history:
+wandb:               comp_len ▂▂▂▂▃▆▄▅▄▄▂▃▅█▂▂▁▄▆▅▁▂▄▄▂▆▁▇▅▄▃▅▃▄▂▂▂▁▂▇
+wandb: cumulative_loss_tokens ▁▁▁▁▁▂▂▃▃▃▃▃▃▃▃▄▄▄▄▄▄▄▅▅▅▅▅▆▆▆▆▆▆▇▇▇▇▇▇█
+wandb:                  epoch ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
+wandb:            finish_rate ██▃▄▅▆▆▅▇█▁▄█▆█▇▇▆▇▆██▆▁██▆▅█▇▄▄█▆█▇▇█▆▅
+wandb:        forward_topk_kl █▇▄▅▇▃▃▅▇▂▆▅▂▅▃▅▅▆▄▁▃▅▅▃▂▃▃▂▅▂▅▂▅▄▃▃▂▄▁▂
+wandb:              grad_norm ▇▅▇█▅▃▆▁▃▅▆▄▇▃▄▄▃▇▆▅▅▄▁▂▂▄▄▁▂▆▆▆▄▅▅▁▅▂▁▃
+wandb:                     lr ▁███████████████████████████████████████
+wandb:                 mem_gb ▅▅▇▆▇▅▄▇▆▄▃▆▇▄▂▂▂▅▅▄▃▄▇▇▁▃▃▆▅▇▄▇▄▁▅▃▆▇█▃
+wandb:                   step ▁▁▁▁▁▂▂▂▂▂▃▃▃▃▃▃▃▄▄▄▄▄▄▅▅▅▅▅▅▅▆▆▆▆▇▇▇███
+wandb:               t_data_s ▁▁▁▁█▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
+wandb:                     +3 ...
+wandb: 
+wandb: Run summary:
+wandb:               comp_len 347.8
+wandb: cumulative_loss_tokens 18000000
+wandb:                  epoch 0
+wandb:            finish_rate 0.997
+wandb:        forward_topk_kl 0.13472
+wandb:              grad_norm 0.44531
+wandb:                     lr 3e-05
+wandb:                 mem_gb 21.85
+wandb:                   step 150
+wandb:               t_data_s 0
+wandb:                     +4 ...
+wandb: 
+wandb: 🚀 View run reap_keep75_s1225 at: https://wandb.ai/hbfreed/glean-general-grid/runs/726qmwib
+wandb: ⭐️ View project at: https://wandb.ai/hbfreed/glean-general-grid
+wandb: Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)
+wandb: Find logs at: outputs/healed/grid_general/reap_keep75_s1225/wandb/run-20260719_075731-726qmwib/logs

healed/grid_general/reap_keep75_s1226.console.log

@@ -0,0 +1,215 @@
+/home/henry/Documents/PythonProjects/variable-reap/.venv/lib/python3.12/site-packages/megablocks/grouped_gemm_util.py:10: UserWarning: Grouped GEMM not available.
+  warnings.warn('Grouped GEMM not available.')
+wandb: [wandb.login()] Loaded credentials for https://api.wandb.ai from /home/henry/.netrc.
+wandb: Currently logged in as: hbfreed to https://api.wandb.ai. Use `wandb login --relogin` to force relogin
+wandb: Tracking run with wandb version 0.28.0
+wandb: Run data is saved locally in outputs/healed/grid_general/reap_keep75_s1226/wandb/run-20260718_222421-e1pary2f
+wandb: Run `wandb offline` to turn off syncing.
+wandb: Syncing run reap_keep75_s1226
+wandb: ⭐️ View project at https://wandb.ai/hbfreed/glean-general-grid
+wandb: 🚀 View run at https://wandb.ai/hbfreed/glean-general-grid/runs/e1pary2f
+
+58360 cached top-128 chat trajectories / 22,295,631 unique tokens | 185 steps/epoch | 150 total steps | student params 5.31B | teacher overlap=False
+{"step": 1, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.21339355171055843, "tokens": 120000, "cumulative_loss_tokens": 120000, "grad_norm": 2.484375, "lr": 6e-06, "finish_rate": 0.945, "comp_len": 389.6, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 68.4, "frames": {"chat": 308}, "mem_gb": 21.9}
+The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.
+[eval step 1] sample: 'to solve the problem, we start by understanding the properties of an arithmetic sequence. in an arithmetic sequence, each term after the first is the sum of the previous term and a constant difference'
+{"step": 2, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.08570967371991525, "tokens": 120000, "cumulative_loss_tokens": 240000, "grad_norm": 1.828125, "lr": 9e-06, "finish_rate": 0.754, "comp_len": 568.7, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 48.1, "frames": {"chat": 211}, "mem_gb": 22.09}
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+[eval step 10] sample: "to solve the problem, let's first understand the properties of an arithmetic sequence. in an arithmetic sequence, each term is obtained by adding a constant difference, denoted as 'd' to the previous "
+{"step": 11, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.09165873881246274, "tokens": 120000, "cumulative_loss_tokens": 1320000, "grad_norm": 0.58984375, "lr": 3e-05, "finish_rate": 0.872, "comp_len": 495.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 54.5, "frames": {"chat": 242}, "mem_gb": 22.09}
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+[eval step 20] sample: "to solve the problem, let's first understand the properties of an arithmetic sequence. in an arithmetic sequence, each term is obtained by adding a constant difference, denoted as d, to the previous t"
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+[eval step 30] sample: "to solve the problem, let's start by understanding the properties of an arithmetic sequence. in an arithmetic sequence, each term is obtained by adding a constant difference, denoted as d, to the prev"
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+{"step": 37, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.1646992900542294, "tokens": 120000, "cumulative_loss_tokens": 4440000, "grad_norm": 0.5625, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 394.7, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 64.0, "frames": {"chat": 304}, "mem_gb": 21.77}
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+[eval step 40] sample: "to solve the problem, let's first express the terms of the arithmetic sequence in terms of the first term \\(a_1\\) and the common difference \\(d\\).\n\nthe general term \\(a_n\\) of an arithmetic sequence i"
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+{"step": 143, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.18335347756737222, "tokens": 120000, "cumulative_loss_tokens": 17160000, "grad_norm": 0.56640625, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 333.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 68.0, "frames": {"chat": 360}, "mem_gb": 21.64}
+{"step": 144, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.17761860483808753, "tokens": 120000, "cumulative_loss_tokens": 17280000, "grad_norm": 0.49609375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 328.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 75.4, "frames": {"chat": 365}, "mem_gb": 21.82}
+{"step": 145, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.1455773277927966, "tokens": 120000, "cumulative_loss_tokens": 17400000, "grad_norm": 0.427734375, "lr": 3e-05, "finish_rate": 0.988, "comp_len": 355.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 67.6, "frames": {"chat": 338}, "mem_gb": 21.9}
+{"step": 146, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.06648420962695964, "tokens": 120000, "cumulative_loss_tokens": 17520000, "grad_norm": 0.41015625, "lr": 3e-05, "finish_rate": 0.888, "comp_len": 434.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 61.7, "frames": {"chat": 276}, "mem_gb": 22.01}
+{"step": 147, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.036196198924879235, "tokens": 120000, "cumulative_loss_tokens": 17640000, "grad_norm": 0.330078125, "lr": 3e-05, "finish_rate": 0.761, "comp_len": 574.2, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 51.4, "frames": {"chat": 209}, "mem_gb": 22.09}
+{"step": 148, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.14141823298179854, "tokens": 120000, "cumulative_loss_tokens": 17760000, "grad_norm": 0.5, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 316.6, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 75.7, "frames": {"chat": 379}, "mem_gb": 21.69}
+{"step": 149, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.13381415352725112, "tokens": 120000, "cumulative_loss_tokens": 17880000, "grad_norm": 0.50390625, "lr": 3e-05, "finish_rate": 0.928, "comp_len": 390.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 66.9, "frames": {"chat": 307}, "mem_gb": 22.03}
+{"step": 150, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.08819010019679553, "tokens": 120000, "cumulative_loss_tokens": 18000000, "grad_norm": 0.41015625, "lr": 3e-05, "finish_rate": 0.942, "comp_len": 409.6, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 64.7, "frames": {"chat": 293}, "mem_gb": 22.07}
+[eval step 150] sample: "to solve the problem, let's first express the terms of the arithmetic sequence in terms of the first term \\(a_1\\) and the common difference \\(d\\).\n\nthe \\(n\\)-th term of an arithmetic sequence is given"
+checkpoint snapshot queued -> outputs/healed/grid_general/reap_keep75_s1226/step0150
+wandb: updating run metadata
+wandb: uploading summary
+wandb: 
+wandb: Run history:
+wandb:               comp_len ▃▅▂▄▂▂▂▆▄▁▂▄▃▂▄▅▂▄▄▃▅▃▁▇▂█▂▃▂▄▅▁▂▂▄▄▃▂▄█
+wandb: cumulative_loss_tokens ▁▁▁▁▁▂▂▂▂▂▂▃▃▃▃▃▃▃▃▃▄▄▄▄▄▅▅▆▆▆▆▆▇▇▇▇▇███
+wandb:                  epoch ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
+wandb:            finish_rate ▅█▄▁▆▂████▅▇▄█▇▆▄█▆█▃▂▃▇███▁▇▅▇▇███▄▇█▂▂
+wandb:        forward_topk_kl ▆▃█▆▅▄▄▅▄▅▃▃▅▃▃▃▆▅▂▃▄▄▆▁▃▁▂▃▆▅▆▅▃▃▄▅▅▄▂▃
+wandb:              grad_norm █▃▂▃▃▃▂▂▂▂▂▂▂▁▂▁▂▂▂▁▁▂▂▂▁▁▂▂▁▂▁▂▂▁▁▂▁▂▂▁
+wandb:                     lr ▁▅██████████████████████████████████████
+wandb:                 mem_gb ██▇▄▅▆▄▄█▄▄▆▇█▇▇▂▇▆▇▃█▆▃▄▇▅▇▄▂▅▄▁▅▅▅▁▄▇▇
+wandb:                   step ▁▁▁▁▂▂▂▂▂▃▃▃▃▄▄▄▄▄▄▄▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇▇▇███
+wandb:               t_data_s ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
+wandb:                     +3 ...
+wandb: 
+wandb: Run summary:
+wandb:               comp_len 409.6
+wandb: cumulative_loss_tokens 18000000
+wandb:                  epoch 0
+wandb:            finish_rate 0.942
+wandb:        forward_topk_kl 0.08819
+wandb:              grad_norm 0.41016
+wandb:                     lr 3e-05
+wandb:                 mem_gb 22.07
+wandb:                   step 150
+wandb:               t_data_s 0
+wandb:                     +4 ...
+wandb: 
+wandb: 🚀 View run reap_keep75_s1226 at: https://wandb.ai/hbfreed/glean-general-grid/runs/e1pary2f
+wandb: ⭐️ View project at: https://wandb.ai/hbfreed/glean-general-grid
+wandb: Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)
+wandb: Find logs at: outputs/healed/grid_general/reap_keep75_s1226/wandb/run-20260718_222421-e1pary2f/logs

healed/grid_general/reap_keep75_s1226.eval.log

@@ -0,0 +1,71 @@
+2026-07-19T01:12:55-07:00 serving outputs/healed/grid_general/reap_keep75_s1226/step0150 on GPU 1 port 8421
+2026-07-19T01:12:55-07:00 waiting for server /health ...
+2026-07-19T01:13:25-07:00 server up; chat pass [gsm8k_cot_zeroshot,minerva_math500,ifeval]
+2026-07-19:01:13:33 INFO     [_cli.run:388] Selected Tasks: ['gsm8k_cot_zeroshot', 'minerva_math500', 'ifeval']
+2026-07-19:01:13:34 INFO     [evaluator:214] Setting random seed to 0 | Setting numpy seed to 1234 | Setting torch manual seed to 1234 | Setting fewshot manual seed to 1234
+2026-07-19:01:13:34 WARNING  [evaluator:226] generation_kwargs: {'max_gen_toks': 1280} specified through cli, these settings will update set parameters in yaml tasks. Ensure 'do_sample=True' for non-greedy decoding!
