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LTA_openwebtext_dualt/logs/basinbankmix_debug/lta_owt_basinbankmix_smoke4gpu_20260514_152205.log

@@ -0,0 +1,111 @@
+{
+  "device": "cuda:0",
+  "rank": 0,
+  "world_size": 4,
+  "samples": "owt_cached_chunks:8734897",
+  "vocab_size": 50257,
+  "tokenizer_vocab_size": 50257,
+  "save_dir": "runs_debug/lta_owt_basinbankmix_smoke4gpu_20260514_152205",
+  "batch_size": 4,
+  "grad_accum": 1,
+  "effective_batch_size": 16,
+  "global_batch_size": 16,
+  "lr_schedule": "constant_warmup",
+  "optimizer": "adamw",
+  "warmup_steps": 1,
+  "min_lr": 6e-05,
+  "weight_decay": 0.0,
+  "adamw_param_groups": "nanogpt",
+  "adam_beta1": 0.9,
+  "adam_beta2": 0.95,
+  "adam_eps": 1e-08,
+  "muon_momentum": 0.95,
+  "muon_ns_steps": 5,
+  "muon_update_scale": 1.0,
+  "ema_decay": 0.0,
+  "ema_start_step": 0,
+  "model_type": "ddit",
+  "dual_t": true,
+  "corrupt_t_mode": "same",
+  "corrupt_min_t": null,
+  "corrupt_max_t": null,
+  "prefix_block_prob": 0.0,
+  "prefix_block_len": 128,
+  "mask_ratio_floor_schedule": "none",
+  "dirichlet_endpoint_mode": "categorical_dual_t",
+  "dirichlet_semantic_t_mode": "same",
+  "dirichlet_semantic_t_value": 0.0,
+  "dirichlet_semantic_t_curve": "linear",
+  "dirichlet_semantic_t_power": 1.0,
+  "endpoint_sequence_random_prob_alpha": 0.0,
+  "categorical_wrong_from_full_vocab": false,
+  "categorical_wrong_from_batch_valid_tokens": false,
+  "categorical_wrong_basin_token_ids": "284,326,340,307,11,314,1949,262,2808,1295,318,12,423,13,2608,290,1842,198,356,2681,642,34,1011,2310,287,286,1577,3853,352,345,467,25,1596,2579,257,678,2555,4747,484,513,16,787,1679,606,460,1110,1542,18,1498,362,357,767,2107,1026,779,17,373,9752,2077,35,1271,532,860,1394",
+  "categorical_wrong_basin_prob": 0.0,
+  "categorical_wrong_unigram_prob": 0.3,
+  "categorical_wrong_uniform_prob": 0.6,
+  "categorical_wrong_basin_shared_prob": 0.1,
+  "mask_mixture_original_prob": 0.0,
+  "mask_mixture_lowk_prob": 0.0,
+  "mask_mixture_lowcorrupt_prob": 0.0,
+  "mask_mixture_block_prob": 0.0,
+  "mask_mixture_all_prob": 0.0,
+  "mask_mixture_lowk_clean_tokens": "1,2,4,8,16,32,64",
+  "mask_mixture_lowcorrupt_tokens": "1,2,4,8,16,32,64",
+  "mask_mixture_block_tokens": "64,128",
+  "simplex_bridge_sampler": "dirichlet",
+  "logistic_normal_sigma_min": 0.18,
+  "logistic_normal_sigma_max": 2.2,
+  "logistic_normal_tau_min": 0.65,
+  "logistic_normal_tau_max": 1.15,
+  "torch_compile": false,
+  "compile_mode": "max-autotune",
+  "state_format": "prob",
+  "target_loss": "hard_ce",
+  "meanflow_weight": 0.0,
+  "rollout_train_prob": 0.0,
+  "rollout_train_steps": 1,
+  "rollout_train_infer_steps": 64,
+  "rollout_train_temp": 1.45,
+  "rollout_train_max_gamma": 1.0,
+  "rollout_train_corrupt_only": true,
+  "rollout_train_samplewise": false,
+  "rollout_train_compute_always": false,
+  "bridge_noise_init": "logistic_normal",
+  "noise_sigma": -1.0,
+  "allow_tf32": true,
+  "activation_checkpointing": false,
+  "activation_checkpoint_interval": 1,
+  "activation_checkpoint_scope": "block",
+  "ddp_static_graph": false,
+  "ddp_gradient_as_bucket_view": true,
+  "blocking_data_transfer": false,
