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healed/healing_breadth/glean_math_keep25_seed1224/wandb/debug-internal.log

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healed/healing_breadth/glean_math_keep25_seed1224/wandb/run-20260714_205307-zx83gucy/files/output.log

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+Loading checkpoint shards: 100%|██████████| 3/3 [00:00<00:00,  9.81it/s]
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+63977 prompts | 940 steps/epoch | 50 total steps | student params 2.09B | teacher overlap=True
+rollout short by 2,838 tokens; generating top-up 1 from 3 prompts
+{"step": 1, "epoch": 0, "training_mode": "on-policy", "reverse_kl": 1.8929568549315134, "tokens": 120000, "cumulative_loss_tokens": 120000, "grad_norm": 47.5, "lr": 6e-06, "finish_rate": 0.216, "comp_len": 431.7, "t_data_s": 0.0, "t_rollout_s": 36.8, "t_step_s": 87.2, "t_refresh_s": 0.3, "mem_gb": 10.33}
+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: '\nThe only number that could have been placed along thethe only number that is 75 divided by 5. The problem is to determine the maximum number of maames that could have been placed.\n\nThe only number th'
+rollout short by 21,055 tokens; generating top-up 1 from 19 prompts
+{"step": 2, "epoch": 0, "training_mode": "on-policy", "reverse_kl": 1.8332483816136917, "tokens": 120000, "cumulative_loss_tokens": 240000, "grad_norm": 38.5, "lr": 9e-06, "finish_rate": 0.337, "comp_len": 371.5, "t_data_s": 0.0, "t_rollout_s": 38.3, "t_step_s": 87.5, "t_refresh_s": 0.3, "mem_gb": 10.45}
+rollout short by 13,628 tokens; generating top-up 1 from 11 prompts
+{"step": 3, "epoch": 0, "training_mode": "on-policy", "reverse_kl": 1.7165187035838763, "tokens": 120000, "cumulative_loss_tokens": 360000, "grad_norm": 33.75, "lr": 1.2e-05, "finish_rate": 0.387, "comp_len": 393.4, "t_data_s": 0.0, "t_rollout_s": 37.4, "t_step_s": 84.6, "t_refresh_s": 0.3, "mem_gb": 10.35}
+rollout short by 12,030 tokens; generating top-up 1 from 10 prompts
+{"step": 4, "epoch": 0, "training_mode": "on-policy", "reverse_kl": 1.5216001023660104, "tokens": 120000, "cumulative_loss_tokens": 480000, "grad_norm": 25.875, "lr": 1.5e-05, "finish_rate": 0.303, "comp_len": 394.7, "t_data_s": 0.0, "t_rollout_s": 36.1, "t_step_s": 80.7, "t_refresh_s": 0.3, "mem_gb": 10.37}
+rollout short by 16,574 tokens; generating top-up 1 from 14 prompts
+{"step": 5, "epoch": 0, "training_mode": "on-policy", "reverse_kl": 1.4106985497762758, "tokens": 120000, "cumulative_loss_tokens": 600000, "grad_norm": 29.875, "lr": 1.8e-05, "finish_rate": 0.429, "comp_len": 378.5, "t_data_s": 0.0, "t_rollout_s": 36.3, "t_step_s": 87.7, "t_refresh_s": 0.3, "mem_gb": 10.36}
+rollout short by 14,735 tokens; generating top-up 1 from 12 prompts
+{"step": 6, "epoch": 0, "training_mode": "on-policy", "reverse_kl": 1.2116640572726727, "tokens": 120000, "cumulative_loss_tokens": 720000, "grad_norm": 28.625, "lr": 2.1e-05, "finish_rate": 0.55, "comp_len": 385.9, "t_data_s": 0.0, "t_rollout_s": 39.7, "t_step_s": 90.7, "t_refresh_s": 0.3, "mem_gb": 10.36}
+rollout short by 9,665 tokens; generating top-up 1 from 8 prompts
+{"step": 7, "epoch": 0, "training_mode": "on-policy", "reverse_kl": 1.0176955454071364, "tokens": 120000, "cumulative_loss_tokens": 840000, "grad_norm": 12.9375, "lr": 2.4e-05, "finish_rate": 0.5, "comp_len": 402.7, "t_data_s": 0.0, "t_rollout_s": 39.5, "t_step_s": 85.6, "t_refresh_s": 0.3, "mem_gb": 10.37}
+{"step": 8, "epoch": 0, "training_mode": "on-policy", "reverse_kl": 0.8163571097140511, "tokens": 120000, "cumulative_loss_tokens": 960000, "grad_norm": 7.1875, "lr": 2.7000000000000002e-05, "finish_rate": 0.317, "comp_len": 452.8, "t_data_s": 0.0, "t_rollout_s": 28.8, "t_step_s": 69.0, "t_refresh_s": 0.3, "mem_gb": 10.37}
+{"step": 9, "epoch": 0, "training_mode": "on-policy", "reverse_kl": 0.7240555229172111, "tokens": 120000, "cumulative_loss_tokens": 1080000, "grad_norm": 5.875, "lr": 3e-05, "finish_rate": 0.193, "comp_len": 491.8, "t_data_s": 0.0, "t_rollout_s": 33.3, "t_step_s": 77.6, "t_refresh_s": 0.3, "mem_gb": 10.38}
+{"step": 10, "epoch": 0, "training_mode": "on-policy", "reverse_kl": 0.691375209479034, "tokens": 120000, "cumulative_loss_tokens": 1200000, "grad_norm": 3.734375, "lr": 3e-05, "finish_rate": 0.224, "comp_len": 480.0, "t_data_s": 0.0, "t_rollout_s": 31.9, "t_step_s": 69.5, "t_refresh_s": 0.3, "mem_gb": 10.54}
+[eval step 10] sample: "To solve this problem, we need to break it down into manageable steps and use logical reasoning. Let's start by analyzing the given conditions and the constraints.\n\n1. **Understanding the Problem:**\n "
+{"step": 11, "epoch": 0, "training_mode": "on-policy", "reverse_kl": 0.6840026769215862, "tokens": 120000, "cumulative_loss_tokens": 1320000, "grad_norm": 3.453125, "lr": 3e-05, "finish_rate": 0.181, "comp_len": 493.8, "t_data_s": 0.0, "t_rollout_s": 33.9, "t_step_s": 70.7, "t_refresh_s": 0.3, "mem_gb": 10.38}
+{"step": 12, "epoch": 0, "training_mode": "on-policy", "reverse_kl": 0.6246620353197058, "tokens": 120000, "cumulative_loss_tokens": 1440000, "grad_norm": 3.0625, "lr": 3e-05, "finish_rate": 0.165, "comp_len": 493.8, "t_data_s": 0.0, "t_rollout_s": 34.2, "t_step_s": 71.5, "t_refresh_s": 0.3, "mem_gb": 10.52}
+{"step": 13, "epoch": 0, "training_mode": "on-policy", "reverse_kl": 0.5813416921809316, "tokens": 120000, "cumulative_loss_tokens": 1560000, "grad_norm": 2.328125, "lr": 3e-05, "finish_rate": 0.145, "comp_len": 495.9, "t_data_s": 0.0, "t_rollout_s": 33.7, "t_step_s": 70.4, "t_refresh_s": 0.3, "mem_gb": 10.42}
+{"step": 14, "epoch": 0, "training_mode": "on-policy", "reverse_kl": 0.6156017889492214, "tokens": 120000, "cumulative_loss_tokens": 1680000, "grad_norm": 3.828125, "lr": 3e-05, "finish_rate": 0.139, "comp_len": 491.8, "t_data_s": 0.0, "t_rollout_s": 33.0, "t_step_s": 69.6, "t_refresh_s": 0.3, "mem_gb": 10.46}
+{"step": 15, "epoch": 0, "training_mode": "on-policy", "reverse_kl": 0.5399293902300298, "tokens": 120000, "cumulative_loss_tokens": 1800000, "grad_norm": 2.265625, "lr": 3e-05, "finish_rate": 0.228, "comp_len": 487.8, "t_data_s": 0.0, "t_rollout_s": 33.8, "t_step_s": 71.3, "t_refresh_s": 0.3, "mem_gb": 10.46}