+2026-07-19:01:13:34 INFO     [evaluator:239] Initializing local-chat-completions model, with arguments: {'model': 'student', 'base_url': 'http://127.0.0.1:8421/v1/chat/completions', 'num_concurrent': 48, 'tokenized_requests': False, 'max_retries': 3}
+2026-07-19:01:13:34 INFO     [models.api_models:179] Using max length 2048 - 1
+2026-07-19:01:13:34 INFO     [models.api_models:200] Using tokenizer None
+2026-07-19:01:13:39 INFO     [evaluator_utils:446] Selected tasks:
+2026-07-19:01:13:39 INFO     [evaluator_utils:480] Task: gsm8k_cot_zeroshot (gsm8k/gsm8k-cot-zeroshot.yaml)
+2026-07-19:01:13:39 INFO     [evaluator_utils:480] Task: ifeval (ifeval/ifeval.yaml)
+2026-07-19:01:13:39 INFO     [evaluator_utils:480] Task: minerva_math500 (minerva_math/minerva_math500.yaml)
+2026-07-19:01:13:39 INFO     [evaluator:314] gsm8k_cot_zeroshot: Using gen_kwargs: {'until': ['Q:', '</s>', '<|im_end|>'], 'do_sample': False, 'max_gen_toks': 1280}
+2026-07-19:01:13:39 INFO     [evaluator:314] minerva_math500: Using gen_kwargs: {'until': ['Problem:'], 'do_sample': False, 'temperature': 0.0, 'max_gen_toks': 1280}
+2026-07-19:01:13:39 INFO     [evaluator:314] ifeval: Using gen_kwargs: {'until': [], 'do_sample': False, 'temperature': 0.0, 'max_gen_toks': 1280}
+2026-07-19:01:13:39 INFO     [api.task:312] Building contexts for gsm8k_cot_zeroshot on rank 0...
+
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+2026-07-19:01:13:40 INFO     [api.task:312] Building contexts for minerva_math500 on rank 0...
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+2026-07-19:01:13:41 INFO     [api.task:312] Building contexts for ifeval on rank 0...
+
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+2026-07-19:01:13:41 INFO     [evaluator:585] Running generate_until requests
+2026-07-19:01:13:41 INFO     [models.api_models:747] Tokenized requests are disabled. Context + generation length is not checked.
+
+
+
+2026-07-19:01:19:00 ERROR    [tasks.ifeval.instructions:189] Unable to detect language for text ****** due to No features in text.
+2026-07-19:01:19:02 INFO     [loggers.evaluation_tracker:247] Saving results aggregated
+2026-07-19:01:19:02 INFO     [loggers.evaluation_tracker:119] Saving per-task samples to outputs/evals/general_suite/healed/reap_keep75_s1226/student/*.jsonl
+local-chat-completions ({'model': 'student', 'base_url': 'http://127.0.0.1:8421/v1/chat/completions', 'num_concurrent': 48, 'tokenized_requests': False, 'max_retries': 3}), gen_kwargs: ({'max_gen_toks': 1280}), limit: None, num_fewshot: None, batch_size: 1
+|      Tasks       |Version|     Filter     |n-shot|        Metric         |   |Value |   |Stderr|
+|------------------|------:|----------------|-----:|-----------------------|---|-----:|---|------|
+|gsm8k_cot_zeroshot|      3|flexible-extract|     0|exact_match            |↑  |0.6687|±  |0.0130|
+|                  |       |strict-match    |     0|exact_match            |↑  |0.0417|±  |0.0055|
+|ifeval            |      4|none            |     0|inst_level_loose_acc   |↑  |0.7374|±  |   N/A|
+|                  |       |none            |     0|inst_level_strict_acc  |↑  |0.7110|±  |   N/A|
+|                  |       |none            |     0|prompt_level_loose_acc |↑  |0.6322|±  |0.0208|
+|                  |       |none            |     0|prompt_level_strict_acc|↑  |0.6063|±  |0.0210|
+|minerva_math500   |      3|none            |     4|exact_match            |↑  |0.1380|±  |0.0154|
+|                  |       |none            |     4|math_verify            |↑  |0.2180|±  |0.0185|
+
+2026-07-19T01:19:04-07:00 code pass [humaneval,mbpp] via /v1/completions (function-continuation)
+2026-07-19:01:19:11 INFO     [_cli.run:388] Selected Tasks: ['humaneval', 'mbpp']
+2026-07-19:01:19:12 INFO     [evaluator:214] Setting random seed to 0 | Setting numpy seed to 1234 | Setting torch manual seed to 1234 | Setting fewshot manual seed to 1234
+2026-07-19:01:19:12 INFO     [evaluator:239] Initializing local-completions model, with arguments: {'model': 'student', 'base_url': 'http://127.0.0.1:8421/v1/completions', 'tokenizer': 'outputs/healed/grid_general/reap_keep75_s1226/step0150', 'num_concurrent': 48, 'tokenized_requests': False, 'max_retries': 3}
+2026-07-19:01:19:12 INFO     [models.openai_completions:42] Remote tokenizer not supported. Using huggingface tokenizer backend.
+2026-07-19:01:19:12 INFO     [models.api_models:179] Using max length 2048 - 1
+2026-07-19:01:19:12 INFO     [models.api_models:200] Using tokenizer huggingface
+2026-07-19:01:19:18 INFO     [evaluator_utils:446] Selected tasks:
+2026-07-19:01:19:18 INFO     [evaluator_utils:480] Task: humaneval (humaneval/humaneval.yaml)
+2026-07-19:01:19:18 INFO     [evaluator_utils:480] Task: mbpp (mbpp/mbpp.yaml)
+2026-07-19:01:19:18 INFO     [evaluator:314] humaneval: Using gen_kwargs: {'until': ['\nclass', '\ndef', '\n#', '\nif', '\nprint'], 'max_gen_toks': 1024, 'do_sample': False}
+2026-07-19:01:19:18 INFO     [evaluator:314] mbpp: Using gen_kwargs: {'until': ['[DONE]'], 'do_sample': False}
+2026-07-19:01:19:18 INFO     [api.task:312] Building contexts for humaneval on rank 0...
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+2026-07-19:01:19:19 INFO     [api.task:312] Building contexts for mbpp on rank 0...
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+2026-07-19:01:19:22 INFO     [evaluator:585] Running generate_until requests
+2026-07-19:01:19:22 INFO     [models.api_models:747] Tokenized requests are disabled. Context + generation length is not checked.
+
+
+2026-07-19:01:22:04 INFO     [loggers.evaluation_tracker:247] Saving results aggregated
+2026-07-19:01:22:04 INFO     [loggers.evaluation_tracker:119] Saving per-task samples to outputs/evals/general_suite/healed/reap_keep75_s1226/student/*.jsonl
+local-completions ({'model': 'student', 'base_url': 'http://127.0.0.1:8421/v1/completions', 'tokenizer': 'outputs/healed/grid_general/reap_keep75_s1226/step0150', 'num_concurrent': 48, 'tokenized_requests': False, 'max_retries': 3}), gen_kwargs: ({}), limit: None, num_fewshot: None, batch_size: 1
+|  Tasks  |Version|  Filter   |n-shot| Metric  |   |Value |   |Stderr|
+|---------|------:|-----------|-----:|---------|---|-----:|---|-----:|
+|humaneval|      1|create_test|     0|pass@1   |↑  |0.3232|±  |0.0366|
+|mbpp     |      1|none       |     3|pass_at_1|↑  |0.2680|±  |0.0198|
+
+2026-07-19T01:22:05-07:00 lm_eval exit=0 -> outputs/evals/general_suite/healed/reap_keep75_s1226

healed/grid_general/uniform_keep25_s1224.console.log

@@ -0,0 +1,212 @@
+/home/henry/Documents/PythonProjects/variable-reap/.venv/lib/python3.12/site-packages/megablocks/grouped_gemm_util.py:10: UserWarning: Grouped GEMM not available.
+  warnings.warn('Grouped GEMM not available.')
+wandb: [wandb.login()] Loaded credentials for https://api.wandb.ai from /home/henry/.netrc.
+wandb: Currently logged in as: hbfreed to https://api.wandb.ai. Use `wandb login --relogin` to force relogin
+wandb: Tracking run with wandb version 0.28.0
+wandb: Run data is saved locally in outputs/healed/grid_general/uniform_keep25_s1224/wandb/run-20260717_174601-ig8p922u
+wandb: Run `wandb offline` to turn off syncing.
+wandb: Syncing run uniform_keep25_s1224
+wandb: ⭐️ View project at https://wandb.ai/hbfreed/glean-general-grid
+wandb: 🚀 View run at https://wandb.ai/hbfreed/glean-general-grid/runs/ig8p922u
+58360 cached top-128 chat trajectories / 22,295,631 unique tokens | 185 steps/epoch | 150 total steps | student params 2.09B | teacher overlap=False
+{"step": 1, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.562714012611409, "tokens": 120000, "cumulative_loss_tokens": 120000, "grad_norm": 14.0625, "lr": 6e-06, "finish_rate": 0.869, "comp_len": 463.3, "t_data_s": 0.2, "t_rollout_s": 0.0, "t_step_s": 38.2, "frames": {"chat": 259}, "mem_gb": 9.93}
+The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.