+  "dataloader_prefetch_factor": 2,
+  "full_train_stats": false,
+  "record_pad_truncate": false,
+  "record_add_eos": false,
+  "record_add_special_tokens": false,
+  "record_pad_token": "pad",
+  "record_shuffle_buffer": 10000,
+  "wrap": true,
+  "wrap_mode": "stream",
+  "wrap_record_buffer_size": 200,
+  "owt_cached_chunks": true,
+  "owt_chunk_cache_dir": "/e2e-data/evad-tech-vla/wanghan58/data/small_benchmarks/langflow_2604_11748/openwebtext_lta_cached_chunks/gpt2_len1024_train_minus_100k",
+  "owt_chunk_cache_rebuild": false,
+  "owt_chunk_cache_write_batch": 4096,
+  "owt_exact_repeat_per_chunk": 0,
+  "online_chunk_shuffle": false,
+  "online_chunk_shuffle_buffer": 10000,
+  "openwebtext_split": "train_minus_100k",
+  "detokenizer": "auto",
+  "resolved_detokenizer": null,
+  "num_workers": 0,
+  "latest_every": 1000,
+  "resume_path": ""
+}
+step=1 micro_steps=1 elapsed=1.3s lr=3.000000e-04 loss=10.8125 loss_recon=10.8125 loss_meanflow=0.0000 mean_model_t=0.2654 mean_corrupt_t=0.2654 mean_loss_t_weight=1.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 out_w_norm=0.0000 out_g_norm=0.8875 acc_all=0.0010 acc_corrupt=0.0008 corrupt_frac=0.5942 loss_all=10.8125 loss_corrupt=10.8125 acc_corrupt_t_0p0_0p2=0.0000 corrupt_frac_t_0p0_0p2=0.3365 acc_corrupt_t_0p2_0p4=0.0016 corrupt_frac_t_0p2_0p4=0.2494 acc_corrupt_t_0p6_0p8=0.0010 corrupt_frac_t_0p6_0p8=0.4141 wrong_frac=0.6315 init_acc_corrupt=0.3591 init_gold_top10=0.3632 init_gold_top100=0.3973
+step=2 micro_steps=2 elapsed=0.1s lr=3.000000e-04 loss=10.7812 loss_recon=10.7812 loss_meanflow=0.0000 mean_model_t=0.3494 mean_corrupt_t=0.3494 mean_loss_t_weight=1.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 out_w_norm=1.7929 out_g_norm=1.1412 acc_all=0.2507 acc_corrupt=0.1279 corrupt_frac=0.5098 loss_all=10.7415 loss_corrupt=10.7812 acc_corrupt_t_0p0_0p2=0.0110 corrupt_frac_t_0p0_0p2=0.3491 acc_corrupt_t_0p2_0p4=0.1098 corrupt_frac_t_0p2_0p4=0.2486 acc_corrupt_t_0p6_0p8=0.2405 corrupt_frac_t_0p6_0p8=0.4023 wrong_frac=0.6140 init_acc_corrupt=0.3348 init_gold_top10=0.3740 init_gold_top100=0.4148
+step=3 micro_steps=3 elapsed=0.1s lr=3.000000e-04 loss=10.7211 loss_recon=10.7211 loss_meanflow=0.0000 mean_model_t=0.2648 mean_corrupt_t=0.2648 mean_loss_t_weight=1.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 out_w_norm=3.2734 out_g_norm=1.3191 acc_all=0.4695 acc_corrupt=0.1768 corrupt_frac=0.3909 loss_all=10.6144 loss_corrupt=10.7211 acc_corrupt_t_0p0_0p2=0.0632 corrupt_frac_t_0p0_0p2=0.2274 acc_corrupt_t_0p2_0p4=0.1393 corrupt_frac_t_0p2_0p4=0.3810 acc_corrupt_t_0p4_0p6=0.2791 corrupt_frac_t_0p4_0p6=0.3916 wrong_frac=0.7046 init_acc_corrupt=0.2142 init_gold_top10=0.2911 init_gold_top100=0.3392
+step=4 micro_steps=4 elapsed=0.1s lr=3.000000e-04 loss=10.4237 loss_recon=10.4237 loss_meanflow=0.0000 mean_model_t=0.5641 mean_corrupt_t=0.5641 mean_loss_t_weight=1.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 out_w_norm=4.6708 out_g_norm=1.8258 acc_all=0.4395 acc_corrupt=0.3441 corrupt_frac=0.6514 loss_all=10.3969 loss_corrupt=10.4237 acc_corrupt_t_0p0_0p2=0.0599 corrupt_frac_t_0p0_0p2=0.2691 acc_corrupt_t_0p4_0p6=0.2872 corrupt_frac_t_0p4_0p6=0.3171 acc_corrupt_t_0p6_0p8=0.4082 corrupt_frac_t_0p6_0p8=0.1001 acc_corrupt_t_0p8_1p0=0.6249 corrupt_frac_t_0p8_1p0=0.3137 wrong_frac=0.4258 init_acc_corrupt=0.5315 init_gold_top10=0.5645 init_gold_top100=0.6008