+{"step": 16, "epoch": 0, "training_mode": "on-policy", "reverse_kl": 0.5018366255449752, "tokens": 120000, "cumulative_loss_tokens": 1920000, "grad_norm": 2.234375, "lr": 3e-05, "finish_rate": 0.2, "comp_len": 489.8, "t_data_s": 0.0, "t_rollout_s": 33.8, "t_step_s": 71.5, "t_refresh_s": 0.3, "mem_gb": 10.44}
+{"step": 17, "epoch": 0, "training_mode": "on-policy", "reverse_kl": 0.49358773938765127, "tokens": 120000, "cumulative_loss_tokens": 2040000, "grad_norm": 2.25, "lr": 3e-05, "finish_rate": 0.255, "comp_len": 485.8, "t_data_s": 0.0, "t_rollout_s": 32.9, "t_step_s": 70.3, "t_refresh_s": 0.3, "mem_gb": 10.36}
+{"step": 18, "epoch": 0, "training_mode": "on-policy", "reverse_kl": 0.45969002382730445, "tokens": 120000, "cumulative_loss_tokens": 2160000, "grad_norm": 2.203125, "lr": 3e-05, "finish_rate": 0.157, "comp_len": 495.9, "t_data_s": 0.0, "t_rollout_s": 34.5, "t_step_s": 71.0, "t_refresh_s": 0.3, "mem_gb": 10.42}
+{"step": 19, "epoch": 0, "training_mode": "on-policy", "reverse_kl": 0.4492309203994771, "tokens": 120000, "cumulative_loss_tokens": 2280000, "grad_norm": 2.03125, "lr": 3e-05, "finish_rate": 0.192, "comp_len": 489.8, "t_data_s": 0.0, "t_rollout_s": 34.1, "t_step_s": 71.6, "t_refresh_s": 0.3, "mem_gb": 10.47}
+{"step": 20, "epoch": 0, "training_mode": "on-policy", "reverse_kl": 0.4397894059561193, "tokens": 120000, "cumulative_loss_tokens": 2400000, "grad_norm": 2.0, "lr": 3e-05, "finish_rate": 0.164, "comp_len": 491.8, "t_data_s": 0.0, "t_rollout_s": 33.7, "t_step_s": 70.7, "t_refresh_s": 0.3, "mem_gb": 10.5}
+[eval step 20] sample: "To solve this problem, we need to use combinatorial reasoning and constraints. Let's break it down step-by-step:\n\n1. **Define Variables:**\n   - Let \\( m \\) be the number of mples.\n   - Let \\( l \\) be "
+{"step": 21, "epoch": 0, "training_mode": "on-policy", "reverse_kl": 0.42920704178611435, "tokens": 120000, "cumulative_loss_tokens": 2520000, "grad_norm": 1.890625, "lr": 3e-05, "finish_rate": 0.214, "comp_len": 483.9, "t_data_s": 0.0, "t_rollout_s": 31.8, "t_step_s": 68.7, "t_refresh_s": 0.3, "mem_gb": 10.38}
+{"step": 22, "epoch": 0, "training_mode": "on-policy", "reverse_kl": 0.4285325840468208, "tokens": 120000, "cumulative_loss_tokens": 2640000, "grad_norm": 1.8359375, "lr": 3e-05, "finish_rate": 0.209, "comp_len": 491.8, "t_data_s": 0.0, "t_rollout_s": 34.4, "t_step_s": 71.7, "t_refresh_s": 0.3, "mem_gb": 10.42}
+{"step": 23, "epoch": 0, "training_mode": "on-policy", "reverse_kl": 0.39100426478857797, "tokens": 120000, "cumulative_loss_tokens": 2760000, "grad_norm": 2.078125, "lr": 3e-05, "finish_rate": 0.117, "comp_len": 500.0, "t_data_s": 0.0, "t_rollout_s": 35.1, "t_step_s": 71.4, "t_refresh_s": 0.3, "mem_gb": 10.43}
+{"step": 24, "epoch": 0, "training_mode": "on-policy", "reverse_kl": 0.44906475950367747, "tokens": 120000, "cumulative_loss_tokens": 2880000, "grad_norm": 2.09375, "lr": 3e-05, "finish_rate": 0.172, "comp_len": 491.8, "t_data_s": 0.0, "t_rollout_s": 33.4, "t_step_s": 71.4, "t_refresh_s": 0.3, "mem_gb": 10.51}
+{"step": 25, "epoch": 0, "training_mode": "on-policy", "reverse_kl": 0.4109231275471548, "tokens": 120000, "cumulative_loss_tokens": 3000000, "grad_norm": 1.8125, "lr": 3e-05, "finish_rate": 0.25, "comp_len": 483.9, "t_data_s": 0.0, "t_rollout_s": 32.5, "t_step_s": 70.7, "t_refresh_s": 0.3, "mem_gb": 10.53}
+checkpoint snapshot queued -> outputs/healed/healing_breadth/glean_math_keep25_seed1224/step0025
+{"step": 26, "epoch": 0, "training_mode": "on-policy", "reverse_kl": 0.3922373386658728, "tokens": 120000, "cumulative_loss_tokens": 3120000, "grad_norm": 1.8125, "lr": 3e-05, "finish_rate": 0.251, "comp_len": 478.1, "t_data_s": 0.0, "t_rollout_s": 32.5, "t_step_s": 71.2, "t_refresh_s": 0.3, "mem_gb": 10.48}
+{"step": 27, "epoch": 0, "training_mode": "on-policy", "reverse_kl": 0.38296805831243597, "tokens": 120000, "cumulative_loss_tokens": 3240000, "grad_norm": 1.859375, "lr": 3e-05, "finish_rate": 0.199, "comp_len": 487.8, "t_data_s": 0.0, "t_rollout_s": 33.3, "t_step_s": 71.8, "t_refresh_s": 0.4, "mem_gb": 10.48}
+{"step": 28, "epoch": 0, "training_mode": "on-policy", "reverse_kl": 0.38206375452155866, "tokens": 120000, "cumulative_loss_tokens": 3360000, "grad_norm": 1.6875, "lr": 3e-05, "finish_rate": 0.152, "comp_len": 493.8, "t_data_s": 0.0, "t_rollout_s": 33.5, "t_step_s": 70.9, "t_refresh_s": 0.3, "mem_gb": 10.5}
+{"step": 29, "epoch": 0, "training_mode": "on-policy", "reverse_kl": 0.3743179695730408, "tokens": 120000, "cumulative_loss_tokens": 3480000, "grad_norm": 1.7421875, "lr": 3e-05, "finish_rate": 0.177, "comp_len": 493.8, "t_data_s": 0.0, "t_rollout_s": 34.0, "t_step_s": 72.0, "t_refresh_s": 0.4, "mem_gb": 10.49}
+{"step": 30, "epoch": 0, "training_mode": "on-policy", "reverse_kl": 0.38618751456712685, "tokens": 120000, "cumulative_loss_tokens": 3600000, "grad_norm": 1.796875, "lr": 3e-05, "finish_rate": 0.242, "comp_len": 483.9, "t_data_s": 0.0, "t_rollout_s": 32.7, "t_step_s": 69.3, "t_refresh_s": 0.3, "mem_gb": 10.45}
+[eval step 30] sample: 'To solve this problem, we need to determine the maximum number of maples that can be planted along the alley given the constraints:\n\n1. The total number of trees is 75.\n2. There are no two maples betw'
+{"step": 31, "epoch": 0, "training_mode": "on-policy", "reverse_kl": 0.3496829746659845, "tokens": 120000, "cumulative_loss_tokens": 3720000, "grad_norm": 1.6328125, "lr": 3e-05, "finish_rate": 0.232, "comp_len": 480.0, "t_data_s": 0.0, "t_rollout_s": 30.5, "t_step_s": 68.1, "t_refresh_s": 0.3, "mem_gb": 10.45}
+{"step": 32, "epoch": 0, "training_mode": "on-policy", "reverse_kl": 0.3457430052795758, "tokens": 120000, "cumulative_loss_tokens": 3840000, "grad_norm": 1.7890625, "lr": 3e-05, "finish_rate": 0.194, "comp_len": 485.8, "t_data_s": 0.0, "t_rollout_s": 32.5, "t_step_s": 69.8, "t_refresh_s": 0.3, "mem_gb": 10.48}