+[eval step 1] sample: 'To solve the problem, we first treat the matrix as follows:\n\n\\[ [12, -16, 4, 16] \\]\n\\[ [-9, 11, -1, -10] \\]\n\\[ [0, 1, -2, -8] \\]\n\nNext, we treat'
+{"step": 2, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 2.007942330916723, "tokens": 120000, "cumulative_loss_tokens": 240000, "grad_norm": 18.125, "lr": 9e-06, "finish_rate": 0.997, "comp_len": 344.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 38.8, "frames": {"chat": 348}, "mem_gb": 9.59}
+{"step": 3, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.8284729678712786, "tokens": 120000, "cumulative_loss_tokens": 360000, "grad_norm": 14.9375, "lr": 1.2e-05, "finish_rate": 0.997, "comp_len": 324.3, "t_data_s": 0.1, "t_rollout_s": 0.0, "t_step_s": 42.3, "frames": {"chat": 370}, "mem_gb": 9.74}
+{"step": 4, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.139117633214593, "tokens": 120000, "cumulative_loss_tokens": 480000, "grad_norm": 9.25, "lr": 1.5e-05, "finish_rate": 0.852, "comp_len": 491.8, "t_data_s": 0.1, "t_rollout_s": 0.0, "t_step_s": 31.6, "frames": {"chat": 244}, "mem_gb": 9.98}
+{"step": 5, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.4628105710600814, "tokens": 120000, "cumulative_loss_tokens": 600000, "grad_norm": 8.4375, "lr": 1.8e-05, "finish_rate": 0.953, "comp_len": 375.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 41.4, "frames": {"chat": 320}, "mem_gb": 9.9}
+{"step": 6, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.4928981926833589, "tokens": 120000, "cumulative_loss_tokens": 720000, "grad_norm": 6.78125, "lr": 2.1e-05, "finish_rate": 0.997, "comp_len": 317.5, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 45.3, "frames": {"chat": 378}, "mem_gb": 9.5}
+{"step": 7, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.5638923177098234, "tokens": 120000, "cumulative_loss_tokens": 840000, "grad_norm": 4.09375, "lr": 2.4e-05, "finish_rate": 0.997, "comp_len": 312.5, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 48.1, "frames": {"chat": 384}, "mem_gb": 9.99}
+{"step": 8, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.3281159411018093, "tokens": 120000, "cumulative_loss_tokens": 960000, "grad_norm": 4.0, "lr": 2.7000000000000002e-05, "finish_rate": 0.997, "comp_len": 370.4, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 46.3, "frames": {"chat": 324}, "mem_gb": 9.84}
+{"step": 9, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.8018940414384007, "tokens": 120000, "cumulative_loss_tokens": 1080000, "grad_norm": 3.375, "lr": 3e-05, "finish_rate": 0.891, "comp_len": 502.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 37.5, "frames": {"chat": 239}, "mem_gb": 10.0}
+{"step": 10, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.2515785626843572, "tokens": 120000, "cumulative_loss_tokens": 1200000, "grad_norm": 3.578125, "lr": 3e-05, "finish_rate": 0.961, "comp_len": 392.2, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 42.0, "frames": {"chat": 306}, "mem_gb": 9.74}
+[eval step 10] sample: "To solve this problem, we need to determine the rank of the given matrix. The rank of a matrix is defined by the number of non-zero elements in the matrix.\n\nLet's break down the problem:\n\n1. **Matrix*"
+{"step": 11, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.3283900368700425, "tokens": 120000, "cumulative_loss_tokens": 1320000, "grad_norm": 3.671875, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 364.7, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 44.6, "frames": {"chat": 329}, "mem_gb": 9.78}
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+{"step": 13, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.0816897711279492, "tokens": 120000, "cumulative_loss_tokens": 1560000, "grad_norm": 2.109375, "lr": 3e-05, "finish_rate": 0.974, "comp_len": 439.6, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 40.9, "frames": {"chat": 273}, "mem_gb": 9.98}
+{"step": 14, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.6135015807601313, "tokens": 120000, "cumulative_loss_tokens": 1680000, "grad_norm": 1.2578125, "lr": 3e-05, "finish_rate": 0.873, "comp_len": 508.5, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 34.7, "frames": {"chat": 236}, "mem_gb": 9.9}
+{"step": 15, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.3105962486989797, "tokens": 120000, "cumulative_loss_tokens": 1800000, "grad_norm": 1.9609375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 315.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 48.9, "frames": {"chat": 381}, "mem_gb": 9.64}
+{"step": 16, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.6666335937586924, "tokens": 120000, "cumulative_loss_tokens": 1920000, "grad_norm": 1.125, "lr": 3e-05, "finish_rate": 0.925, "comp_len": 451.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 37.0, "frames": {"chat": 266}, "mem_gb": 9.83}
+{"step": 17, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.9004511144821843, "tokens": 120000, "cumulative_loss_tokens": 2040000, "grad_norm": 1.296875, "lr": 3e-05, "finish_rate": 0.961, "comp_len": 389.6, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 43.2, "frames": {"chat": 308}, "mem_gb": 9.82}
+{"step": 18, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.7443993991047144, "tokens": 120000, "cumulative_loss_tokens": 2160000, "grad_norm": 1.1953125, "lr": 3e-05, "finish_rate": 0.853, "comp_len": 439.6, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 38.7, "frames": {"chat": 273}, "mem_gb": 10.0}
+{"step": 19, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.133398624741286, "tokens": 120000, "cumulative_loss_tokens": 2280000, "grad_norm": 1.2734375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 326.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 49.5, "frames": {"chat": 368}, "mem_gb": 9.63}
+{"step": 20, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.2558532213958602, "tokens": 120000, "cumulative_loss_tokens": 2400000, "grad_norm": 1.1953125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 354.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 49.5, "frames": {"chat": 339}, "mem_gb": 9.88}
+[eval step 20] sample: 'To compute the rank of a 4x4 matrix \\( A \\) given by \\([12, -16, 4, 16]\\), \\([-9, 11, -1, -10]\\), \\([0, 1, -2, -2]\\), and \\([0, 4, -8'
+{"step": 21, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.7775650892155865, "tokens": 120000, "cumulative_loss_tokens": 2520000, "grad_norm": 1.0390625, "lr": 3e-05, "finish_rate": 0.963, "comp_len": 402.7, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 41.1, "frames": {"chat": 298}, "mem_gb": 9.8}
+{"step": 22, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.0902612226290007, "tokens": 120000, "cumulative_loss_tokens": 2640000, "grad_norm": 1.1328125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 342.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 48.6, "frames": {"chat": 350}, "mem_gb": 9.53}
+{"step": 23, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.6757159860300521, "tokens": 120000, "cumulative_loss_tokens": 2760000, "grad_norm": 0.9765625, "lr": 3e-05, "finish_rate": 0.858, "comp_len": 487.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 40.2, "frames": {"chat": 246}, "mem_gb": 9.97}
+{"step": 24, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.0760687224897245, "tokens": 120000, "cumulative_loss_tokens": 2880000, "grad_norm": 1.125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 320.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 50.2, "frames": {"chat": 375}, "mem_gb": 9.7}
+{"step": 25, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.0879509686164557, "tokens": 120000, "cumulative_loss_tokens": 3000000, "grad_norm": 1.1640625, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 340.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 49.5, "frames": {"chat": 352}, "mem_gb": 9.76}
+{"step": 26, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.1019300509187082, "tokens": 120000, "cumulative_loss_tokens": 3120000, "grad_norm": 1.09375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 303.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 52.9, "frames": {"chat": 396}, "mem_gb": 9.71}
+{"step": 27, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.9349442116856576, "tokens": 120000, "cumulative_loss_tokens": 3240000, "grad_norm": 1.015625, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 331.5, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 49.7, "frames": {"chat": 362}, "mem_gb": 9.57}
+{"step": 28, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.1320429676694175, "tokens": 120000, "cumulative_loss_tokens": 3360000, "grad_norm": 1.09375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 325.2, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 51.4, "frames": {"chat": 369}, "mem_gb": 9.77}
+{"step": 29, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.1115945567950607, "tokens": 120000, "cumulative_loss_tokens": 3480000, "grad_norm": 1.078125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 300.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 52.4, "frames": {"chat": 399}, "mem_gb": 9.62}
+{"step": 30, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.6293747738944366, "tokens": 120000, "cumulative_loss_tokens": 3600000, "grad_norm": 1.078125, "lr": 3e-05, "finish_rate": 0.911, "comp_len": 485.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 39.4, "frames": {"chat": 247}, "mem_gb": 9.9}
+[eval step 30] sample: 'To compute the rank of a 4x4 matrix, we need to determine the number of linearly independent rows (or equivalently, the number of non-zero rows) in the matrix. The rank of a matrix is given by the num'
+{"step": 31, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.37254366254458826, "tokens": 120000, "cumulative_loss_tokens": 3720000, "grad_norm": 0.765625, "lr": 3e-05, "finish_rate": 0.891, "comp_len": 521.7, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 32.7, "frames": {"chat": 230}, "mem_gb": 9.83}
+{"step": 32, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.5862284223752717, "tokens": 120000, "cumulative_loss_tokens": 3840000, "grad_norm": 0.796875, "lr": 3e-05, "finish_rate": 0.905, "comp_len": 456.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 40.3, "frames": {"chat": 263}, "mem_gb": 10.0}
+{"step": 33, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.606637933870405, "tokens": 120000, "cumulative_loss_tokens": 3960000, "grad_norm": 0.8359375, "lr": 3e-05, "finish_rate": 0.848, "comp_len": 454.5, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 40.1, "frames": {"chat": 264}, "mem_gb": 9.99}
+{"step": 34, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.9400521790258587, "tokens": 120000, "cumulative_loss_tokens": 4080000, "grad_norm": 0.96875, "lr": 3e-05, "finish_rate": 0.991, "comp_len": 365.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 51.7, "frames": {"chat": 328}, "mem_gb": 9.85}
+{"step": 35, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.5495978371700893, "tokens": 120000, "cumulative_loss_tokens": 4200000, "grad_norm": 0.77734375, "lr": 3e-05, "finish_rate": 0.925, "comp_len": 427.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 44.0, "frames": {"chat": 281}, "mem_gb": 9.93}
+{"step": 36, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.4820209278317789, "tokens": 120000, "cumulative_loss_tokens": 4320000, "grad_norm": 0.765625, "lr": 3e-05, "finish_rate": 0.861, "comp_len": 506.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 38.4, "frames": {"chat": 237}, "mem_gb": 9.92}
+{"step": 37, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.5574739918556064, "tokens": 120000, "cumulative_loss_tokens": 4440000, "grad_norm": 0.765625, "lr": 3e-05, "finish_rate": 0.91, "comp_len": 431.7, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 40.6, "frames": {"chat": 278}, "mem_gb": 10.0}
+{"step": 38, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.9362318514594188, "tokens": 120000, "cumulative_loss_tokens": 4560000, "grad_norm": 0.96484375, "lr": 3e-05, "finish_rate": 0.955, "comp_len": 387.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 45.5, "frames": {"chat": 310}, "mem_gb": 9.88}
+{"step": 39, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.6705026273014024, "tokens": 120000, "cumulative_loss_tokens": 4680000, "grad_norm": 2.421875, "lr": 3e-05, "finish_rate": 0.959, "comp_len": 406.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 42.8, "frames": {"chat": 295}, "mem_gb": 9.81}
+{"step": 40, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.7008066074669361, "tokens": 120000, "cumulative_loss_tokens": 4800000, "grad_norm": 0.94921875, "lr": 3e-05, "finish_rate": 0.957, "comp_len": 364.7, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 44.9, "frames": {"chat": 329}, "mem_gb": 9.83}
+[eval step 40] sample: "To compute the rank of a matrix, we need to find the maximum number of linearly independent rows or columns in the matrix. Let's represent the given 4x4 matrix as follows:\n\n\\[\nA = \\begin{bmatrix}\n12 &"
+{"step": 41, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.6580265941616148, "tokens": 120000, "cumulative_loss_tokens": 4920000, "grad_norm": 0.82421875, "lr": 3e-05, "finish_rate": 0.905, "comp_len": 456.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 42.6, "frames": {"chat": 263}, "mem_gb": 9.94}
+{"step": 42, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.6893753549223145, "tokens": 120000, "cumulative_loss_tokens": 5040000, "grad_norm": 0.80078125, "lr": 3e-05, "finish_rate": 0.981, "comp_len": 381.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 46.3, "frames": {"chat": 315}, "mem_gb": 9.83}
+{"step": 43, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.9123652948531011, "tokens": 120000, "cumulative_loss_tokens": 5160000, "grad_norm": 0.9296875, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 339.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 50.1, "frames": {"chat": 354}, "mem_gb": 9.78}
+{"step": 44, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.4531729037904491, "tokens": 120000, "cumulative_loss_tokens": 5280000, "grad_norm": 0.76171875, "lr": 3e-05, "finish_rate": 0.927, "comp_len": 463.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 39.3, "frames": {"chat": 259}, "mem_gb": 9.95}
+{"step": 45, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.663369363786156, "tokens": 120000, "cumulative_loss_tokens": 5400000, "grad_norm": 0.78125, "lr": 3e-05, "finish_rate": 0.92, "comp_len": 436.4, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 42.7, "frames": {"chat": 275}, "mem_gb": 9.81}
+{"step": 46, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.8982400885956362, "tokens": 120000, "cumulative_loss_tokens": 5520000, "grad_norm": 0.94921875, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 359.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 50.3, "frames": {"chat": 334}, "mem_gb": 9.67}
+{"step": 47, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.9199835396355639, "tokens": 120000, "cumulative_loss_tokens": 5640000, "grad_norm": 0.88671875, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 320.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 52.1, "frames": {"chat": 374}, "mem_gb": 9.53}
+{"step": 48, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.7805620063818991, "tokens": 120000, "cumulative_loss_tokens": 5760000, "grad_norm": 0.86328125, "lr": 3e-05, "finish_rate": 0.974, "comp_len": 385.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 47.2, "frames": {"chat": 311}, "mem_gb": 9.7}
+{"step": 49, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.6897149990312755, "tokens": 120000, "cumulative_loss_tokens": 5880000, "grad_norm": 0.74609375, "lr": 3e-05, "finish_rate": 0.876, "comp_len": 463.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 40.2, "frames": {"chat": 259}, "mem_gb": 10.0}
+{"step": 50, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.8139691390002767, "tokens": 120000, "cumulative_loss_tokens": 6000000, "grad_norm": 0.87109375, "lr": 3e-05, "finish_rate": 0.944, "comp_len": 393.4, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 47.0, "frames": {"chat": 305}, "mem_gb": 9.99}
+[eval step 50] sample: 'To determine the rank of a matrix, we need to find the maximum number of linearly independent rows or columns. A matrix is rank-one if its rows and columns are linearly independent.\n\nGiven the matrix:'
+{"step": 51, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.7557955997183919, "tokens": 120000, "cumulative_loss_tokens": 6120000, "grad_norm": 0.8125, "lr": 3e-05, "finish_rate": 0.946, "comp_len": 402.7, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 46.9, "frames": {"chat": 298}, "mem_gb": 9.96}
+{"step": 52, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.9865281463935971, "tokens": 120000, "cumulative_loss_tokens": 6240000, "grad_norm": 0.93359375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 329.7, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 54.4, "frames": {"chat": 364}, "mem_gb": 9.74}
+{"step": 53, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.5802756533251454, "tokens": 120000, "cumulative_loss_tokens": 6360000, "grad_norm": 0.7421875, "lr": 3e-05, "finish_rate": 0.96, "comp_len": 397.4, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 46.1, "frames": {"chat": 302}, "mem_gb": 9.85}
+{"step": 54, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.9891807129281263, "tokens": 120000, "cumulative_loss_tokens": 6480000, "grad_norm": 0.96484375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 379.7, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 49.1, "frames": {"chat": 316}, "mem_gb": 9.61}
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+[eval step 60] sample: 'To determine the rank of a matrix, we need to find the maximum number of linearly independent rows or columns. A matrix is rank-one if its rows are linearly independent.\n\nGiven the matrix:\n\\[\n\\begin{b'
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+[eval step 70] sample: 'To find the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. \n\nGiven the matrix:\n\\[\n\\begin{bmatrix}\n12 & -16 & 4 & 16 \\\\\n-9 & 11 & -1 & '
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+[eval step 80] sample: 'To find the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. \n\nGiven the matrix:\n\\[\n\\begin{bmatrix}\n12 & -16 & 4 & 16 \\\\\n-9 & 11 & -1 & '
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+[eval step 90] sample: "To find the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns. Here's how we can do it step-by-step:\n\n1. **Convert the matrix to row echelon form (REF)*"
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+[eval step 100] sample: "To find the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns. Here's how we can do it step-by-step:\n\n1. **Convert the matrix to row echelon form (REF)*"
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+[eval step 110] sample: "To find the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. Here's how we can do it step-by-step:\n\n1. **Write down the matrix:**\n   \\[\n"
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+[eval step 120] sample: 'To find the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns. A matrix is said to be of rank \\( r \\) if it has \\( r \\) linearly independent rows or col'
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+[eval step 130] sample: 'To find the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns. A matrix is rank-deficient if its rank is less than the number of rows or columns.\n\nGiven'
+{"step": 131, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.5161035601972291, "tokens": 120000, "cumulative_loss_tokens": 15720000, "grad_norm": 0.6875, "lr": 3e-05, "finish_rate": 0.868, "comp_len": 452.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 31.0, "frames": {"chat": 265}, "mem_gb": 10.0}
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+[eval step 140] sample: "To compute the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. Here's how we can do it step-by-step:\n\n1. **Form the Matrix:**\n   \\[\n   "
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+[eval step 150] sample: "To compute the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. Here's how we can do it step-by-step:\n\n1. **Convert the matrix to a row "
+checkpoint snapshot queued -> outputs/healed/grid_general/uniform_keep25_s1224/step0150
+wandb: updating run metadata
+wandb: uploading summary
+wandb: 
+wandb: Run history:
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+wandb:        forward_topk_kl █▇▅▂▄▂▂▄▄▃▂▃▃▁▄▄▂▃▂▂▂▃▂▃▁▄▁▃▃▃▃▃▂▂▁▂▄▂▃▃
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+wandb:                     lr ▁▂▃▅▅███████████████████████████████████
+wandb:                 mem_gb ▁▇▄▅▁▄▄▂▄▇▅▅▅▁▄▆▇▄▄▇▇▇▄▄▆▂▅▅▆▇▇█▇▆▃▇▄▄▄█
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+wandb:               t_data_s █▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
+wandb:                     +3 ...