+step=5 micro_steps=5 elapsed=0.1s lr=3.000000e-04 loss=10.2527 loss_recon=10.2527 loss_meanflow=0.0000 mean_model_t=0.6118 mean_corrupt_t=0.6118 mean_loss_t_weight=1.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 out_w_norm=5.9812 out_g_norm=2.4594 acc_all=0.2424 acc_corrupt=0.1392 corrupt_frac=0.5701 loss_all=10.2071 loss_corrupt=10.2527 acc_corrupt_t_0p2_0p4=0.0524 corrupt_frac_t_0p2_0p4=0.3842 acc_corrupt_t_0p6_0p8=0.1933 corrupt_frac_t_0p6_0p8=0.6158 wrong_frac=0.4313 init_acc_corrupt=0.5388 init_gold_top10=0.5687 init_gold_top100=0.5704

LTA_openwebtext_dualt/logs/eval_lm1b_200k_methods_genppl_20260506_rerun.log

@@ -0,0 +1,65 @@
+[load] mdlm_200k step=200000 ckpt=runs/lm1b_mdlm_unified_ddit_small_len128_gbs512_8gpu_1m_20260505_repro/step_0200000.pt
+[mdlm] generated 16/256
+[mdlm] generated 32/256
+[mdlm] generated 48/256
+[mdlm] generated 64/256
+[mdlm] generated 80/256
+[mdlm] generated 96/256
+[mdlm] generated 112/256
+[mdlm] generated 128/256
+[mdlm] generated 144/256
+[mdlm] generated 160/256
+[mdlm] generated 176/256
+[mdlm] generated 192/256
+[mdlm] generated 208/256
+[mdlm] generated 224/256
+[mdlm] generated 240/256
+[mdlm] generated 256/256
+[summary] {"type": "summary", "name": "mdlm_200k", "kind": "mdlm", "checkpoint": "runs/lm1b_mdlm_unified_ddit_small_len128_gbs512_8gpu_1m_20260505_repro/step_0200000.pt", "step": 200000, "decode": {"kind": "mdlm", "steps": 256, "decode_rule": "confidence_unmask", "start": "all_mask", "n_samples": 256, "seed": 20260506}, "raw_genppl": {"ppl": 7.612718874390907, "nll_per_token": 2.0298203845952183, "tokens": 38144, "kept_samples": 256, "total_samples": 256, "empty_rate": 0.0, "skipped_samples": 0}, "stripped_genppl": {"ppl": 9.005877695089472, "nll_per_token": 2.19787744140625, "tokens": 32000, "kept_samples": 256, "total_samples": 256, "empty_rate": 0.0, "skipped_samples": 0}, "diversity": {"sample_entropy": 3.3737493108092327, "unique_tokens": 50, "token_count": 32768, "distinct_1": 0.00152587890625, "distinct_2": 0.0022145669291338582, "top_token_mass": 0.1875}}
+[load] duo_200k step=200000 ckpt=runs/lm1b_duo_unified_ddit_small_len128_gbs512_8gpu_1m_20260505_repro/step_0200000.pt
+Traceback (most recent call last):
+  File "/e2e-data/evad-tech-vla/wanghan58/workspace/LTA_openwebtext_dualt/scripts/eval_lm1b_200k_methods_genppl_20260506.py", line 587, in <module>
+    main()
+  File "/e2e-data/evad-tech-vla/wanghan58/workspace/LTA_openwebtext_dualt/scripts/eval_lm1b_200k_methods_genppl_20260506.py", line 538, in main
+    ids, texts, decode = decode_dense_baseline(
+                         ^^^^^^^^^^^^^^^^^^^^^^
+  File "/e2e-data/evad-tech-vla/wanghan58/workspace/LTA_openwebtext_dualt/scripts/eval_lm1b_200k_methods_genppl_20260506.py", line 232, in decode_dense_baseline
+    logits = model(state_for_model(model, probs, eps), t, attn).float()
+             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py", line 1739, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py", line 1750, in _call_impl