+{"step": 33, "epoch": 0, "training_mode": "on-policy", "reverse_kl": 0.36799976486029723, "tokens": 120000, "cumulative_loss_tokens": 3960000, "grad_norm": 1.71875, "lr": 3e-05, "finish_rate": 0.232, "comp_len": 480.0, "t_data_s": 0.0, "t_rollout_s": 31.6, "t_step_s": 69.4, "t_refresh_s": 0.3, "mem_gb": 10.44}
+{"step": 34, "epoch": 0, "training_mode": "on-policy", "reverse_kl": 0.3638427472679565, "tokens": 120000, "cumulative_loss_tokens": 4080000, "grad_norm": 1.6484375, "lr": 3e-05, "finish_rate": 0.257, "comp_len": 481.9, "t_data_s": 0.0, "t_rollout_s": 31.5, "t_step_s": 69.5, "t_refresh_s": 0.3, "mem_gb": 10.51}
+{"step": 35, "epoch": 0, "training_mode": "on-policy", "reverse_kl": 0.36085621776568394, "tokens": 120000, "cumulative_loss_tokens": 4200000, "grad_norm": 1.7265625, "lr": 3e-05, "finish_rate": 0.241, "comp_len": 481.9, "t_data_s": 0.0, "t_rollout_s": 32.7, "t_step_s": 71.0, "t_refresh_s": 0.3, "mem_gb": 10.47}
+{"step": 36, "epoch": 0, "training_mode": "on-policy", "reverse_kl": 0.327357999405389, "tokens": 120000, "cumulative_loss_tokens": 4320000, "grad_norm": 1.5, "lr": 3e-05, "finish_rate": 0.237, "comp_len": 481.9, "t_data_s": 0.0, "t_rollout_s": 32.7, "t_step_s": 71.1, "t_refresh_s": 0.3, "mem_gb": 10.5}
+{"step": 37, "epoch": 0, "training_mode": "on-policy", "reverse_kl": 0.34588671219460665, "tokens": 120000, "cumulative_loss_tokens": 4440000, "grad_norm": 1.6875, "lr": 3e-05, "finish_rate": 0.171, "comp_len": 489.8, "t_data_s": 0.0, "t_rollout_s": 32.9, "t_step_s": 70.7, "t_refresh_s": 0.3, "mem_gb": 10.42}
+{"step": 38, "epoch": 0, "training_mode": "on-policy", "reverse_kl": 0.34063462760510543, "tokens": 120000, "cumulative_loss_tokens": 4560000, "grad_norm": 1.7109375, "lr": 3e-05, "finish_rate": 0.21, "comp_len": 483.9, "t_data_s": 0.0, "t_rollout_s": 32.2, "t_step_s": 69.0, "t_refresh_s": 0.3, "mem_gb": 10.49}
+{"step": 39, "epoch": 0, "training_mode": "on-policy", "reverse_kl": 0.336651858022064, "tokens": 120000, "cumulative_loss_tokens": 4680000, "grad_norm": 1.4921875, "lr": 3e-05, "finish_rate": 0.238, "comp_len": 483.9, "t_data_s": 0.0, "t_rollout_s": 31.0, "t_step_s": 68.4, "t_refresh_s": 0.3, "mem_gb": 10.47}
+{"step": 40, "epoch": 0, "training_mode": "on-policy", "reverse_kl": 0.32966975886846583, "tokens": 120000, "cumulative_loss_tokens": 4800000, "grad_norm": 1.6875, "lr": 3e-05, "finish_rate": 0.246, "comp_len": 483.9, "t_data_s": 0.0, "t_rollout_s": 32.7, "t_step_s": 70.5, "t_refresh_s": 0.3, "mem_gb": 10.37}
+[eval step 40] sample: "To solve this problem, we need to use combinatorial reasoning and constraints. Let's break down the problem step-by-step:\n\n1. **Define Variables:**\n   - Let \\( m \\) be the number of maples.\n   - Let \\"
+{"step": 41, "epoch": 0, "training_mode": "on-policy", "reverse_kl": 0.31904542534798386, "tokens": 120000, "cumulative_loss_tokens": 4920000, "grad_norm": 1.703125, "lr": 3e-05, "finish_rate": 0.24, "comp_len": 480.0, "t_data_s": 0.0, "t_rollout_s": 31.7, "t_step_s": 70.4, "t_refresh_s": 0.3, "mem_gb": 10.53}
+{"step": 42, "epoch": 0, "training_mode": "on-policy", "reverse_kl": 0.3319877192765474, "tokens": 120000, "cumulative_loss_tokens": 5040000, "grad_norm": 1.6796875, "lr": 3e-05, "finish_rate": 0.213, "comp_len": 481.9, "t_data_s": 0.0, "t_rollout_s": 32.3, "t_step_s": 69.9, "t_refresh_s": 0.3, "mem_gb": 10.46}
+{"step": 43, "epoch": 0, "training_mode": "on-policy", "reverse_kl": 0.32353154991058014, "tokens": 120000, "cumulative_loss_tokens": 5160000, "grad_norm": 1.578125, "lr": 3e-05, "finish_rate": 0.293, "comp_len": 481.9, "t_data_s": 0.0, "t_rollout_s": 32.2, "t_step_s": 69.6, "t_refresh_s": 0.3, "mem_gb": 10.38}
+{"step": 44, "epoch": 0, "training_mode": "on-policy", "reverse_kl": 0.3083366705554227, "tokens": 120000, "cumulative_loss_tokens": 5280000, "grad_norm": 1.59375, "lr": 3e-05, "finish_rate": 0.217, "comp_len": 481.9, "t_data_s": 0.0, "t_rollout_s": 31.9, "t_step_s": 69.0, "t_refresh_s": 0.3, "mem_gb": 10.44}
+{"step": 45, "epoch": 0, "training_mode": "on-policy", "reverse_kl": 0.3119819214983533, "tokens": 120000, "cumulative_loss_tokens": 5400000, "grad_norm": 1.59375, "lr": 3e-05, "finish_rate": 0.245, "comp_len": 481.9, "t_data_s": 0.0, "t_rollout_s": 31.6, "t_step_s": 68.3, "t_refresh_s": 0.3, "mem_gb": 10.36}
+{"step": 46, "epoch": 0, "training_mode": "on-policy", "reverse_kl": 0.30937388323086, "tokens": 120000, "cumulative_loss_tokens": 5520000, "grad_norm": 1.5, "lr": 3e-05, "finish_rate": 0.252, "comp_len": 480.0, "t_data_s": 0.0, "t_rollout_s": 31.8, "t_step_s": 69.8, "t_refresh_s": 0.3, "mem_gb": 10.43}
+{"step": 47, "epoch": 0, "training_mode": "on-policy", "reverse_kl": 0.2979762297540903, "tokens": 120000, "cumulative_loss_tokens": 5640000, "grad_norm": 1.6171875, "lr": 3e-05, "finish_rate": 0.186, "comp_len": 485.8, "t_data_s": 0.0, "t_rollout_s": 31.0, "t_step_s": 68.3, "t_refresh_s": 0.3, "mem_gb": 10.46}
+{"step": 48, "epoch": 0, "training_mode": "on-policy", "reverse_kl": 0.31448883896246554, "tokens": 120000, "cumulative_loss_tokens": 5760000, "grad_norm": 1.6015625, "lr": 3e-05, "finish_rate": 0.201, "comp_len": 481.9, "t_data_s": 0.0, "t_rollout_s": 32.0, "t_step_s": 70.8, "t_refresh_s": 0.3, "mem_gb": 10.43}
+{"step": 49, "epoch": 0, "training_mode": "on-policy", "reverse_kl": 0.2903304275934895, "tokens": 120000, "cumulative_loss_tokens": 5880000, "grad_norm": 1.3671875, "lr": 3e-05, "finish_rate": 0.231, "comp_len": 485.8, "t_data_s": 0.0, "t_rollout_s": 31.9, "t_step_s": 69.4, "t_refresh_s": 0.3, "mem_gb": 10.52}
+{"step": 50, "epoch": 0, "training_mode": "on-policy", "reverse_kl": 0.30557283139005303, "tokens": 120000, "cumulative_loss_tokens": 6000000, "grad_norm": 1.5859375, "lr": 3e-05, "finish_rate": 0.25, "comp_len": 483.9, "t_data_s": 0.0, "t_rollout_s": 33.3, "t_step_s": 64.3, "t_refresh_s": 0.0, "mem_gb": 10.54}
+[eval step 50] sample: "To solve this problem, we need to use combinatorial reasoning and constraints. Let's break down the problem step-by-step:\n\n1. **Define Variables:**\n   - Let \\( m \\) be the number of maples.\n   - Let \\"
+checkpoint snapshot queued -> outputs/healed/healing_breadth/glean_math_keep25_seed1224/step0050