+wandb: 
+wandb: Run summary:
+wandb:               comp_len 405.4
+wandb: cumulative_loss_tokens 18000000
+wandb:                  epoch 0
+wandb:            finish_rate 0.97
+wandb:        forward_topk_kl 0.5852
+wandb:              grad_norm 0.70312
+wandb:                     lr 3e-05
+wandb:                 mem_gb 9.88
+wandb:                   step 150
+wandb:               t_data_s 0
+wandb:                     +4 ...
+wandb: 
+wandb: 🚀 View run uniform_keep25_s1224 at: https://wandb.ai/hbfreed/glean-general-grid/runs/ig8p922u
+wandb: ⭐️ View project at: https://wandb.ai/hbfreed/glean-general-grid
+wandb: Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)
+wandb: Find logs at: outputs/healed/grid_general/uniform_keep25_s1224/wandb/run-20260717_174601-ig8p922u/logs

healed/grid_general/uniform_keep25_s1225.console.log

@@ -0,0 +1,212 @@
+/home/henry/Documents/PythonProjects/variable-reap/.venv/lib/python3.12/site-packages/megablocks/grouped_gemm_util.py:10: UserWarning: Grouped GEMM not available.
+  warnings.warn('Grouped GEMM not available.')
+wandb: [wandb.login()] Loaded credentials for https://api.wandb.ai from /home/henry/.netrc.
+wandb: Currently logged in as: hbfreed to https://api.wandb.ai. Use `wandb login --relogin` to force relogin
+wandb: Tracking run with wandb version 0.28.0
+wandb: Run data is saved locally in outputs/healed/grid_general/uniform_keep25_s1225/wandb/run-20260717_172920-exrn8c9o
+wandb: Run `wandb offline` to turn off syncing.
+wandb: Syncing run uniform_keep25_s1225
+wandb: ⭐️ View project at https://wandb.ai/hbfreed/glean-general-grid
+wandb: 🚀 View run at https://wandb.ai/hbfreed/glean-general-grid/runs/exrn8c9o
+58360 cached top-128 chat trajectories / 22,295,631 unique tokens | 185 steps/epoch | 150 total steps | student params 2.09B | teacher overlap=False
+{"step": 1, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 2.0794557246148586, "tokens": 120000, "cumulative_loss_tokens": 120000, "grad_norm": 19.25, "lr": 6e-06, "finish_rate": 0.997, "comp_len": 362.5, "t_data_s": 0.1, "t_rollout_s": 0.0, "t_step_s": 44.9, "frames": {"chat": 331}, "mem_gb": 9.8}
+The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.
+[eval step 1] sample: 'To solve the problem, follow these steps:\n\n1. **Calculate the initial savings**: \n   Given $50,000 initial savings, Jamie starts with $2,500 each month. \n   \\[ 50,000 = 2,500 \\]\n   \\[ \\text{Initial sa'
+{"step": 2, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.9382405855561295, "tokens": 120000, "cumulative_loss_tokens": 240000, "grad_norm": 17.375, "lr": 9e-06, "finish_rate": 0.997, "comp_len": 350.9, "t_data_s": 0.1, "t_rollout_s": 0.0, "t_step_s": 39.4, "frames": {"chat": 342}, "mem_gb": 9.62}
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+{"step": 4, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.7511012202739715, "tokens": 120000, "cumulative_loss_tokens": 480000, "grad_norm": 12.25, "lr": 1.5e-05, "finish_rate": 0.997, "comp_len": 348.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 42.4, "frames": {"chat": 344}, "mem_gb": 9.75}
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+[eval step 10] sample: "To solve this problem, we need to determine how many months it will take to reach Jamie's goal of $1,000,000.\n\nFirst, let's calculate the total amount of money Jamie will save from their salary.\n\nJami"
+{"step": 11, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.1225586707924804, "tokens": 120000, "cumulative_loss_tokens": 1320000, "grad_norm": 2.703125, "lr": 3e-05, "finish_rate": 0.959, "comp_len": 377.4, "t_data_s": 0.1, "t_rollout_s": 0.0, "t_step_s": 38.1, "frames": {"chat": 318}, "mem_gb": 9.91}
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+[eval step 20] sample: 'To solve this problem, we need to determine how many months it will take Jamie to reach their goal of $1,000,000.\n\n1. **Initial Savings:**\n   Jamie starts with $50,000 in savings.\n\n2. **Saving Rate:**'
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+[eval step 30] sample: 'To solve this problem, we need to determine how many months it will take Jamie to reach their goal of $1,000,000.\n\n1. **Calculate the total amount of savings:**\n   Jamie starts with $50,000 and can sa'
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+[eval step 40] sample: 'To solve this problem, we need to determine how many months it will take Jamie to reach their goal of $1,000,000.\n\n1. **Initial Savings:**\n   Jamie starts with $50,000 in savings.\n\n2. **Saving Calcula'
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+{"step": 149, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.7678621418327093, "tokens": 120000, "cumulative_loss_tokens": 17880000, "grad_norm": 0.8046875, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 334.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 39.4, "frames": {"chat": 359}, "mem_gb": 9.69}
+{"step": 150, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.7568772708850602, "tokens": 120000, "cumulative_loss_tokens": 18000000, "grad_norm": 0.75390625, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 347.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 40.2, "frames": {"chat": 345}, "mem_gb": 9.75}
+[eval step 150] sample: 'To solve this problem, we need to determine how many months it will take Jamie to save $1,000,000.\n\n**Step 1: Calculate the total amount Jamie will save.**\n\nJamie starts with $50,000 in savings. They '
+checkpoint snapshot queued -> outputs/healed/grid_general/uniform_keep25_s1225/step0150
+wandb: updating run metadata
+wandb: uploading output.log; uploading wandb-summary.json; uploading config.yaml
+wandb: 
+wandb: Run history:
+wandb:               comp_len ▄▃▃▃▆█▄▅▇▆▄▇▇▁▅▂▆▆▄▂▅▃▅▃▃▃▅▅▅▃▃▃▆▇▄▇▄▃▃▃
+wandb: cumulative_loss_tokens ▁▁▁▁▂▂▂▂▂▃▃▃▃▃▃▃▄▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇██
+wandb:                  epoch ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
+wandb:            finish_rate ▇▇▅██▇█▅▄█▅▄██▂▄▇▆███▆▅▁▇█▃█▇▇▆██▇▇▃▇▅▄█
+wandb:        forward_topk_kl █▃▃▂▃▄▄▂▂▂▂▁▃▄▃▃▃▃▂▂▃▂▃▃▂▁▂▂▃▃▁▂▃▃▂▁▃▃▂▃
+wandb:              grad_norm ██▂▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
+wandb:                     lr ▁▅██████████████████████████████████████
+wandb:                 mem_gb ▄▂▆▇▆▄▆▇▅▄▂▃▇▂▅▂▅▁▄▄▄▇▇▄▄▆▅▄▇▄▆▂▃▇▄▅▄█▄▃
+wandb:                   step ▁▁▁▁▁▂▂▂▃▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▆▆▆▇▇▇▇▇█
+wandb:               t_data_s █▁▁█▁▁▁▁▁█▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
+wandb:                     +3 ...
+wandb: 
+wandb: Run summary:
+wandb:               comp_len 347.8
+wandb: cumulative_loss_tokens 18000000
+wandb:                  epoch 0
+wandb:            finish_rate 0.997
+wandb:        forward_topk_kl 0.75688
+wandb:              grad_norm 0.75391
+wandb:                     lr 3e-05
+wandb:                 mem_gb 9.75
+wandb:                   step 150
+wandb:               t_data_s 0
+wandb:                     +4 ...
+wandb: 
+wandb: 🚀 View run uniform_keep25_s1225 at: https://wandb.ai/hbfreed/glean-general-grid/runs/exrn8c9o
+wandb: ⭐️ View project at: https://wandb.ai/hbfreed/glean-general-grid
+wandb: Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)
+wandb: Find logs at: outputs/healed/grid_general/uniform_keep25_s1225/wandb/run-20260717_172920-exrn8c9o/logs

healed/grid_general/uniform_keep25_s1226.console.log

@@ -0,0 +1,213 @@
+/home/henry/Documents/PythonProjects/variable-reap/.venv/lib/python3.12/site-packages/megablocks/grouped_gemm_util.py:10: UserWarning: Grouped GEMM not available.
+  warnings.warn('Grouped GEMM not available.')
+wandb: [wandb.login()] Loaded credentials for https://api.wandb.ai from /home/henry/.netrc.