+    return forward_call(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/e2e-data/evad-tech-vla/wanghan58/workspace/LTA_openwebtext_dualt/flowtext_lab/model.py", line 302, in forward
+    x = block(x, rotary_cos_sin, c)
+        ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py", line 1739, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py", line 1750, in _call_impl
+    return forward_call(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/e2e-data/evad-tech-vla/wanghan58/workspace/LTA_openwebtext_dualt/flowtext_lab/model.py", line 187, in forward
+    x = x + gate_mlp * F.dropout(self.mlp(h), p=self.dropout, training=self.training)
+                                 ^^^^^^^^^^^
+  File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py", line 1739, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py", line 1750, in _call_impl
+    return forward_call(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/container.py", line 250, in forward
+    input = module(input)
+            ^^^^^^^^^^^^^
+  File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py", line 1739, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py", line 1750, in _call_impl
+    return forward_call(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/linear.py", line 125, in forward
+    return F.linear(input, self.weight, self.bias)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 12.00 MiB. GPU 0 has a total capacity of 95.22 GiB of which 9.88 MiB is free. Process 746240 has 19.59 GiB memory in use. Process 1128627 has 75.61 GiB memory in use. Of the allocated memory 74.90 GiB is allocated by PyTorch, and 294.45 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)

LTA_openwebtext_dualt/logs/infer_owt_t5_rollin450k_dualline_state_steps128_n16_20260522.log

@@ -0,0 +1,51 @@
+[ckpt] runs/lta_owt_t5_adaln_adamw_wd0p1_rollin_p50_randk0_3_uniformt_temp1_synct_gbs512_2node8gpu_1m_t-20260518224737-tftgw/step_0450000.pt step=450000
+[decode-base] n=16 max_len=1024 steps=128 model_t=post
+[decode-time] schedule=linear s=[0.0,0.25] gumbel=(2.2,0.8) force_final=True t0=0.000000 t_mid=0.500000 t_end=1.000000 dt_mean=0.007812 dt_max=0.007812
+Traceback (most recent call last):
+  File "/e2e-data/evad-tech-vla/wanghan58/workspace/LTA_openwebtext_dualt/scripts/standard_genppl_entropy_latest_decode.py", line 826, in <module>
+    main()
+  File "/e2e-data/evad-tech-vla/wanghan58/workspace/LTA_openwebtext_dualt/scripts/standard_genppl_entropy_latest_decode.py", line 673, in main
+    ids, raw_texts = decode_samples(
+                     ^^^^^^^^^^^^^^^
+  File "/usr/local/lib/python3.12/dist-packages/torch/utils/_contextlib.py", line 116, in decorate_context
+    return func(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^
+  File "/e2e-data/evad-tech-vla/wanghan58/workspace/LTA_openwebtext_dualt/scripts/standard_genppl_entropy_latest_decode.py", line 354, in decode_samples
+    probs = make_started_state(
+            ^^^^^^^^^^^^^^^^^^^
+  File "/e2e-data/evad-tech-vla/wanghan58/workspace/LTA_openwebtext_dualt/scripts/standard_genppl_entropy_latest_decode.py", line 264, in make_started_state