healed/healing_breadth/glean_math_keep25_seed1224/wandb/run-20260714_205307-zx83gucy/files/requirements.txt

@@ -0,0 +1,123 @@
+certifi==2026.6.17
+fonttools==4.63.0
+urllib3==2.7.0
+requests==2.34.2
+nvidia-cublas-cu12==12.6.4.1
+packaging==26.2
+regex==2026.6.28
+portalocker==3.2.0
+safetensors==0.8.0
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healed/healing_breadth/glean_math_keep25_seed1224/wandb/run-20260714_205307-zx83gucy/logs/debug.log

@@ -0,0 +1,24 @@
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+2026-07-14 20:53:07,071 INFO    MainThread:188967 [wandb_init.py:setup_run_log_directory():725] Logging user logs to outputs/healed/healing_breadth/glean_math_keep25_seed1224/wandb/run-20260714_205307-zx83gucy/logs/debug.log
+2026-07-14 20:53:07,071 INFO    MainThread:188967 [wandb_init.py:setup_run_log_directory():726] Logging internal logs to outputs/healed/healing_breadth/glean_math_keep25_seed1224/wandb/run-20260714_205307-zx83gucy/logs/debug-internal.log
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healed/healing_breadth/glean_math_keep25_seed1224_long/wandb/run-20260715_071701-i5fg3xzq/files/output.log

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+Loading checkpoint shards: 100%|██████████| 3/3 [00:00<00:00,  9.50it/s]
+resumed student weights from outputs/healed/healing_breadth/glean_math_keep25_seed1224/step0050 (fresh optimizer, step counter at 50)
+starting vllm rollout server on GPU GPU-864c54df-0130-7780-e271-8a5551d1733f (port 8377) ...
+vllm server healthy in 24s
+dataset_source filter ['omega', 'polaris', 'orz_math', 'mathsub', 'dapo-math'] -> 63998 prompts
+difficulty <= 4 -> 63998 prompts
+WARNING: difficulty filter removed nothing — the slice likely carries difficulty=None (Dolci math sources do), so the teacher-competence guard is NOT in effect
+63977 prompts | 940 steps/epoch | 500 total steps | student params 2.09B | teacher overlap=True
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+checkpoint snapshot queued -> outputs/healed/healing_breadth/glean_math_keep25_seed1224_long/step0100
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+{"step": 150, "gsm8k_n": 64, "gsm8k_quick_chat": 0.3125, "t_eval_s": 44.9}
+checkpoint snapshot queued -> outputs/healed/healing_breadth/glean_math_keep25_seed1224_long/step0150
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+[eval step 170] sample: 'To solve this problem, we need to carefully analyze the constraints and use reasoning to determine the maximum number of maples that can be planted.\n\n### Problem Breakdown:\n\n1. **Total Number of Trees'
+{"step": 170, "gsm8k_n": 64, "gsm8k_quick_chat": 0.34375, "t_eval_s": 44.8}
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healed/healing_breadth/glean_math_keep25_seed1224_long/wandb/run-20260715_071701-i5fg3xzq/files/requirements.txt

@@ -0,0 +1,123 @@
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+pillow==12.3.0
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+      640,
+      512,
+      512,
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+      128,
+      384,
+      128,
+      512,
+      128,
+      256
+    ],
+    [
+      256,
+      768,
+      512,
+      640,
+      512,
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+      512,
+      256,
+      640,
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+  ],
+  "glean_metadata": {
+    "base_model": "allenai/OLMoE-1B-7B-0125-Instruct",
+    "block_size": 128,
+    "criterion": "reap",
+    "dead_experts": 442,
+    "keep_fraction": 0.25,
+    "min_width": 128,
+    "params": 2086418432,
+    "scores": "outputs/scores_0125inst_dolmino-math/scores.pt"
+  },
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 1024,
+  "max_position_embeddings": 4096,
+  "model_type": "pruned_olmoe",
+  "norm_topk_prob": false,
+  "num_attention_heads": 16,
+  "num_experts": 64,
+  "num_experts_per_tok": 8,
+  "num_hidden_layers": 16,
+  "num_key_value_heads": 16,
+  "output_router_logits": false,
+  "pad_token_id": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "router_aux_loss_coef": 0.01,
+  "tie_word_embeddings": false,
+  "transformers_version": "4.57.6",
+  "use_cache": false,
+  "vocab_size": 50304
+}

healed/healing_breadth/glean_math_keep25_seed1224_long768/step0500/configuration_pruned_olmoe.py

@@ -0,0 +1,27 @@
+"""Configuration for GLEAN-pruned OLMoE: variable-width, variable-count experts.
+"""
+
+from transformers.models.olmoe.configuration_olmoe import OlmoeConfig
+
+
+class PrunedOlmoeConfig(OlmoeConfig):
+    """OlmoeConfig plus a per-(layer, expert) width table.
+
+    ``expert_widths[l]`` lists the SwiGLU intermediate width of each surviving
+    expert in decoder layer ``l``, in expert order. Lists are ragged: layers
+    may keep different numbers of experts (deleted experts simply don't
+    appear — the router in layer ``l`` has ``len(expert_widths[l])`` rows),
+    and each width may differ (multiples of the GEMM block size, 128, for
+    variable-MegaBlocks execution). ``None`` means an unpruned model
+    (uniform ``num_experts`` × ``intermediate_size``).
+
+    The inherited ``num_experts`` / ``intermediate_size`` keep their ORIGINAL
+    (pre-pruning) values for provenance; the width table is authoritative for
+    the built architecture.
+    """
+
+    model_type = "pruned_olmoe"
+
+    def __init__(self, expert_widths: list[list[int]] | None = None, **kwargs):
+        super().__init__(**kwargs)
+        self.expert_widths = expert_widths

healed/healing_breadth/glean_math_keep25_seed1224_long768/step0500/generation_config.json

@@ -0,0 +1,6 @@
+{
+  "_from_model_config": true,
+  "eos_token_id": 50279,
+  "pad_token_id": 1,
+  "transformers_version": "4.57.6"
+}

healed/healing_breadth/glean_math_keep25_seed1224_long768/step0500/modeling_pruned_olmoe.py

@@ -0,0 +1,66 @@
+"""GLEAN-pruned OLMoE: HF-loadable model with ragged (variable-width) experts.
+
+Pattern follows hbfreed/variable-flex-olmo's PrunedFlexOlmoForCausalLM
+(docs/recon/prior-work-hbfreed.md), generalized from one scalar width to a
+per-(layer, expert) width table: ``super().__init__`` builds the uniform
+architecture from the config, then every MoE block is rebuilt to its pruned
+shape — surviving experts only, each at its own width, router sliced to
+match — so the state dict aligns exactly with what
+``glean.prune.prune_channels_global`` leaves behind.
+
+Caveat: ``output_router_logits=True`` (the load-balancing aux loss) assumes a
+uniform ``config.num_experts`` and is unsupported on ragged models.
+"""
+
+import torch.nn as nn
+from transformers.activations import ACT2FN
+from transformers.models.olmoe.modeling_olmoe import OlmoeForCausalLM
+
+from .configuration_pruned_olmoe import PrunedOlmoeConfig
+
+
+class RaggedOlmoeMLP(nn.Module):
+    """OlmoeMLP with an explicit intermediate width (SwiGLU, no biases)."""
+
+    def __init__(self, hidden_size: int, intermediate_size: int, hidden_act: str):
+        super().__init__()
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.gate_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
+        self.up_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
+        self.down_proj = nn.Linear(intermediate_size, hidden_size, bias=False)
+        self.act_fn = ACT2FN[hidden_act]
+
+    def forward(self, x):
+        return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+
+
+class PrunedOlmoeForCausalLM(OlmoeForCausalLM):
+    """OLMoE with per-layer surviving-expert lists at per-expert widths."""
+
+    config_class = PrunedOlmoeConfig
+
+    def __init__(self, config: PrunedOlmoeConfig):
+        super().__init__(config)
+        widths_table = getattr(config, "expert_widths", None)
+        if widths_table is None:
+            return  # unpruned: plain OLMoE
+        if len(widths_table) != len(self.model.layers):
+            raise ValueError(
+                f"expert_widths has {len(widths_table)} rows but the model has "
+                f"{len(self.model.layers)} decoder layers"
+            )
+        for layer, widths in zip(self.model.layers, widths_table):
+            if any(w <= 0 for w in widths):
+                raise ValueError("expert_widths must list surviving experts only (>0)")
+            block = layer.mlp
+            if len(widths) < block.top_k:
+                raise ValueError(
+                    f"a layer keeps {len(widths)} experts < top_k={block.top_k}"
+                )
+            block.num_experts = len(widths)
+            block.gate = nn.Linear(config.hidden_size, len(widths), bias=False)
+            block.experts = nn.ModuleList(
+                RaggedOlmoeMLP(config.hidden_size, w, config.hidden_act)
+                for w in widths
+            )

healed/healing_breadth/glean_math_keep25_seed1224_long768/step0500/special_tokens_map.json

@@ -0,0 +1,23 @@
+{
+  "bos_token": {
+    "content": "|||IP_ADDRESS|||",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "|||IP_ADDRESS|||",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

healed/healing_breadth/glean_math_keep25_seed1224_long768/step0500/tokenizer_config.json