+wandb: Currently logged in as: hbfreed to https://api.wandb.ai. Use `wandb login --relogin` to force relogin
+wandb: setting up run 35vquuvg
+wandb: Tracking run with wandb version 0.28.0
+wandb: Run data is saved locally in outputs/healed/grid_general/uniform_keep25_s1226/wandb/run-20260717_172747-35vquuvg
+wandb: Run `wandb offline` to turn off syncing.
+wandb: Syncing run uniform_keep25_s1226
+wandb: ⭐️ View project at https://wandb.ai/hbfreed/glean-general-grid
+wandb: 🚀 View run at https://wandb.ai/hbfreed/glean-general-grid/runs/35vquuvg
+58360 cached top-128 chat trajectories / 22,295,631 unique tokens | 185 steps/epoch | 150 total steps | student params 2.09B | teacher overlap=False
+{"step": 1, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.6198377058381837, "tokens": 120000, "cumulative_loss_tokens": 120000, "grad_norm": 14.1875, "lr": 6e-06, "finish_rate": 0.945, "comp_len": 389.6, "t_data_s": 0.2, "t_rollout_s": 0.0, "t_step_s": 42.4, "frames": {"chat": 308}, "mem_gb": 9.9}
+The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.
+[eval step 1] sample: "To solve the problem, first, let's express the arithmetic sequence $\\{a_n\\}$ in terms. The equation given is: $a_1 + 3a_8 + a_{15} = 120$. \n\nWe can start by isolating $a_1$ and $a_{15}$ separately:"
+{"step": 2, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.1278039398397008, "tokens": 120000, "cumulative_loss_tokens": 240000, "grad_norm": 11.1875, "lr": 9e-06, "finish_rate": 0.754, "comp_len": 568.7, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 27.8, "frames": {"chat": 211}, "mem_gb": 10.0}
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+[eval step 10] sample: "To solve this problem, we need to find the value of \\(a_9 - a_{10}\\) given the arithmetic sequence \\(a_1 + 3a_8 + a_{15} = 120\\).\n\nFirst, let's express the terms of the arithmetic sequence \\(a_1 + 3a"
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+[eval step 20] sample: 'to solve the problem, we need to find the value of $2a_9 - a_{10}$ given the arithmetic sequence $\\{a_n, a_{n+1} = a_n, a_{n+2} = a_{n+1}, a_{n+3} = a_{'
+{"step": 21, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.48773726695353786, "tokens": 120000, "cumulative_loss_tokens": 2520000, "grad_norm": 1.140625, "lr": 3e-05, "finish_rate": 0.889, "comp_len": 491.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 30.0, "frames": {"chat": 244}, "mem_gb": 10.0}
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+{"step": 29, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.0916470245361327, "tokens": 120000, "cumulative_loss_tokens": 3480000, "grad_norm": 1.1015625, "lr": 3e-05, "finish_rate": 0.991, "comp_len": 347.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 38.4, "frames": {"chat": 345}, "mem_gb": 9.73}
+{"step": 30, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.6021138668128599, "tokens": 120000, "cumulative_loss_tokens": 3600000, "grad_norm": 0.8125, "lr": 3e-05, "finish_rate": 0.897, "comp_len": 458.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 32.6, "frames": {"chat": 262}, "mem_gb": 9.91}
+[eval step 30] sample: 'to solve the problem, we need to find the value of \\(2a_9 - a_{10}\\) given the arithmetic sequence \\(\\{a_n, a_{n+1} = a_n, a_{n+2} = a_{n+1}\\}\\).\n\nthe sequence'
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+{"step": 33, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.7716704062253237, "tokens": 120000, "cumulative_loss_tokens": 3960000, "grad_norm": 0.87890625, "lr": 3e-05, "finish_rate": 0.969, "comp_len": 367.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 36.8, "frames": {"chat": 327}, "mem_gb": 9.85}
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+[eval step 40] sample: 'to solve the problem, we need to understand the relationship between the terms of the arithmetic sequence and the given equation. the arithmetic sequence is defined by the sum of the first and the eig'
+{"step": 41, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.7716472785164913, "tokens": 120000, "cumulative_loss_tokens": 4920000, "grad_norm": 0.8359375, "lr": 3e-05, "finish_rate": 0.981, "comp_len": 377.4, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 37.4, "frames": {"chat": 318}, "mem_gb": 9.83}
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+[eval step 50] sample: 'in the arithmetic sequence {a_n}, if a_1 + 3a_8 + a_{15} = 120, we need to find the value of a_9 - a_{10}. to solve this, we first express the sequence in terms of the common difference of the first a'
+{"step": 51, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.5774158618660022, "tokens": 120000, "cumulative_loss_tokens": 6120000, "grad_norm": 0.73828125, "lr": 3e-05, "finish_rate": 0.914, "comp_len": 446.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 31.8, "frames": {"chat": 269}, "mem_gb": 9.97}
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+{"step": 59, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.6659456232734025, "tokens": 120000, "cumulative_loss_tokens": 7080000, "grad_norm": 0.765625, "lr": 3e-05, "finish_rate": 0.953, "comp_len": 401.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 35.4, "frames": {"chat": 299}, "mem_gb": 9.79}
+{"step": 60, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.9349627352940539, "tokens": 120000, "cumulative_loss_tokens": 7200000, "grad_norm": 0.875, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 362.5, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 40.5, "frames": {"chat": 331}, "mem_gb": 9.67}
+[eval step 60] sample: 'in the arithmetic sequence {a_n}, if a_1 + 3a_8 + a_{15} = 120, we can find the value of a_9 - a_10. to solve this, we first express a_9 and a_10 in terms of a_n:\n\na'
+{"step": 61, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.5694986918556193, "tokens": 120000, "cumulative_loss_tokens": 7320000, "grad_norm": 0.671875, "lr": 3e-05, "finish_rate": 0.954, "comp_len": 428.6, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 33.2, "frames": {"chat": 280}, "mem_gb": 9.88}
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+{"step": 63, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.9245277111411094, "tokens": 120000, "cumulative_loss_tokens": 7560000, "grad_norm": 0.9140625, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 373.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 38.5, "frames": {"chat": 321}, "mem_gb": 9.63}
+{"step": 64, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.5552141196810951, "tokens": 120000, "cumulative_loss_tokens": 7680000, "grad_norm": 0.6875, "lr": 3e-05, "finish_rate": 0.943, "comp_len": 427.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 34.1, "frames": {"chat": 281}, "mem_gb": 9.93}
+{"step": 65, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.9923324734320244, "tokens": 120000, "cumulative_loss_tokens": 7800000, "grad_norm": 0.84375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 362.5, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 39.3, "frames": {"chat": 331}, "mem_gb": 9.61}
+{"step": 66, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.7915697015117854, "tokens": 120000, "cumulative_loss_tokens": 7920000, "grad_norm": 0.80859375, "lr": 3e-05, "finish_rate": 0.956, "comp_len": 377.4, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 37.1, "frames": {"chat": 318}, "mem_gb": 9.98}
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+{"step": 69, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.4627537495482713, "tokens": 120000, "cumulative_loss_tokens": 8280000, "grad_norm": 0.76171875, "lr": 3e-05, "finish_rate": 0.921, "comp_len": 430.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 33.2, "frames": {"chat": 279}, "mem_gb": 9.9}
+{"step": 70, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.3126349515615652, "tokens": 120000, "cumulative_loss_tokens": 8400000, "grad_norm": 0.59765625, "lr": 3e-05, "finish_rate": 0.915, "comp_len": 483.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 30.0, "frames": {"chat": 248}, "mem_gb": 9.91}
+[eval step 70] sample: 'to solve the problem, we need to understand the arithmetic sequence and the given equation. the arithmetic sequence is defined as:\n\n\\[ \\{a_n\\} = \\{a_1, a_2, \\ldots, a_n\\} \\]\n\ngiven that:\n\n\\[ a_1 + 3'
+{"step": 71, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.7028052366066724, "tokens": 120000, "cumulative_loss_tokens": 8520000, "grad_norm": 0.78515625, "lr": 3e-05, "finish_rate": 0.907, "comp_len": 447.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 34.4, "frames": {"chat": 268}, "mem_gb": 9.88}
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+{"step": 74, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.5234173207546895, "tokens": 120000, "cumulative_loss_tokens": 8880000, "grad_norm": 0.625, "lr": 3e-05, "finish_rate": 0.887, "comp_len": 483.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 32.2, "frames": {"chat": 248}, "mem_gb": 10.01}
+{"step": 75, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.7484029913706084, "tokens": 120000, "cumulative_loss_tokens": 9000000, "grad_norm": 0.7734375, "lr": 3e-05, "finish_rate": 0.988, "comp_len": 368.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 36.9, "frames": {"chat": 326}, "mem_gb": 9.91}
+{"step": 76, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.6783317150050153, "tokens": 120000, "cumulative_loss_tokens": 9120000, "grad_norm": 0.76171875, "lr": 3e-05, "finish_rate": 0.984, "comp_len": 382.2, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 36.7, "frames": {"chat": 314}, "mem_gb": 9.67}
+{"step": 77, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.32216203611418603, "tokens": 120000, "cumulative_loss_tokens": 9240000, "grad_norm": 0.609375, "lr": 3e-05, "finish_rate": 0.828, "comp_len": 517.2, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 30.8, "frames": {"chat": 232}, "mem_gb": 10.0}
+{"step": 78, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.8948443909298629, "tokens": 120000, "cumulative_loss_tokens": 9360000, "grad_norm": 0.80859375, "lr": 3e-05, "finish_rate": 0.994, "comp_len": 363.6, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 38.7, "frames": {"chat": 330}, "mem_gb": 9.73}
+{"step": 79, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.5822775135998924, "tokens": 120000, "cumulative_loss_tokens": 9480000, "grad_norm": 0.72265625, "lr": 3e-05, "finish_rate": 0.901, "comp_len": 411.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 35.6, "frames": {"chat": 292}, "mem_gb": 9.88}
+{"step": 80, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.8399514859688779, "tokens": 120000, "cumulative_loss_tokens": 9600000, "grad_norm": 0.87109375, "lr": 3e-05, "finish_rate": 0.978, "comp_len": 384.6, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 36.7, "frames": {"chat": 312}, "mem_gb": 9.91}
+[eval step 80] sample: 'to solve the problem, we need to understand the arithmetic sequence and the given equation. the arithmetic sequence is defined as:\n\n\\[ \\{a_n\\} = \\{a_1, a_2, \\ldots, a_n\\} \\]\n\nthe given equation is:\n\n\\'
+{"step": 81, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.9575093897075703, "tokens": 120000, "cumulative_loss_tokens": 9720000, "grad_norm": 0.93359375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 314.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 44.3, "frames": {"chat": 382}, "mem_gb": 9.71}
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+{"step": 83, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.25758756727054716, "tokens": 120000, "cumulative_loss_tokens": 9960000, "grad_norm": 0.54296875, "lr": 3e-05, "finish_rate": 0.824, "comp_len": 540.5, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 28.8, "frames": {"chat": 222}, "mem_gb": 9.85}
+{"step": 84, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.8978787859870742, "tokens": 120000, "cumulative_loss_tokens": 10080000, "grad_norm": 0.82421875, "lr": 3e-05, "finish_rate": 0.977, "comp_len": 350.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 42.1, "frames": {"chat": 342}, "mem_gb": 9.9}
+{"step": 85, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.8793410871063669, "tokens": 120000, "cumulative_loss_tokens": 10200000, "grad_norm": 0.78125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 356.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 38.7, "frames": {"chat": 337}, "mem_gb": 9.7}
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+[eval step 90] sample: 'to solve the problem, we need to follow these steps:\n\n1. **understand the arithmetic sequence:**\n   the arithmetic sequence is defined by the formula:\n   \\[\n   a_n = a_1 + 3a_n\n   \\]\n\n2. **use the giv'
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+[eval step 100] sample: 'to solve the problem, we need to understand the relationship between the terms of the arithmetic sequence. the general formula for the nth term of an arithmetic sequence is given by:\n\n\\[ a_n = a_1 + ('
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+[eval step 110] sample: 'to solve the problem, we need to understand the relationship between the terms of the arithmetic sequence. the general formula for the nth term of an arithmetic sequence is given by:\n\n\\[ a_n = a_1 + ('
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+[eval step 120] sample: 'to solve the problem, we need to understand the sequence and the given equation. the arithmetic sequence is defined by the first term \\(a_1\\) and the common difference \\(d\\). the general term \\(a_n\\) '
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+[eval step 130] sample: 'to solve the problem, we need to understand the arithmetic sequence and the given condition. the arithmetic sequence is defined as:\n\n\\[ \\{a_n\\} = \\{a_1, a_2, \\ldots, a_n\\} \\]\n\nthe given condition is:\n'
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+{"step": 140, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.5197104676648975, "tokens": 120000, "cumulative_loss_tokens": 16800000, "grad_norm": 0.6953125, "lr": 3e-05, "finish_rate": 0.889, "comp_len": 428.6, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 33.5, "frames": {"chat": 280}, "mem_gb": 10.03}