+    return sample_noise_simplex(
+           ^^^^^^^^^^^^^^^^^^^^^
+  File "/e2e-data/evad-tech-vla/wanghan58/workspace/LTA_openwebtext_dualt/flowtext_lab/decode.py", line 32, in sample_noise_simplex
+    sigma = resolve_noise_sigma(vocab_size, target_prob, noise_sigma)
+            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/e2e-data/evad-tech-vla/wanghan58/workspace/LTA_openwebtext_dualt/flowtext_lab/decode.py", line 16, in resolve_noise_sigma
+    return float(margin_for_target_prob(vocab_size, target_prob))
+                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/e2e-data/evad-tech-vla/wanghan58/workspace/LTA_openwebtext_dualt/flowtext_lab/bridges.py", line 161, in margin_for_target_prob
+    raise ValueError(f"target_prob must be in (0, 1), got {q}")
+ValueError: target_prob must be in (0, 1), got 1.0
+[ckpt] runs/lta_owt_t5_adaln_adamw_wd0p1_rollin_p50_randk0_3_uniformt_temp1_synct_gbs512_2node8gpu_1m_t-20260518224737-tftgw/step_0450000.pt step=450000
+[decode-base] n=16 max_len=1024 steps=128 model_t=post
+[decode-time] schedule=linear s=[0.0,0.25] gumbel=(2.2,0.8) force_final=True t0=0.000000 t_mid=0.500000 t_end=1.000000 dt_mean=0.007812 dt_max=0.007812
+[decode] temp=1.00 final=state rule=dual_line_resample support=1 semantic=1 anchor=state cfg=0/1@0:uniform decode_freq_penalty=0/0/0-1^1 final_sample=argmax/1/k64/p0.95 freq_penalty=0/0/0 start_t=0 start_init=noise time_path=0.0000->1.0000 generated 4/16
+[decode] temp=1.00 final=state rule=dual_line_resample support=1 semantic=1 anchor=state cfg=0/1@0:uniform decode_freq_penalty=0/0/0-1^1 final_sample=argmax/1/k64/p0.95 freq_penalty=0/0/0 start_t=0 start_init=noise time_path=0.0000->1.0000 generated 8/16
+[decode] temp=1.00 final=state rule=dual_line_resample support=1 semantic=1 anchor=state cfg=0/1@0:uniform decode_freq_penalty=0/0/0-1^1 final_sample=argmax/1/k64/p0.95 freq_penalty=0/0/0 start_t=0 start_init=noise time_path=0.0000->1.0000 generated 12/16
+[decode] temp=1.00 final=state rule=dual_line_resample support=1 semantic=1 anchor=state cfg=0/1@0:uniform decode_freq_penalty=0/0/0-1^1 final_sample=argmax/1/k64/p0.95 freq_penalty=0/0/0 start_t=0 start_init=noise time_path=0.0000->1.0000 generated 16/16
+[decode] temp=1.30 final=state rule=dual_line_resample support=1 semantic=1 anchor=state cfg=0/1@0:uniform decode_freq_penalty=0/0/0-1^1 final_sample=argmax/1/k64/p0.95 freq_penalty=0/0/0 start_t=0 start_init=noise time_path=0.0000->1.0000 generated 4/16
+[decode] temp=1.30 final=state rule=dual_line_resample support=1 semantic=1 anchor=state cfg=0/1@0:uniform decode_freq_penalty=0/0/0-1^1 final_sample=argmax/1/k64/p0.95 freq_penalty=0/0/0 start_t=0 start_init=noise time_path=0.0000->1.0000 generated 8/16
+[decode] temp=1.30 final=state rule=dual_line_resample support=1 semantic=1 anchor=state cfg=0/1@0:uniform decode_freq_penalty=0/0/0-1^1 final_sample=argmax/1/k64/p0.95 freq_penalty=0/0/0 start_t=0 start_init=noise time_path=0.0000->1.0000 generated 12/16
+[decode] temp=1.30 final=state rule=dual_line_resample support=1 semantic=1 anchor=state cfg=0/1@0:uniform decode_freq_penalty=0/0/0-1^1 final_sample=argmax/1/k64/p0.95 freq_penalty=0/0/0 start_t=0 start_init=noise time_path=0.0000->1.0000 generated 16/16