@@ -0,0 +1,247 @@
+{
+  "add_bos_token": false,
+  "add_eos_token": false,
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<|padding|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50254": {
+      "content": "                        ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50255": {
+      "content": "                       ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50256": {
+      "content": "                      ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50257": {
+      "content": "                     ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50258": {
+      "content": "                    ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50259": {
+      "content": "                   ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50260": {
+      "content": "                  ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50261": {
+      "content": "                 ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50262": {
+      "content": "                ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50263": {
+      "content": "               ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50264": {
+      "content": "              ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50265": {
+      "content": "             ",
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+    },
+    "50266": {
+      "content": "            ",
+      "lstrip": false,
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+      "rstrip": false,
+      "single_word": false,
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+    },
+    "50267": {
+      "content": "           ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
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+      "special": false
+    },
+    "50268": {
+      "content": "          ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50269": {
+      "content": "         ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50270": {
+      "content": "        ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50271": {
+      "content": "       ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50272": {
+      "content": "      ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50273": {
+      "content": "     ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50274": {
+      "content": "    ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50275": {
+      "content": "   ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50276": {
+      "content": "  ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50277": {
+      "content": "|||EMAIL_ADDRESS|||",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50278": {
+      "content": "|||PHONE_NUMBER|||",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50279": {
+      "content": "|||IP_ADDRESS|||",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50280": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "|||IP_ADDRESS|||",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "|||IP_ADDRESS|||",
+  "extra_special_tokens": {},
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "<pad>",
+  "tokenizer_class": "GPTNeoXTokenizer",
+  "unk_token": null
+}

healed/healing_breadth/glean_math_keep25_seed1224_long768/wandb/debug.log

@@ -0,0 +1,24 @@
+2026-07-15 09:56:10,324 INFO    MainThread:259657 [wandb_setup.py:_flush():81] Current SDK version is 0.28.0
+2026-07-15 09:56:10,324 INFO    MainThread:259657 [wandb_setup.py:_flush():81] Configure stats pid to 259657
+2026-07-15 09:56:10,324 INFO    MainThread:259657 [wandb_setup.py:_flush():81] Loading settings from environment variables
+2026-07-15 09:56:10,324 INFO    MainThread:259657 [wandb_init.py:setup_run_log_directory():725] Logging user logs to outputs/healed/healing_breadth/glean_math_keep25_seed1224_long768/wandb/run-20260715_095610-zrxexeas/logs/debug.log
+2026-07-15 09:56:10,324 INFO    MainThread:259657 [wandb_init.py:setup_run_log_directory():726] Logging internal logs to outputs/healed/healing_breadth/glean_math_keep25_seed1224_long768/wandb/run-20260715_095610-zrxexeas/logs/debug-internal.log
+2026-07-15 09:56:10,324 INFO    MainThread:259657 [wandb_init.py:init():768] calling init triggers
+2026-07-15 09:56:10,324 INFO    MainThread:259657 [wandb_init.py:init():773] wandb.init called with sweep_config: {}
+config: {'student': 'outputs/pruned/glean-0125inst-math-keep25', 'teacher': 'allenai/OLMoE-1B-7B-0125-Instruct', 'training_mode': 'on-policy', 'kl_direction': 'reverse', 'dataset': 'allenai/Dolci-Instruct-RL', 'dataset_sources': 'omega,polaris,orz_math,MathSub,DAPO-Math', 'max_difficulty': 4, 'trajectories': 'outputs/teacher_trajectories/dolci_math_curated.jsonl', 'trajectory_dataset': 'allenai/Dolci-Instruct-RL', 'off_policy_frames': 'chat', 'off_policy_max_seq_len': 2048, 'topk_targets': None, 'max_loss_tokens': None, 'loss_tokens_per_step': 120000, 'teacher_device': 'cuda:0', 'student_device': 'cuda:1', 'lr': 3e-05, 'optimizer': 'adamw8bit', 'weight_decay': 0.1, 'epochs': 1, 'prompts_per_step': 272, 'group_size': 4, 'rollout_batch': 64, 'micro_batch': 4, 'max_new_tokens': 768, 'max_prompt_len': 1024, 'warmup_steps': 10, 'max_grad_norm': 1.0, 'eval_every': 10, 'gsm8k_every': 10, 'gsm8k_n': 64, 'gsm8k_batch': 16, 'gsm8k_frames': 'chat', 'save_every': 50, 'out_dir': 'outputs/healed/healing_breadth/glean_math_keep25_seed1224_long768', 'sweep': 500, 'wandb': True, 'wandb_project': 'glean-heal', 'wandb_run_name': 'heal-glean-math-keep25-seed1224-long768-step500', 'wandb_run_id': None, 'wandb_resume': None, 'wandb_mode': 'online', 'no_wandb_sync': False, 'debug': False, 'resume_from': 'outputs/healed/healing_breadth/glean_math_keep25_seed1224_long/step0150', 'start_step': 150, 'no_grad_checkpointing': False, 'seed': 1224, 'no_teacher_overlap': False, 'sync_checkpoints': False, '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_keep25_long768_1224', 'resolved_kl_direction': 'reverse', '_wandb': {}}
+2026-07-15 09:56:10,324 INFO    MainThread:259657 [wandb_init.py:init():816] starting backend
+2026-07-15 09:56:10,324 INFO    MainThread:259657 [wandb_init.py:init():831] sending inform_init request
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+2026-07-15 09:56:10,558 INFO    MainThread:259657 [wandb_init.py:init():906] updated telemetry
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+2026-07-15 18:37:29,895 INFO    MainThread:259657 [wandb_run.py:_finish():2285] finishing run hbfreed/glean-heal/zrxexeas
+2026-07-15 18:37:29,896 INFO    MainThread:259657 [wandb_run.py:_atexit_cleanup():2490] got exitcode: 0
+2026-07-15 18:37:29,897 INFO    MainThread:259657 [wandb_run.py:_restore():2472] restore
+2026-07-15 18:37:29,897 INFO    MainThread:259657 [wandb_run.py:_restore():2478] restore done
+2026-07-15 18:37:30,692 INFO    MainThread:259657 [wandb_run.py:_footer_sync_info():3895] logging synced files

healed/healing_breadth/glean_math_keep25_seed1224_long768/wandb/run-20260715_095610-zrxexeas/files/config.yaml