+[eval step 140] sample: 'to solve the problem, we need to understand the properties of an arithmetic sequence. in an arithmetic sequence, each term is the sum of the previous two terms, i.e., \\(a_n = a_{n-1} + a_{n-2}\\).\n\ngiv'
+{"step": 141, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.4203244868689527, "tokens": 120000, "cumulative_loss_tokens": 16920000, "grad_norm": 1.2578125, "lr": 3e-05, "finish_rate": 0.884, "comp_len": 434.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 33.7, "frames": {"chat": 276}, "mem_gb": 9.87}
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+{"step": 143, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.8516633477176229, "tokens": 120000, "cumulative_loss_tokens": 17160000, "grad_norm": 0.84375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 333.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 39.2, "frames": {"chat": 360}, "mem_gb": 9.54}
+{"step": 144, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.813726552273954, "tokens": 120000, "cumulative_loss_tokens": 17280000, "grad_norm": 0.8203125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 328.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 43.5, "frames": {"chat": 365}, "mem_gb": 9.72}
+{"step": 145, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.6931645369105041, "tokens": 120000, "cumulative_loss_tokens": 17400000, "grad_norm": 0.70703125, "lr": 3e-05, "finish_rate": 0.988, "comp_len": 355.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 37.3, "frames": {"chat": 338}, "mem_gb": 9.81}
+{"step": 146, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.4358943793937564, "tokens": 120000, "cumulative_loss_tokens": 17520000, "grad_norm": 0.6640625, "lr": 3e-05, "finish_rate": 0.888, "comp_len": 434.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 34.9, "frames": {"chat": 276}, "mem_gb": 9.91}
+{"step": 147, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.27220238340807457, "tokens": 120000, "cumulative_loss_tokens": 17640000, "grad_norm": 0.5625, "lr": 3e-05, "finish_rate": 0.761, "comp_len": 574.2, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 28.4, "frames": {"chat": 209}, "mem_gb": 9.99}
+{"step": 148, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.8054786739685883, "tokens": 120000, "cumulative_loss_tokens": 17760000, "grad_norm": 0.84375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 316.6, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 42.3, "frames": {"chat": 379}, "mem_gb": 9.59}
+{"step": 149, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.7144403724439442, "tokens": 120000, "cumulative_loss_tokens": 17880000, "grad_norm": 0.77734375, "lr": 3e-05, "finish_rate": 0.928, "comp_len": 390.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 37.3, "frames": {"chat": 307}, "mem_gb": 9.93}
+{"step": 150, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.5410168964619437, "tokens": 120000, "cumulative_loss_tokens": 18000000, "grad_norm": 0.69140625, "lr": 3e-05, "finish_rate": 0.942, "comp_len": 409.6, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 36.0, "frames": {"chat": 293}, "mem_gb": 9.97}
+[eval step 150] sample: 'to solve the problem, we need to understand the properties of an arithmetic sequence. in an arithmetic sequence, each term is the sum of the previous two terms, i.e., \\(a_n = a_{n-1} + a_{n-2}\\).\n\ngiv'
+checkpoint snapshot queued -> outputs/healed/grid_general/uniform_keep25_s1226/step0150
+wandb: updating run metadata
+wandb: uploading output.log; uploading wandb-summary.json; uploading config.yaml
+wandb: 
+wandb: Run history:
+wandb:               comp_len ▄█▃▁▂▄▇▃▄▂▅▂▅▇▃▄▆▃▅▄▃▄▃▃▂▄▂▅▂▃▂▃▁▄▅▄▆▁█▁
+wandb: cumulative_loss_tokens ▁▁▁▁▂▂▂▂▂▃▃▄▄▄▄▅▅▅▅▅▅▅▅▅▆▆▆▆▆▆▇▇▇▇▇▇████
+wandb:                  epoch ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
+wandb:            finish_rate ▆█▄███▅█▇█▄▇█▇▆█▆▇▆▅▇▇█▇▆▆▅▃███▇▆█▆▅█▇▆▁
+wandb:        forward_topk_kl █▄▃▅▅▃▅▄▅▃▄▃▃▅▂▃▄▄▁▃▁▂▄▄▂▃▁▃▄▃▄▂▄▃▃▃▁▂▃▃
+wandb:              grad_norm █▆▃▂▂▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
+wandb:                     lr ▁▃██████████████████████████████████████
+wandb:                 mem_gb █▇▃▃▃▄▇█▄▇▇▇▃▅▃▃▃▆█▆▆▆▆▄▂▃▂▅▅▆▄▄▅▅█▁▆▅▇▇
+wandb:                   step ▁▁▁▂▂▂▂▂▂▂▃▃▃▃▃▄▄▄▄▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇█████
+wandb:               t_data_s ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
+wandb:                     +3 ...
+wandb: 
+wandb: Run summary:
+wandb:               comp_len 409.6
+wandb: cumulative_loss_tokens 18000000
+wandb:                  epoch 0
+wandb:            finish_rate 0.942
+wandb:        forward_topk_kl 0.54102
+wandb:              grad_norm 0.69141
+wandb:                     lr 3e-05
+wandb:                 mem_gb 9.97
+wandb:                   step 150
+wandb:               t_data_s 0
+wandb:                     +4 ...
+wandb: 
+wandb: 🚀 View run uniform_keep25_s1226 at: https://wandb.ai/hbfreed/glean-general-grid/runs/35vquuvg
+wandb: ⭐️ View project at: https://wandb.ai/hbfreed/glean-general-grid
+wandb: Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)
+wandb: Find logs at: outputs/healed/grid_general/uniform_keep25_s1226/wandb/run-20260717_172747-35vquuvg/logs

healed/grid_general/uniform_keep50_s1224.console.log

@@ -0,0 +1,215 @@
+/home/henry/Documents/PythonProjects/variable-reap/.venv/lib/python3.12/site-packages/megablocks/grouped_gemm_util.py:10: UserWarning: Grouped GEMM not available.
+  warnings.warn('Grouped GEMM not available.')
+wandb: [wandb.login()] Loaded credentials for https://api.wandb.ai from /home/henry/.netrc.
+wandb: Currently logged in as: hbfreed to https://api.wandb.ai. Use `wandb login --relogin` to force relogin
+wandb: Tracking run with wandb version 0.28.0
+wandb: Run data is saved locally in outputs/healed/grid_general/uniform_keep50_s1224/wandb/run-20260718_134414-osqsd9b4
+wandb: Run `wandb offline` to turn off syncing.
+wandb: Syncing run uniform_keep50_s1224
+wandb: ⭐️ View project at https://wandb.ai/hbfreed/glean-general-grid
+wandb: 🚀 View run at https://wandb.ai/hbfreed/glean-general-grid/runs/osqsd9b4
+
+58360 cached top-128 chat trajectories / 22,295,631 unique tokens | 185 steps/epoch | 150 total steps | student params 3.70B | teacher overlap=False
+{"step": 1, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.6917759428766866, "tokens": 120000, "cumulative_loss_tokens": 120000, "grad_norm": 6.5, "lr": 6e-06, "finish_rate": 0.869, "comp_len": 463.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 47.0, "frames": {"chat": 259}, "mem_gb": 15.93}
+The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.
+[eval step 1] sample: 'To compute the rank of the given 4x4 matrix, we first determine the number of elements (rows) and columns. The matrix given is:\n\n\\[\n\\begin{bmatrix}\n12 & -16 & 4 & 16 \\\\\n-9 & 11 & -1 & -10 \\\\\n0 &'
+{"step": 2, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.9499301103445391, "tokens": 120000, "cumulative_loss_tokens": 240000, "grad_norm": 8.0625, "lr": 9e-06, "finish_rate": 0.997, "comp_len": 344.8, "t_data_s": 0.1, "t_rollout_s": 0.0, "t_step_s": 52.1, "frames": {"chat": 348}, "mem_gb": 15.64}
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+{"step": 4, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.4464047732328375, "tokens": 120000, "cumulative_loss_tokens": 480000, "grad_norm": 3.46875, "lr": 1.5e-05, "finish_rate": 0.852, "comp_len": 491.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 45.0, "frames": {"chat": 244}, "mem_gb": 16.02}
+{"step": 5, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.6813110845401883, "tokens": 120000, "cumulative_loss_tokens": 600000, "grad_norm": 3.5, "lr": 1.8e-05, "finish_rate": 0.953, "comp_len": 375.0, "t_data_s": 0.1, "t_rollout_s": 0.0, "t_step_s": 57.1, "frames": {"chat": 320}, "mem_gb": 15.94}
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+{"step": 7, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.8012765964987377, "tokens": 120000, "cumulative_loss_tokens": 840000, "grad_norm": 2.5, "lr": 2.4e-05, "finish_rate": 0.997, "comp_len": 312.5, "t_data_s": 0.1, "t_rollout_s": 0.0, "t_step_s": 67.5, "frames": {"chat": 384}, "mem_gb": 16.04}
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+{"step": 9, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.34498201645724474, "tokens": 120000, "cumulative_loss_tokens": 1080000, "grad_norm": 1.4765625, "lr": 3e-05, "finish_rate": 0.891, "comp_len": 502.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 49.4, "frames": {"chat": 239}, "mem_gb": 16.05}
+{"step": 10, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.6355094303450237, "tokens": 120000, "cumulative_loss_tokens": 1200000, "grad_norm": 1.4296875, "lr": 3e-05, "finish_rate": 0.961, "comp_len": 392.2, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 61.5, "frames": {"chat": 306}, "mem_gb": 15.79}
+[eval step 10] sample: "To determine the rank of a given 4x4 matrix, we need to find the maximum number of linearly independent rows or columns in the matrix.\n\nLet's break down the steps:\n\n1. **Identify the Matrix:**\n   \\[\n "
+{"step": 11, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.6753487973206987, "tokens": 120000, "cumulative_loss_tokens": 1320000, "grad_norm": 1.640625, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 364.7, "t_data_s": 0.1, "t_rollout_s": 0.0, "t_step_s": 65.6, "frames": {"chat": 329}, "mem_gb": 15.83}
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+{"step": 19, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.5860139168731868, "tokens": 120000, "cumulative_loss_tokens": 2280000, "grad_norm": 0.8984375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 326.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 65.5, "frames": {"chat": 368}, "mem_gb": 15.68}
+{"step": 20, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.6743619577939312, "tokens": 120000, "cumulative_loss_tokens": 2400000, "grad_norm": 0.9453125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 354.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 65.0, "frames": {"chat": 339}, "mem_gb": 15.93}
+[eval step 20] sample: "To determine the rank of a matrix, we need to find the maximum number of linearly independent rows or columns in the matrix.\n\nLet's start by writing down the rows of the given matrix:\n\n\\[\n\\begin{bmatr"
+{"step": 21, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.3908682078843315, "tokens": 120000, "cumulative_loss_tokens": 2520000, "grad_norm": 0.69921875, "lr": 3e-05, "finish_rate": 0.963, "comp_len": 402.7, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 54.7, "frames": {"chat": 298}, "mem_gb": 15.85}
+{"step": 22, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.5756209870963047, "tokens": 120000, "cumulative_loss_tokens": 2640000, "grad_norm": 0.7890625, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 342.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 69.2, "frames": {"chat": 350}, "mem_gb": 15.57}
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+{"step": 25, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.577202946131366, "tokens": 120000, "cumulative_loss_tokens": 3000000, "grad_norm": 0.80078125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 340.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 63.5, "frames": {"chat": 352}, "mem_gb": 15.8}
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+{"step": 27, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.4940694273682932, "tokens": 120000, "cumulative_loss_tokens": 3240000, "grad_norm": 0.7734375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 331.5, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 65.2, "frames": {"chat": 362}, "mem_gb": 15.61}
+{"step": 28, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.6087611052072918, "tokens": 120000, "cumulative_loss_tokens": 3360000, "grad_norm": 0.796875, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 325.2, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 67.9, "frames": {"chat": 369}, "mem_gb": 15.82}
+{"step": 29, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.6013093366716057, "tokens": 120000, "cumulative_loss_tokens": 3480000, "grad_norm": 0.78515625, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 300.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 68.8, "frames": {"chat": 399}, "mem_gb": 15.67}
+{"step": 30, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.3221179998536284, "tokens": 120000, "cumulative_loss_tokens": 3600000, "grad_norm": 0.96484375, "lr": 3e-05, "finish_rate": 0.911, "comp_len": 485.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 49.2, "frames": {"chat": 247}, "mem_gb": 15.95}
+[eval step 30] sample: "To determine the rank of a matrix, we need to find the maximum number of linearly independent rows or columns in the matrix.\n\nLet's break down the matrix step-by-step:\n\n\\[\n\\begin{bmatrix}\n12 & -16 & 4"
+{"step": 31, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.16846333308269582, "tokens": 120000, "cumulative_loss_tokens": 3720000, "grad_norm": 0.56640625, "lr": 3e-05, "finish_rate": 0.891, "comp_len": 521.7, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 48.8, "frames": {"chat": 230}, "mem_gb": 15.88}
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+{"step": 33, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.31605170328461873, "tokens": 120000, "cumulative_loss_tokens": 3960000, "grad_norm": 0.6484375, "lr": 3e-05, "finish_rate": 0.848, "comp_len": 454.5, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 54.0, "frames": {"chat": 264}, "mem_gb": 16.04}
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+{"step": 35, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.28057213364044825, "tokens": 120000, "cumulative_loss_tokens": 4200000, "grad_norm": 0.57421875, "lr": 3e-05, "finish_rate": 0.925, "comp_len": 427.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 54.4, "frames": {"chat": 281}, "mem_gb": 15.97}
+{"step": 36, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.23167578200120478, "tokens": 120000, "cumulative_loss_tokens": 4320000, "grad_norm": 0.546875, "lr": 3e-05, "finish_rate": 0.861, "comp_len": 506.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 46.7, "frames": {"chat": 237}, "mem_gb": 15.96}