+[decode] temp=1.45 final=state rule=dual_line_resample support=1 semantic=1 anchor=state cfg=0/1@0:uniform decode_freq_penalty=0/0/0-1^1 final_sample=argmax/1/k64/p0.95 freq_penalty=0/0/0 start_t=0 start_init=noise time_path=0.0000->1.0000 generated 4/16
+[decode] temp=1.45 final=state rule=dual_line_resample support=1 semantic=1 anchor=state cfg=0/1@0:uniform decode_freq_penalty=0/0/0-1^1 final_sample=argmax/1/k64/p0.95 freq_penalty=0/0/0 start_t=0 start_init=noise time_path=0.0000->1.0000 generated 8/16
+[decode] temp=1.45 final=state rule=dual_line_resample support=1 semantic=1 anchor=state cfg=0/1@0:uniform decode_freq_penalty=0/0/0-1^1 final_sample=argmax/1/k64/p0.95 freq_penalty=0/0/0 start_t=0 start_init=noise time_path=0.0000->1.0000 generated 12/16
+[decode] temp=1.45 final=state rule=dual_line_resample support=1 semantic=1 anchor=state cfg=0/1@0:uniform decode_freq_penalty=0/0/0-1^1 final_sample=argmax/1/k64/p0.95 freq_penalty=0/0/0 start_t=0 start_init=noise time_path=0.0000->1.0000 generated 16/16
+[decode] temp=1.60 final=state rule=dual_line_resample support=1 semantic=1 anchor=state cfg=0/1@0:uniform decode_freq_penalty=0/0/0-1^1 final_sample=argmax/1/k64/p0.95 freq_penalty=0/0/0 start_t=0 start_init=noise time_path=0.0000->1.0000 generated 4/16
+[decode] temp=1.60 final=state rule=dual_line_resample support=1 semantic=1 anchor=state cfg=0/1@0:uniform decode_freq_penalty=0/0/0-1^1 final_sample=argmax/1/k64/p0.95 freq_penalty=0/0/0 start_t=0 start_init=noise time_path=0.0000->1.0000 generated 8/16
+[decode] temp=1.60 final=state rule=dual_line_resample support=1 semantic=1 anchor=state cfg=0/1@0:uniform decode_freq_penalty=0/0/0-1^1 final_sample=argmax/1/k64/p0.95 freq_penalty=0/0/0 start_t=0 start_init=noise time_path=0.0000->1.0000 generated 12/16
+[decode] temp=1.60 final=state rule=dual_line_resample support=1 semantic=1 anchor=state cfg=0/1@0:uniform decode_freq_penalty=0/0/0-1^1 final_sample=argmax/1/k64/p0.95 freq_penalty=0/0/0 start_t=0 start_init=noise time_path=0.0000->1.0000 generated 16/16
+[summary] {"type": "summary", "checkpoint": "runs/lta_owt_t5_adaln_adamw_wd0p1_rollin_p50_randk0_3_uniformt_temp1_synct_gbs512_2node8gpu_1m_t-20260518224737-tftgw/step_0450000.pt", "step": 450000, "decode": {"steps": 128, "model_t_mode": "post", "decode_time_schedule": "linear", "decode_s_min_frac": 0.0, "decode_s_max_frac": 0.25, "decode_force_final_t": true, "decode_time_grid": [0.0, 0.0078125, 0.015625, 0.0234375, 0.03125, 0.0390625, 0.046875, 0.0546875, 0.0625, 0.0703125, 0.078125, 0.0859375, 0.09375, 0.1015625, 0.109375, 0.1171875, 0.125, 0.1328125, 0.140625, 0.1484375, 0.15625, 0.1640625, 0.171875, 0.1796875, 0.1875, 0.1953125, 0.203125, 0.2109375, 0.21875, 0.2265625, 0.234375, 0.2421875, 0.25, 0.2578125, 0.265625, 0.2734375, 0.28125, 0.2890625, 0.296875, 0.3046875, 0.3125, 0.3203125, 0.328125, 0.3359375, 0.34375, 0.3515625, 0.359375, 0.3671875, 0.375, 0.3828125, 0.390625, 0.3984375, 0.40625, 0.4140625, 0.421875, 0.4296875, 0.4375, 0.4453125, 0.453125, 0.4609375, 0.46875, 0.4765625, 0.484375, 0.4921875, 0.5, 0.5078125, 