@@ -0,0 +1,272 @@
+_wandb:
+    value:
+        cli_version: 0.28.0
+        e:
+            w5rl3e6bz6rwdjhouk2h1lxz9agn8eme:
+                args:
+                    - --student
+                    - outputs/pruned/glean-0125inst-math-keep25
+                    - --resume-from
+                    - outputs/healed/healing_breadth/glean_math_keep25_seed1224_long/step0150
+                    - --start-step
+                    - "150"
+                    - --training-mode
+                    - on-policy
+                    - --kl-direction
+                    - reverse
+                    - --dataset
+                    - allenai/Dolci-Instruct-RL
+                    - --dataset-sources
+                    - omega,polaris,orz_math,MathSub,DAPO-Math
+                    - --max-difficulty
+                    - "4"
+                    - --teacher-device
+                    - cuda:0
+                    - --student-device
+                    - cuda:1
+                    - --lr
+                    - "3e-5"
+                    - --optimizer
+                    - adamw8bit
+                    - --epochs
+                    - "1"
+                    - --sweep
+                    - "500"
+                    - --prompts-per-step
+                    - "272"
+                    - --group-size
+                    - "4"
+                    - --micro-batch
+                    - "4"
+                    - --max-new-tokens
+                    - "768"
+                    - --loss-tokens-per-step
+                    - "120000"
+                    - --rollout-engine
+                    - vllm
+                    - --vllm-gpu
+                    - GPU-864c54df-0130-7780-e271-8a5551d1733f
+                    - --vllm-serve-bin
+                    - vllm-plugin/.venv25/bin/python
+                    - --vllm-refresh-every
+                    - "1"
+                    - --vllm-refresh-mode
+                    - reload
+                    - --vllm-live-dir
+                    - /dev/shm/glean_keep25_long768_1224
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+config: {'student': 'outputs/pruned/glean-0125inst-math-keep25', 'teacher': 'allenai/OLMoE-1B-7B-0125-Instruct', 'training_mode': 'on-policy', 'kl_direction': 'reverse', 'dataset': 'allenai/Dolci-Instruct-RL', 'dataset_sources': 'omega,polaris,orz_math,MathSub,DAPO-Math', 'max_difficulty': 4, 'trajectories': 'outputs/teacher_trajectories/dolci_math_curated.jsonl', 'trajectory_dataset': 'allenai/Dolci-Instruct-RL', 'off_policy_frames': 'chat', 'off_policy_max_seq_len': 2048, 'topk_targets': None, 'max_loss_tokens': None, 'loss_tokens_per_step': 120000, 'teacher_device': 'cuda:0', 'student_device': 'cuda:1', 'lr': 3e-05, 'optimizer': 'adamw8bit', 'weight_decay': 0.1, 'epochs': 1, 'prompts_per_step': 272, 'group_size': 4, 'rollout_batch': 64, 'micro_batch': 4, 'max_new_tokens': 768, 'max_prompt_len': 1024, 'warmup_steps': 10, 'max_grad_norm': 1.0, 'eval_every': 10, 'gsm8k_every': 10, 'gsm8k_n': 64, 'gsm8k_batch': 16, 'gsm8k_frames': 'chat', 'save_every': 50, 'out_dir': 'outputs/healed/healing_breadth/glean_math_keep25_seed1224_long768', 'sweep': 500, 'wandb': True, 'wandb_project': 'glean-heal', 'wandb_run_name': 'heal-glean-math-keep25-seed1224-long768-step500', 'wandb_run_id': None, 'wandb_resume': None, 'wandb_mode': 'online', 'no_wandb_sync': False, 'debug': False, 'resume_from': 'outputs/healed/healing_breadth/glean_math_keep25_seed1224_long/step0150', 'start_step': 150, 'no_grad_checkpointing': False, 'seed': 1224, 'no_teacher_overlap': False, 'sync_checkpoints': False, '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_keep25_long768_1224', 'resolved_kl_direction': 'reverse', '_wandb': {}}
+2026-07-15 09:56:10,324 INFO    MainThread:259657 [wandb_init.py:init():816] starting backend
+2026-07-15 09:56:10,324 INFO    MainThread:259657 [wandb_init.py:init():831] sending inform_init request
+2026-07-15 09:56:10,555 INFO    MainThread:259657 [wandb_init.py:init():836] backend started and connected
+2026-07-15 09:56:10,558 INFO    MainThread:259657 [wandb_init.py:init():906] updated telemetry
+2026-07-15 09:56:10,565 INFO    MainThread:259657 [wandb_init.py:init():929] communicating run to backend with 90.0 second timeout
+2026-07-15 09:56:11,000 INFO    MainThread:259657 [wandb_init.py:init():974] starting run threads in backend
+2026-07-15 09:56:11,130 INFO    MainThread:259657 [wandb_run.py:_console_start():2523] atexit reg
+2026-07-15 09:56:11,130 INFO    MainThread:259657 [wandb_run.py:_redirect():2373] redirect: wrap_raw
+2026-07-15 09:56:11,130 INFO    MainThread:259657 [wandb_run.py:_redirect():2442] Wrapping output streams.
+2026-07-15 09:56:11,130 INFO    MainThread:259657 [wandb_run.py:_redirect():2465] Redirects installed.
+2026-07-15 09:56:11,133 INFO    MainThread:259657 [wandb_init.py:init():1012] run started, returning control to user process
+2026-07-15 18:37:29,895 INFO    MainThread:259657 [wandb_run.py:_finish():2285] finishing run hbfreed/glean-heal/zrxexeas
+2026-07-15 18:37:29,896 INFO    MainThread:259657 [wandb_run.py:_atexit_cleanup():2490] got exitcode: 0
+2026-07-15 18:37:29,897 INFO    MainThread:259657 [wandb_run.py:_restore():2472] restore
+2026-07-15 18:37:29,897 INFO    MainThread:259657 [wandb_run.py:_restore():2478] restore done
+2026-07-15 18:37:30,692 INFO    MainThread:259657 [wandb_run.py:_footer_sync_info():3895] logging synced files

healed/healing_breadth/reap_math_keep75_seed1224/step0050/chat_template.jinja

@@ -0,0 +1,9 @@
+{{ bos_token }}{% for message in messages %}{% if message['role'] == 'system' %}{{ '<|system|>
+' + message['content'] + '
+' }}{% elif message['role'] == 'user' %}{{ '<|user|>
+' + message['content'] + '
+' }}{% elif message['role'] == 'assistant' %}{% if not loop.last %}{{ '<|assistant|>
+'  + message['content'] + eos_token + '
+' }}{% else %}{{ '<|assistant|>
+'  + message['content'] + eos_token }}{% endif %}{% endif %}{% if loop.last and add_generation_prompt %}{{ '<|assistant|>
+' }}{% endif %}{% endfor %}

healed/healing_breadth/reap_math_keep75_seed1224/step0050/config.json

@@ -0,0 +1,32 @@
+{
+  "architectures": [
+    "OlmoeForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "clip_qkv": null,
+  "dtype": "bfloat16",
+  "eos_token_id": 50279,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 1024,
+  "max_position_embeddings": 4096,
+  "model_type": "olmoe",
+  "norm_topk_prob": false,
+  "num_attention_heads": 16,
+  "num_experts": 48,
+  "num_experts_per_tok": 8,
+  "num_hidden_layers": 16,
+  "num_key_value_heads": 16,
+  "output_router_logits": false,
+  "pad_token_id": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "router_aux_loss_coef": 0.01,
+  "tie_word_embeddings": false,
+  "transformers_version": "4.57.6",
+  "use_cache": false,
+  "vocab_size": 50304
+}

healed/healing_breadth/reap_math_keep75_seed1224/step0050/configuration_pruned_olmoe.py

@@ -0,0 +1,27 @@
+"""Configuration for GLEAN-pruned OLMoE: variable-width, variable-count experts.
+"""
+
+from transformers.models.olmoe.configuration_olmoe import OlmoeConfig
+
+
+class PrunedOlmoeConfig(OlmoeConfig):
+    """OlmoeConfig plus a per-(layer, expert) width table.
+
+    ``expert_widths[l]`` lists the SwiGLU intermediate width of each surviving
+    expert in decoder layer ``l``, in expert order. Lists are ragged: layers
+    may keep different numbers of experts (deleted experts simply don't
+    appear — the router in layer ``l`` has ``len(expert_widths[l])`` rows),
+    and each width may differ (multiples of the GEMM block size, 128, for
+    variable-MegaBlocks execution). ``None`` means an unpruned model
+    (uniform ``num_experts`` × ``intermediate_size``).
+
+    The inherited ``num_experts`` / ``intermediate_size`` keep their ORIGINAL
+    (pre-pruning) values for provenance; the width table is authoritative for
+    the built architecture.
+    """
+
+    model_type = "pruned_olmoe"
+
+    def __init__(self, expert_widths: list[list[int]] | None = None, **kwargs):
+        super().__init__(**kwargs)
+        self.expert_widths = expert_widths

healed/healing_breadth/reap_math_keep75_seed1224/step0050/generation_config.json

@@ -0,0 +1,6 @@
+{
+  "_from_model_config": true,
+  "eos_token_id": 50279,
+  "pad_token_id": 1,
+  "transformers_version": "4.57.6"
+}

healed/healing_breadth/reap_math_keep75_seed1224/step0050/modeling_pruned_olmoe.py

@@ -0,0 +1,66 @@
+"""GLEAN-pruned OLMoE: HF-loadable model with ragged (variable-width) experts.
+
+Pattern follows hbfreed/variable-flex-olmo's PrunedFlexOlmoForCausalLM
+(docs/recon/prior-work-hbfreed.md), generalized from one scalar width to a
+per-(layer, expert) width table: ``super().__init__`` builds the uniform
+architecture from the config, then every MoE block is rebuilt to its pruned
+shape — surviving experts only, each at its own width, router sliced to
+match — so the state dict aligns exactly with what
+``glean.prune.prune_channels_global`` leaves behind.
+
+Caveat: ``output_router_logits=True`` (the load-balancing aux loss) assumes a
+uniform ``config.num_experts`` and is unsupported on ragged models.
+"""
+
+import torch.nn as nn
+from transformers.activations import ACT2FN
+from transformers.models.olmoe.modeling_olmoe import OlmoeForCausalLM
+
+from .configuration_pruned_olmoe import PrunedOlmoeConfig
+
+
+class RaggedOlmoeMLP(nn.Module):
+    """OlmoeMLP with an explicit intermediate width (SwiGLU, no biases)."""
+
+    def __init__(self, hidden_size: int, intermediate_size: int, hidden_act: str):
+        super().__init__()
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.gate_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
+        self.up_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
+        self.down_proj = nn.Linear(intermediate_size, hidden_size, bias=False)
+        self.act_fn = ACT2FN[hidden_act]
+
+    def forward(self, x):
+        return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+
+
+class PrunedOlmoeForCausalLM(OlmoeForCausalLM):
+    """OLMoE with per-layer surviving-expert lists at per-expert widths."""
+
+    config_class = PrunedOlmoeConfig
+
+    def __init__(self, config: PrunedOlmoeConfig):
+        super().__init__(config)
+        widths_table = getattr(config, "expert_widths", None)
+        if widths_table is None:
+            return  # unpruned: plain OLMoE
+        if len(widths_table) != len(self.model.layers):
+            raise ValueError(
+                f"expert_widths has {len(widths_table)} rows but the model has "
+                f"{len(self.model.layers)} decoder layers"
+            )
+        for layer, widths in zip(self.model.layers, widths_table):
+            if any(w <= 0 for w in widths):
+                raise ValueError("expert_widths must list surviving experts only (>0)")
+            block = layer.mlp
+            if len(widths) < block.top_k:
+                raise ValueError(
+                    f"a layer keeps {len(widths)} experts < top_k={block.top_k}"
+                )
+            block.num_experts = len(widths)
+            block.gate = nn.Linear(config.hidden_size, len(widths), bias=False)
+            block.experts = nn.ModuleList(
+                RaggedOlmoeMLP(config.hidden_size, w, config.hidden_act)
+                for w in widths
+            )

healed/healing_breadth/reap_math_keep75_seed1224/step0050/special_tokens_map.json