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+{"step": 38, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.5016841662073508, "tokens": 120000, "cumulative_loss_tokens": 4560000, "grad_norm": 0.7265625, "lr": 3e-05, "finish_rate": 0.955, "comp_len": 387.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 59.5, "frames": {"chat": 310}, "mem_gb": 15.92}
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+{"step": 40, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.3634161012196913, "tokens": 120000, "cumulative_loss_tokens": 4800000, "grad_norm": 0.63671875, "lr": 3e-05, "finish_rate": 0.957, "comp_len": 364.7, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 63.9, "frames": {"chat": 329}, "mem_gb": 15.87}
+[eval step 40] sample: 'To compute the rank of a matrix, we can use various methods such as Gaussian elimination, which is a systematic method to reduce a matrix to its row-echelon form (REF) or reduced row-echelon form (RRE'
+{"step": 41, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.33847409060771266, "tokens": 120000, "cumulative_loss_tokens": 4920000, "grad_norm": 0.63671875, "lr": 3e-05, "finish_rate": 0.905, "comp_len": 456.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 58.3, "frames": {"chat": 263}, "mem_gb": 15.99}
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+{"step": 43, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.487745441975072, "tokens": 120000, "cumulative_loss_tokens": 5160000, "grad_norm": 0.69140625, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 339.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 66.4, "frames": {"chat": 354}, "mem_gb": 15.83}
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+{"step": 48, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.4144724924205492, "tokens": 120000, "cumulative_loss_tokens": 5760000, "grad_norm": 0.8125, "lr": 3e-05, "finish_rate": 0.974, "comp_len": 385.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 61.9, "frames": {"chat": 311}, "mem_gb": 15.75}
+{"step": 49, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.360805739873896, "tokens": 120000, "cumulative_loss_tokens": 5880000, "grad_norm": 0.58984375, "lr": 3e-05, "finish_rate": 0.876, "comp_len": 463.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 51.1, "frames": {"chat": 259}, "mem_gb": 16.05}
+{"step": 50, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.4351383126423384, "tokens": 120000, "cumulative_loss_tokens": 6000000, "grad_norm": 0.66015625, "lr": 3e-05, "finish_rate": 0.944, "comp_len": 393.4, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 60.3, "frames": {"chat": 305}, "mem_gb": 16.04}
+[eval step 50] sample: 'To find the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns. A matrix is said to have rank \\( r \\) if it has \\( r \\) linearly independent rows or colu'
+checkpoint snapshot queued -> outputs/healed/grid_general/uniform_keep50_s1224/step0050
+{"step": 51, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.40691574598941954, "tokens": 120000, "cumulative_loss_tokens": 6120000, "grad_norm": 0.6171875, "lr": 3e-05, "finish_rate": 0.946, "comp_len": 402.7, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 53.7, "frames": {"chat": 298}, "mem_gb": 16.01}
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+{"step": 58, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.424083882328247, "tokens": 120000, "cumulative_loss_tokens": 6960000, "grad_norm": 0.640625, "lr": 3e-05, "finish_rate": 0.968, "comp_len": 348.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 64.7, "frames": {"chat": 344}, "mem_gb": 15.94}
+{"step": 59, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.4671809927133843, "tokens": 120000, "cumulative_loss_tokens": 7080000, "grad_norm": 0.65625, "lr": 3e-05, "finish_rate": 0.992, "comp_len": 339.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 69.4, "frames": {"chat": 353}, "mem_gb": 15.61}
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+[eval step 60] sample: 'To find the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns. A matrix is said to be of rank \\( r \\) if the number of linearly independent rows or colu'
+{"step": 61, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.5186026561640203, "tokens": 120000, "cumulative_loss_tokens": 7320000, "grad_norm": 0.7109375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 378.5, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 58.8, "frames": {"chat": 317}, "mem_gb": 15.79}
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+[eval step 70] sample: "To find the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. Let's represent the given 4x4 matrix as follows:\n\n\\[\nA = \\begin{bmatrix}\n12"
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+[eval step 80] sample: "To find the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. \n\nLet's represent the given 4x4 matrix:\n\n\\[\n\\begin{bmatrix}\n12 & -16 & 4 & "
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+[eval step 90] sample: "To find the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns. Here's how we can do it step-by-step:\n\n1. **Write down the matrix:**\n   \\[\n   \\begin{bmat"
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+[eval step 100] sample: 'To find the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns. A matrix is said to be of rank \\( r \\) if it has \\( r \\) linearly independent rows or col'
+checkpoint snapshot queued -> outputs/healed/grid_general/uniform_keep50_s1224/step0100
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+[eval step 110] sample: "To find the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns. In this case, we have a 4x4 matrix.\n\nLet's represent the matrix as follows:\n\n\\[\nA = \\begi"
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+[eval step 120] sample: 'To find the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns. A matrix is rank-deficient if it has fewer linearly independent rows or columns than its '
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+{"step": 129, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.3810055477776254, "tokens": 120000, "cumulative_loss_tokens": 15480000, "grad_norm": 0.62109375, "lr": 3e-05, "finish_rate": 0.974, "comp_len": 387.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 46.7, "frames": {"chat": 310}, "mem_gb": 15.9}
+{"step": 130, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.275449989147267, "tokens": 120000, "cumulative_loss_tokens": 15600000, "grad_norm": 0.546875, "lr": 3e-05, "finish_rate": 0.834, "comp_len": 463.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 40.9, "frames": {"chat": 259}, "mem_gb": 16.08}
+[eval step 130] sample: "To find the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. \n\nLet's represent the given 4x4 matrix as follows:\n\n\\[\nA = \\begin{bmatrix}\n"
+{"step": 131, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.27469143630973997, "tokens": 120000, "cumulative_loss_tokens": 15720000, "grad_norm": 0.51953125, "lr": 3e-05, "finish_rate": 0.868, "comp_len": 452.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 39.5, "frames": {"chat": 265}, "mem_gb": 16.05}
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+{"step": 133, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.2858842681933194, "tokens": 120000, "cumulative_loss_tokens": 15960000, "grad_norm": 0.5390625, "lr": 3e-05, "finish_rate": 0.972, "comp_len": 413.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 43.2, "frames": {"chat": 290}, "mem_gb": 15.86}
+{"step": 134, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.42380685371415067, "tokens": 120000, "cumulative_loss_tokens": 16080000, "grad_norm": 0.65234375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 343.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 49.5, "frames": {"chat": 349}, "mem_gb": 15.79}
+{"step": 135, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.2827025428744033, "tokens": 120000, "cumulative_loss_tokens": 16200000, "grad_norm": 0.5234375, "lr": 3e-05, "finish_rate": 0.928, "comp_len": 412.4, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 42.6, "frames": {"chat": 291}, "mem_gb": 15.89}
+{"step": 136, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.44822633866084116, "tokens": 120000, "cumulative_loss_tokens": 16320000, "grad_norm": 0.67578125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 350.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 49.2, "frames": {"chat": 342}, "mem_gb": 15.76}
+{"step": 137, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.49938472879038504, "tokens": 120000, "cumulative_loss_tokens": 16440000, "grad_norm": 0.67578125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 355.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 47.4, "frames": {"chat": 338}, "mem_gb": 15.7}
+{"step": 138, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.492944224542814, "tokens": 120000, "cumulative_loss_tokens": 16560000, "grad_norm": 0.6875, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 337.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 52.2, "frames": {"chat": 356}, "mem_gb": 15.67}
+{"step": 139, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.2609467014649262, "tokens": 120000, "cumulative_loss_tokens": 16680000, "grad_norm": 0.5390625, "lr": 3e-05, "finish_rate": 0.86, "comp_len": 452.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 44.1, "frames": {"chat": 265}, "mem_gb": 16.05}
+{"step": 140, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.4059377191117654, "tokens": 120000, "cumulative_loss_tokens": 16800000, "grad_norm": 0.62890625, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 339.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 51.3, "frames": {"chat": 353}, "mem_gb": 15.76}
+[eval step 140] sample: "To compute the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. Here's how we can do it step-by-step:\n\n1. **Write down the matrix:**\n   "
+{"step": 141, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.3910453383545081, "tokens": 120000, "cumulative_loss_tokens": 16920000, "grad_norm": 0.6328125, "lr": 3e-05, "finish_rate": 0.949, "comp_len": 379.7, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 46.5, "frames": {"chat": 316}, "mem_gb": 15.95}
+{"step": 142, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.3369911602856281, "tokens": 120000, "cumulative_loss_tokens": 17040000, "grad_norm": 0.578125, "lr": 3e-05, "finish_rate": 0.945, "comp_len": 415.2, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 44.7, "frames": {"chat": 289}, "mem_gb": 16.1}
+{"step": 143, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.35459049528371545, "tokens": 120000, "cumulative_loss_tokens": 17160000, "grad_norm": 0.56640625, "lr": 3e-05, "finish_rate": 0.982, "comp_len": 362.5, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 48.8, "frames": {"chat": 331}, "mem_gb": 15.74}
+{"step": 144, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.3481748200963562, "tokens": 120000, "cumulative_loss_tokens": 17280000, "grad_norm": 0.57421875, "lr": 3e-05, "finish_rate": 0.972, "comp_len": 375.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 45.6, "frames": {"chat": 320}, "mem_gb": 16.05}
+{"step": 145, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.3090666974460706, "tokens": 120000, "cumulative_loss_tokens": 17400000, "grad_norm": 0.53515625, "lr": 3e-05, "finish_rate": 0.941, "comp_len": 441.2, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 39.1, "frames": {"chat": 272}, "mem_gb": 15.79}
+{"step": 146, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.5301194600745104, "tokens": 120000, "cumulative_loss_tokens": 17520000, "grad_norm": 0.69140625, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 326.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 52.8, "frames": {"chat": 368}, "mem_gb": 15.7}
+{"step": 147, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.4325976529110844, "tokens": 120000, "cumulative_loss_tokens": 17640000, "grad_norm": 0.6328125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 365.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 48.6, "frames": {"chat": 328}, "mem_gb": 15.73}
+{"step": 148, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.4370371102225035, "tokens": 120000, "cumulative_loss_tokens": 17760000, "grad_norm": 0.6328125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 368.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 47.8, "frames": {"chat": 326}, "mem_gb": 15.95}
+{"step": 149, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.4871703383412212, "tokens": 120000, "cumulative_loss_tokens": 17880000, "grad_norm": 0.67578125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 329.7, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 51.9, "frames": {"chat": 364}, "mem_gb": 15.89}
+{"step": 150, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.31749200204570466, "tokens": 120000, "cumulative_loss_tokens": 18000000, "grad_norm": 0.53125, "lr": 3e-05, "finish_rate": 0.97, "comp_len": 405.4, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 42.4, "frames": {"chat": 296}, "mem_gb": 15.93}
+[eval step 150] sample: "To compute the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. Here's how we can do it step-by-step:\n\n1. **Write down the matrix:**\n   "
+checkpoint snapshot queued -> outputs/healed/grid_general/uniform_keep50_s1224/step0150
+wandb: updating run metadata
+wandb: uploading summary, console lines 171-171
+wandb: 
+wandb: Run history:
+wandb:               comp_len ▂▃▃▅▅▅▂▁▇▅▄▃▅▅▅▂▃▃▅▃▃▃▂▃▄▃▅▂█▅▃▂▃▄▅▄▂▂▂▂
+wandb: cumulative_loss_tokens ▁▁▁▁▁▂▂▂▃▃▃▃▄▄▄▄▄▄▅▅▅▅▆▆▆▆▆▆▆▆▇▇▇▇▇█████
+wandb:                  epoch ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
+wandb:            finish_rate ██▇█▁██▃▄▁▆█▄█▆██▇▆▆████▇▆█▇▆█▇███▇█▃▇██
+wandb:        forward_topk_kl ▆█▆▅▆▃▄▂▃▃▂▄▃▄▁▄▄▃▄▃▄▃▃▄▃▁▁▄▄▃▃▃▂▄▂▃▃▄▄▃
+wandb:              grad_norm █▃▂▂▂▂▂▂▂▂▁▁▂▁▁▁▁▁▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
+wandb:                     lr ▁▄▅▅▇███████████████████████████████████
+wandb:                 mem_gb ▅▄▅▃▆▄▅▆▅▇▅▁▄▅▆▇▂▇▄▁▃▃▂▄▅▆█▄▃▃▇▅▇▂▅▄▆▃▄▆
+wandb:                   step ▁▁▁▁▁▂▂▂▂▂▂▃▃▃▃▄▄▄▄▅▅▅▅▅▅▆▆▆▇▇▇▇▇▇▇▇████
+wandb:               t_data_s █▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
+wandb:                     +3 ...