@@ -0,0 +1,23 @@
+{
+  "bos_token": {
+    "content": "|||IP_ADDRESS|||",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "|||IP_ADDRESS|||",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

healed/healing_breadth/reap_math_keep75_seed1224/step0050/tokenizer_config.json

@@ -0,0 +1,247 @@
+{
+  "add_bos_token": false,
+  "add_eos_token": false,
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<|padding|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50254": {
+      "content": "                        ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50255": {
+      "content": "                       ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50256": {
+      "content": "                      ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50257": {
+      "content": "                     ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50258": {
+      "content": "                    ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50259": {
+      "content": "                   ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50260": {
+      "content": "                  ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50261": {
+      "content": "                 ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50262": {
+      "content": "                ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50263": {
+      "content": "               ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50264": {
+      "content": "              ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50265": {
+      "content": "             ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50266": {
+      "content": "            ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50267": {
+      "content": "           ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50268": {
+      "content": "          ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50269": {
+      "content": "         ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50270": {
+      "content": "        ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50271": {
+      "content": "       ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50272": {
+      "content": "      ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50273": {
+      "content": "     ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50274": {
+      "content": "    ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50275": {
+      "content": "   ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50276": {
+      "content": "  ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50277": {
+      "content": "|||EMAIL_ADDRESS|||",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50278": {
+      "content": "|||PHONE_NUMBER|||",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50279": {
+      "content": "|||IP_ADDRESS|||",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50280": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "|||IP_ADDRESS|||",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "|||IP_ADDRESS|||",
+  "extra_special_tokens": {},
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "<pad>",
+  "tokenizer_class": "GPTNeoXTokenizer",
+  "unk_token": null
+}

healed/healing_breadth/reap_math_keep75_seed1224/wandb/debug-internal.log

@@ -0,0 +1,723 @@
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healed/healing_breadth/reap_math_keep75_seed1224/wandb/debug.log

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healed/healing_breadth/reap_math_keep75_seed1224/wandb/run-20260714_215914-wghixdgk/files/config.yaml

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+                      name: NVIDIA GeForce RTX 3090
+                      uuid: GPU-a6acf07f-31f5-618f-a5d0-c0017e7e2e27
+                    - architecture: Ampere
+                      cudaCores: 10496
+                      memoryTotal: "25769803776"
+                      name: NVIDIA GeForce RTX 3090
+                      uuid: GPU-864c54df-0130-7780-e271-8a5551d1733f
+                host: pop-os
+                memory:
+                    total: "134900731904"
+                os: Linux-7.0.11-76070011-generic-x86_64-with-glibc2.35
+                program: /home/henry/Documents/PythonProjects/variable-reap/scripts/11_distill_on_policy.py
+                python: CPython 3.12.12
+                root: outputs/healed/healing_breadth/reap_math_keep75_seed1224
+                startedAt: "2026-07-15T04:59:14.432681Z"
+                writerId: romac3fhrihntb85przynjqfngu3otm5
+        m:
+            - "1": step
+              "6":
+                - 3
+              "7": []
+            - "2": '*'
+              "5": 1
+              "6":
+                - 1
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+        python_version: 3.12.12
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+            "12": 0.28.0
+            "13": linux-x86_64
+dataset:
+    value: allenai/Dolci-Instruct-RL
+dataset_sources:
+    value: omega,polaris,orz_math,MathSub,DAPO-Math
+debug:
+    value: false
+epochs:
+    value: 1
+eval_every:
+    value: 10
+group_size:
+    value: 4
+gsm8k_batch:
+    value: 16
+gsm8k_every:
+    value: 0
+gsm8k_frames:
+    value: raw,chat
+gsm8k_n:
+    value: 64
+kl_direction:
+    value: reverse
+loss_tokens_per_step:
+    value: 120000
+lr:
+    value: 3e-05
+max_difficulty:
+    value: 4
+max_grad_norm:
+    value: 1
+max_loss_tokens:
+    value: null
+max_new_tokens:
+    value: 512
+max_prompt_len:
+    value: 1024
+micro_batch:
+    value: 2
+no_grad_checkpointing:
+    value: false
+no_teacher_overlap:
+    value: false
+no_wandb_sync:
+    value: false
+off_policy_frames:
+    value: chat
+off_policy_max_seq_len:
+    value: 2048
+optimizer:
+    value: adamw8bit
+out_dir:
+    value: outputs/healed/healing_breadth/reap_math_keep75_seed1224
+prompts_per_step:
+    value: 272
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+    value: reverse
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+rollout_batch:
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+rollout_engine:
+    value: vllm
+save_every:
+    value: 25
+seed:
+    value: 1224
+start_step:
+    value: 0
+student:
+    value: outputs/pruned/reap48-0125inst-math
+student_device:
+    value: cuda:1
+sweep:
+    value: 50
+sync_checkpoints:
+    value: false
+teacher:
+    value: allenai/OLMoE-1B-7B-0125-Instruct
+teacher_device:
+    value: cuda:0
+topk_targets:
+    value: null
+training_mode:
+    value: on-policy
+trajectories:
+    value: outputs/teacher_trajectories/dolci_math_curated.jsonl
+trajectory_dataset:
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+vllm_gpu:
+    value: GPU-864c54df-0130-7780-e271-8a5551d1733f
+vllm_gpu_mem_util:
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+vllm_port:
+    value: 8377
+vllm_refresh_every:
+    value: 1
+vllm_refresh_mode:
+    value: reload
+vllm_serve_bin:
+    value: vllm-plugin/.venv25/bin/python
+wandb:
+    value: true
+wandb_mode:
+    value: online
+wandb_project:
+    value: glean-heal
+wandb_resume:
+    value: null
+wandb_run_id:
+    value: null
+wandb_run_name:
+    value: heal-reap_math_keep75-seed1224-run1
+warmup_steps:
+    value: 10
+weight_decay:
+    value: 0.1

healed/healing_breadth/reap_math_keep75_seed1224/wandb/run-20260714_215914-wghixdgk/files/output.log

@@ -0,0 +1,70 @@
+Loading checkpoint shards: 100%|██████████| 3/3 [00:00<00:00,  9.74it/s]
+Loading checkpoint shards: 100%|██████████| 3/3 [00:02<00:00,  1.07it/s]
+starting vllm rollout server on GPU GPU-864c54df-0130-7780-e271-8a5551d1733f (port 8377) ...
+vllm server healthy in 24s
+dataset_source filter ['omega', 'polaris', 'orz_math', 'mathsub', 'dapo-math'] -> 63998 prompts
+Filter: 100%|██████████| 63998/63998 [00:07<00:00, 8132.74 examples/s]
+difficulty <= 4 -> 63998 prompts
+WARNING: difficulty filter removed nothing — the slice likely carries difficulty=None (Dolci math sources do), so the teacher-competence guard is NOT in effect
+Map: 100%|██████████| 63998/63998 [00:21<00:00, 3030.78 examples/s]
+Filter: 100%|██████████| 63998/63998 [00:02<00:00, 25994.83 examples/s]
+63977 prompts | 940 steps/epoch | 50 total steps | student params 5.31B | teacher overlap=True
+{"step": 1, "epoch": 0, "training_mode": "on-policy", "reverse_kl": 0.22007248604670168, "tokens": 120000, "cumulative_loss_tokens": 120000, "grad_norm": 4.15625, "lr": 6e-06, "finish_rate": 0.079, "comp_len": 502.1, "t_data_s": 0.0, "t_rollout_s": 31.9, "t_step_s": 123.1, "t_refresh_s": 2.7, "mem_gb": 21.1}
+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 maximize the number of maples \\( m \\) while ensuring that no two maples are exactly 5 trees apart.\n\nGiven:\n1. Total number of trees \\( m + l = 75 \\)\n2. No two maples '
+{"step": 2, "epoch": 0, "training_mode": "on-policy", "reverse_kl": 0.19900615173100183, "tokens": 120000, "cumulative_loss_tokens": 240000, "grad_norm": 3.890625, "lr": 9e-06, "finish_rate": 0.1, "comp_len": 500.0, "t_data_s": 0.0, "t_rollout_s": 32.2, "t_step_s": 105.8, "t_refresh_s": 2.7, "mem_gb": 21.25}
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+[eval step 10] sample: "To solve this problem, we need to maximize the number of maples \\( m \\) such that there are no two maples exactly 5 trees apart. Let's denote the positions of the maples as \\( x_1, x_2, \\ldots, x_m \\)"
+{"step": 11, "epoch": 0, "training_mode": "on-policy", "reverse_kl": 0.08126618308176597, "tokens": 120000, "cumulative_loss_tokens": 1320000, "grad_norm": 0.9453125, "lr": 3e-05, "finish_rate": 0.196, "comp_len": 480.0, "t_data_s": 0.0, "t_rollout_s": 30.9, "t_step_s": 103.0, "t_refresh_s": 2.7, "mem_gb": 21.21}
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+{"step": 20, "epoch": 0, "training_mode": "on-policy", "reverse_kl": 0.0648774014983947, "tokens": 120000, "cumulative_loss_tokens": 2400000, "grad_norm": 0.859375, "lr": 3e-05, "finish_rate": 0.163, "comp_len": 489.8, "t_data_s": 0.0, "t_rollout_s": 31.6, "t_step_s": 103.0, "t_refresh_s": 2.7, "mem_gb": 21.27}
+[eval step 20] sample: "To solve this problem, we need to maximize the number of maples \\( m \\) while ensuring that there are no two maples that are exactly 5 trees apart. \n\nLet's break down the problem:\n\n1. **Define Variabl"
+{"step": 21, "epoch": 0, "training_mode": "on-policy", "reverse_kl": 0.057967641365962726, "tokens": 120000, "cumulative_loss_tokens": 2520000, "grad_norm": 1.0625, "lr": 3e-05, "finish_rate": 0.259, "comp_len": 478.1, "t_data_s": 0.0, "t_rollout_s": 30.2, "t_step_s": 102.4, "t_refresh_s": 2.7, "mem_gb": 21.21}
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+checkpoint snapshot queued -> outputs/healed/healing_breadth/reap_math_keep75_seed1224/step0025
+{"step": 26, "epoch": 0, "training_mode": "on-policy", "reverse_kl": 0.062316528513189404, "tokens": 120000, "cumulative_loss_tokens": 3120000, "grad_norm": 1.015625, "lr": 3e-05, "finish_rate": 0.17, "comp_len": 485.8, "t_data_s": 0.0, "t_rollout_s": 31.6, "t_step_s": 104.3, "t_refresh_s": 2.7, "mem_gb": 21.29}
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+{"step": 29, "epoch": 0, "training_mode": "on-policy", "reverse_kl": 0.05680957672783794, "tokens": 120000, "cumulative_loss_tokens": 3480000, "grad_norm": 0.7265625, "lr": 3e-05, "finish_rate": 0.127, "comp_len": 491.8, "t_data_s": 0.0, "t_rollout_s": 32.0, "t_step_s": 104.9, "t_refresh_s": 2.7, "mem_gb": 21.29}
+{"step": 30, "epoch": 0, "training_mode": "on-policy", "reverse_kl": 0.059886360732543595, "tokens": 120000, "cumulative_loss_tokens": 3600000, "grad_norm": 0.68359375, "lr": 3e-05, "finish_rate": 0.227, "comp_len": 485.8, "t_data_s": 0.0, "t_rollout_s": 30.8, "t_step_s": 102.0, "t_refresh_s": 2.7, "mem_gb": 21.25}
+[eval step 30] sample: "To solve this problem, we need to determine the maximum number of maples (\\(M\\)) that can be planted along the alley such that there are no two maples exactly 5 trees apart.\n\nLet's break down the prob"
+{"step": 31, "epoch": 0, "training_mode": "on-policy", "reverse_kl": 0.05055032962113619, "tokens": 120000, "cumulative_loss_tokens": 3720000, "grad_norm": 0.640625, "lr": 3e-05, "finish_rate": 0.213, "comp_len": 491.8, "t_data_s": 0.0, "t_rollout_s": 31.0, "t_step_s": 102.2, "t_refresh_s": 2.7, "mem_gb": 21.34}
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+[eval step 40] sample: "To solve this problem, we need to maximize the number of maples \\( m \\) such that there are no two maples exactly 5 trees apart. Let's denote the number of larches by \\( l \\). According to the problem"
+{"step": 41, "epoch": 0, "training_mode": "on-policy", "reverse_kl": 0.04683446280825883, "tokens": 120000, "cumulative_loss_tokens": 4920000, "grad_norm": 0.79296875, "lr": 3e-05, "finish_rate": 0.181, "comp_len": 481.9, "t_data_s": 0.0, "t_rollout_s": 31.3, "t_step_s": 104.6, "t_refresh_s": 2.7, "mem_gb": 21.33}
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+{"step": 43, "epoch": 0, "training_mode": "on-policy", "reverse_kl": 0.0487479245382982, "tokens": 120000, "cumulative_loss_tokens": 5160000, "grad_norm": 0.84375, "lr": 3e-05, "finish_rate": 0.172, "comp_len": 491.8, "t_data_s": 0.0, "t_rollout_s": 31.1, "t_step_s": 101.6, "t_refresh_s": 2.7, "mem_gb": 21.22}
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+{"step": 45, "epoch": 0, "training_mode": "on-policy", "reverse_kl": 0.052038926035289965, "tokens": 120000, "cumulative_loss_tokens": 5400000, "grad_norm": 0.87109375, "lr": 3e-05, "finish_rate": 0.152, "comp_len": 493.8, "t_data_s": 0.0, "t_rollout_s": 31.1, "t_step_s": 101.6, "t_refresh_s": 2.7, "mem_gb": 21.2}
+{"step": 46, "epoch": 0, "training_mode": "on-policy", "reverse_kl": 0.051263596718541034, "tokens": 120000, "cumulative_loss_tokens": 5520000, "grad_norm": 0.65234375, "lr": 3e-05, "finish_rate": 0.173, "comp_len": 493.8, "t_data_s": 0.0, "t_rollout_s": 31.5, "t_step_s": 103.1, "t_refresh_s": 2.7, "mem_gb": 21.24}
+{"step": 47, "epoch": 0, "training_mode": "on-policy", "reverse_kl": 0.051921346824189335, "tokens": 120000, "cumulative_loss_tokens": 5640000, "grad_norm": 0.74609375, "lr": 3e-05, "finish_rate": 0.219, "comp_len": 478.1, "t_data_s": 0.0, "t_rollout_s": 30.8, "t_step_s": 103.4, "t_refresh_s": 2.7, "mem_gb": 21.25}
+{"step": 48, "epoch": 0, "training_mode": "on-policy", "reverse_kl": 0.05030671559885765, "tokens": 120000, "cumulative_loss_tokens": 5760000, "grad_norm": 0.65625, "lr": 3e-05, "finish_rate": 0.136, "comp_len": 495.9, "t_data_s": 0.0, "t_rollout_s": 31.9, "t_step_s": 103.4, "t_refresh_s": 2.7, "mem_gb": 21.24}
+{"step": 49, "epoch": 0, "training_mode": "on-policy", "reverse_kl": 0.048079004754545164, "tokens": 120000, "cumulative_loss_tokens": 5880000, "grad_norm": 0.59375, "lr": 3e-05, "finish_rate": 0.095, "comp_len": 497.9, "t_data_s": 0.0, "t_rollout_s": 31.6, "t_step_s": 102.4, "t_refresh_s": 2.7, "mem_gb": 21.28}
+{"step": 50, "epoch": 0, "training_mode": "on-policy", "reverse_kl": 0.051015459259475274, "tokens": 120000, "cumulative_loss_tokens": 6000000, "grad_norm": 0.78515625, "lr": 3e-05, "finish_rate": 0.136, "comp_len": 493.8, "t_data_s": 0.0, "t_rollout_s": 32.2, "t_step_s": 84.7, "t_refresh_s": 0.0, "mem_gb": 21.29}
+[eval step 50] sample: "To solve this problem, we need to maximize the number of maples \\( m \\) such that there are no two maples exactly 5 trees apart. Let's break down the problem step-by-step:\n\n1. **Understand the Constra"
+checkpoint snapshot queued -> outputs/healed/healing_breadth/reap_math_keep75_seed1224/step0050

healed/healing_breadth/reap_math_keep75_seed1224/wandb/run-20260714_215914-wghixdgk/files/requirements.txt

@@ -0,0 +1,123 @@
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