+wandb: 
+wandb: Run summary:
+wandb:               comp_len 405.4
+wandb: cumulative_loss_tokens 18000000
+wandb:                  epoch 0
+wandb:            finish_rate 0.97
+wandb:        forward_topk_kl 0.31749
+wandb:              grad_norm 0.53125
+wandb:                     lr 3e-05
+wandb:                 mem_gb 15.93
+wandb:                   step 150
+wandb:               t_data_s 0
+wandb:                     +4 ...
+wandb: 
+wandb: 🚀 View run uniform_keep50_s1224 at: https://wandb.ai/hbfreed/glean-general-grid/runs/osqsd9b4
+wandb: ⭐️ View project at: https://wandb.ai/hbfreed/glean-general-grid
+wandb: Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)
+wandb: Find logs at: outputs/healed/grid_general/uniform_keep50_s1224/wandb/run-20260718_134414-osqsd9b4/logs

healed/grid_general/uniform_keep50_s1225.console.log

@@ -0,0 +1,216 @@
+/home/henry/Documents/PythonProjects/variable-reap/.venv/lib/python3.12/site-packages/megablocks/grouped_gemm_util.py:10: UserWarning: Grouped GEMM not available.
+  warnings.warn('Grouped GEMM not available.')
+wandb: [wandb.login()] Loaded credentials for https://api.wandb.ai from /home/henry/.netrc.
+wandb: Currently logged in as: hbfreed to https://api.wandb.ai. Use `wandb login --relogin` to force relogin
+wandb: setting up run 056290eo
+wandb: Tracking run with wandb version 0.28.0
+wandb: Run data is saved locally in outputs/healed/grid_general/uniform_keep50_s1225/wandb/run-20260718_124640-056290eo
+wandb: Run `wandb offline` to turn off syncing.
+wandb: Syncing run uniform_keep50_s1225
+wandb: ⭐️ View project at https://wandb.ai/hbfreed/glean-general-grid
+wandb: 🚀 View run at https://wandb.ai/hbfreed/glean-general-grid/runs/056290eo
+
+58360 cached top-128 chat trajectories / 22,295,631 unique tokens | 185 steps/epoch | 150 total steps | student params 3.70B | teacher overlap=False
+{"step": 1, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.0012346531342715, "tokens": 120000, "cumulative_loss_tokens": 120000, "grad_norm": 8.625, "lr": 6e-06, "finish_rate": 0.997, "comp_len": 362.5, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 55.4, "frames": {"chat": 331}, "mem_gb": 15.8}
+The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.
+[eval step 1] sample: 'To solve this problem, we need to consider both the monthly savings and the interest earned each month.\n\n1. **Initial Savings:** Jamie starts with $50,000.\n\n2. **Total Goal:** Jamie aims to reach $1,0'
+{"step": 2, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.9385142022331555, "tokens": 120000, "cumulative_loss_tokens": 240000, "grad_norm": 7.6875, "lr": 9e-06, "finish_rate": 0.997, "comp_len": 350.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 50.5, "frames": {"chat": 342}, "mem_gb": 15.67}
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+{"step": 7, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.7034493155685564, "tokens": 120000, "cumulative_loss_tokens": 840000, "grad_norm": 2.109375, "lr": 2.4e-05, "finish_rate": 0.979, "comp_len": 364.7, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 49.2, "frames": {"chat": 329}, "mem_gb": 15.93}
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+{"step": 9, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.7796795962753396, "tokens": 120000, "cumulative_loss_tokens": 1080000, "grad_norm": 1.78125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 339.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 53.2, "frames": {"chat": 354}, "mem_gb": 15.89}
+{"step": 10, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.47490170586556196, "tokens": 120000, "cumulative_loss_tokens": 1200000, "grad_norm": 1.4140625, "lr": 3e-05, "finish_rate": 0.943, "comp_len": 430.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 42.3, "frames": {"chat": 279}, "mem_gb": 16.02}
+[eval step 10] sample: 'To determine how many months it will take for Jamie to reach their goal of $1,000,000, we need to consider both the amount saved and the interest earned each month.\n\n1. **Initial Savings:**\n   Jamie s'
+{"step": 11, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.5603851036655406, "tokens": 120000, "cumulative_loss_tokens": 1320000, "grad_norm": 1.3671875, "lr": 3e-05, "finish_rate": 0.959, "comp_len": 377.4, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 48.6, "frames": {"chat": 318}, "mem_gb": 15.95}
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+{"step": 14, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.5093685679781561, "tokens": 120000, "cumulative_loss_tokens": 1680000, "grad_norm": 0.98046875, "lr": 3e-05, "finish_rate": 0.962, "comp_len": 381.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 48.8, "frames": {"chat": 315}, "mem_gb": 16.01}
+{"step": 15, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.6256126999858767, "tokens": 120000, "cumulative_loss_tokens": 1800000, "grad_norm": 1.0078125, "lr": 3e-05, "finish_rate": 0.977, "comp_len": 400.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 45.3, "frames": {"chat": 300}, "mem_gb": 15.81}
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+checkpoint snapshot queued -> outputs/healed/grid_general/uniform_keep50_s1225/step0100
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+[eval step 120] sample: 'To solve this problem, we need to determine how many months it will take Jamie to save enough money to reach their goal of $1,000,000, given their monthly savings and the interest earned.\n\n**Step 1: C'
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+[eval step 130] sample: 'To solve this problem, we need to determine how many months it will take Jamie to save a total of $1,000,000 given their monthly savings and the interest earned from their investment account.\n\n**Step '
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+[eval step 140] sample: 'To solve this problem, we need to determine how many months it will take Jamie to save a total of $1,000,000 given their monthly savings and the interest earned from their investment account.\n\n**Step '
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+{"step": 148, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.27330009992414467, "tokens": 120000, "cumulative_loss_tokens": 17760000, "grad_norm": 0.5, "lr": 3e-05, "finish_rate": 0.923, "comp_len": 442.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 42.0, "frames": {"chat": 271}, "mem_gb": 15.95}
+{"step": 149, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.41801022604368626, "tokens": 120000, "cumulative_loss_tokens": 17880000, "grad_norm": 0.61328125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 334.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 50.6, "frames": {"chat": 359}, "mem_gb": 15.74}
+{"step": 150, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.4207492188954105, "tokens": 120000, "cumulative_loss_tokens": 18000000, "grad_norm": 0.61328125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 347.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 51.4, "frames": {"chat": 345}, "mem_gb": 15.8}
+[eval step 150] sample: 'To solve this problem, we need to determine how many months it will take Jamie to save a total of $1,000,000 given their monthly savings and the interest earned from their investment account.\n\n**Step '
+checkpoint snapshot queued -> outputs/healed/grid_general/uniform_keep50_s1225/step0150
+wandb: updating run metadata
+wandb: uploading summary, console lines 171-171
+wandb: 
+wandb: Run history:
+wandb:               comp_len ▂▂▂▅▆▄▅▃▄▄▃▂█▁▂▇▄▅▂▄▁▃▃▂▄▆▃▅▅▂▇▅▃▂▆▃▁▇▅▂
+wandb: cumulative_loss_tokens ▁▁▂▂▂▂▂▂▂▃▃▃▃▃▄▄▅▅▅▅▅▅▆▆▆▆▆▆▆▆▇▇▇▇▇▇▇▇▇█
+wandb:                  epoch ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
+wandb:            finish_rate ▂█▇▇▇█▆█▇▄▄████▇▇▆█▆▄▂▆█▁███▃▇▇█▄██▇▇█▇█
+wandb:        forward_topk_kl █▇▄▄▅▅▅▂▂▄▄▂▄▂▂▄▃▃▁▃▃▃▃▂▂▄▃▃▂▂▂▁▂▃▃▃▂▂▃▂
+wandb:              grad_norm █▂▂▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
+wandb:                     lr ▁▂██████████████████████████████████████
+wandb:                 mem_gb ▄▃█▆▇▃▂▅▃▃▆▄▃▄▇▆▇▄▂▅▆▁▄▄▇▃▂▃▆▅▃▂▅▃▆▆▅█▅▄
+wandb:                   step ▁▁▁▁▁▂▂▂▂▂▂▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▇▇▇▇▇▇▇█████
+wandb:               t_data_s ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
+wandb:                     +3 ...
+wandb: 
+wandb: Run summary:
+wandb:               comp_len 347.8
+wandb: cumulative_loss_tokens 18000000
+wandb:                  epoch 0
+wandb:            finish_rate 0.997
+wandb:        forward_topk_kl 0.42075
+wandb:              grad_norm 0.61328
+wandb:                     lr 3e-05
+wandb:                 mem_gb 15.8
+wandb:                   step 150
+wandb:               t_data_s 0
+wandb:                     +4 ...
+wandb: 
+wandb: 🚀 View run uniform_keep50_s1225 at: https://wandb.ai/hbfreed/glean-general-grid/runs/056290eo
+wandb: ⭐️ View project at: https://wandb.ai/hbfreed/glean-general-grid
+wandb: Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)
+wandb: Find logs at: outputs/healed/grid_general/uniform_keep50_s1225/wandb/run-20260718_124640-056290eo/logs