0.515625, 0.5234375, 0.53125, 0.5390625, 0.546875, 0.5546875, 0.5625, 0.5703125, 0.578125, 0.5859375, 0.59375, 0.6015625, 0.609375, 0.6171875, 0.625, 0.6328125, 0.640625, 0.6484375, 0.65625, 0.6640625, 0.671875, 0.6796875, 0.6875, 0.6953125, 0.703125, 0.7109375, 0.71875, 0.7265625, 0.734375, 0.7421875, 0.75, 0.7578125, 0.765625, 0.7734375, 0.78125, 0.7890625, 0.796875, 0.8046875, 0.8125, 0.8203125, 0.828125, 0.8359375, 0.84375, 0.8515625, 0.859375, 0.8671875, 0.875, 0.8828125, 0.890625, 0.8984375, 0.90625, 0.9140625, 0.921875, 0.9296875, 0.9375, 0.9453125, 0.953125, 0.9609375, 0.96875, 0.9765625, 0.984375, 0.9921875, 1.0], "decode_rule": "dual_line_resample", "support_power": 1.0, "semantic_power": 1.0, "anchor_mode": "state", "cfg_scale": 0.0, "cfg_power": 1.0, "cfg_start": 0.0, "cfg_prior": "uniform", "decode_freq_penalty_alpha": 0.0, "decode_freq_penalty_beta": 0.0, "decode_freq_penalty_floor": 0.0, "decode_freq_penalty_start": 0.0, "decode_freq_penalty_end": 1.0, "decode_freq_penalty_power": 1.0, "start_t": 0.0, "start_init": "noise", "noise_init": "logistic_normal", "noise_sigma": -1.0, "dirichlet_concentration": 1.0, "concentration_min": 1.0, "concentration_max": 1024.0, "target_prob": 0.999, "endpoint_temp": 1.0, "final_from": "state", "final_sample_mode": "argmax", "final_sample_temp": 1.0, "final_top_k": 64, "final_top_p": 0.95, "final_freq_penalty_alpha": 0.0, "final_freq_penalty_beta": 0.0, "final_freq_penalty_floor": 0.0, "lock_bos": false, "n_samples": 16, "seed": 20260503}, "raw_genppl": {"ppl": 2.2326077476556088, "nll_per_token": 0.8031702958314813, "tokens": 8999, "kept_samples": 16, "total_samples": 16, "empty_rate": 0.0, "skipped_samples": 0}, "stripped_genppl": {"ppl": 2.2307195664959876, "nll_per_token": 0.8023242089822044, "tokens": 8998, "kept_samples": 16, "total_samples": 16, "empty_rate": 0.0, "skipped_samples": 0}, "diversity": {"sample_entropy": 1.0316432280294907, "unique_tokens": 41, "token_count": 16384, "distinct_1": 0.00250244140625, "distinct_2": 0.010202834799608993, "top_token_mass": 0.21685791015625}}
+[summary] {"type": "summary", "checkpoint": "runs/lta_owt_t5_adaln_adamw_wd0p1_rollin_p50_randk0_3_uniformt_temp1_synct_gbs512_2node8gpu_1m_t-20260518224737-tftgw/step_0450000.pt", "step": 450000, "decode": {"steps": 128, "model_t_mode": "post", "decode_time_schedule": "linear", "decode_s_min_frac": 0.0, "decode_s_max_frac": 0.25, "decode_force_final_t": true, "decode_time_grid": [0.0, 0.0078125, 0.015625, 0.0234375, 0.03125, 0.0390625, 0.046875, 0.0546875, 0.0625, 0.0703125, 0.078125, 0.0859375, 0.09375, 0.1015625, 0.109375, 0.1171875, 0.125, 0.1328125, 0.140625, 0.1484375, 0.15625, 0.1640625, 0.171875, 0.1796875, 0.1875, 0.1953125, 0.203125, 0.2109375, 0.21875, 0.2265625, 0.234375, 0.2421875, 0.25, 0.2578125, 0.265625, 0.2734375, 0.28125, 0.2890625, 0.296875, 0.3046875, 0.3125, 0.3203125, 0.328125, 0.3359375, 0.34375, 0.3515625, 0.359375, 0.3671875, 0.375, 0.3828125, 0.390625, 0.3984375, 0.40625, 0.4140625, 0.421875, 0.4296875, 0.4375, 0.4453125, 0.453125, 0.4609375, 0.46875, 0.4765625, 0.484375, 0.4921875, 0.5, 0.5078125, 0.515625, 0.5234375, 0.53125, 0.5390625, 0.546875, 0.5546875, 0.5625, 0.5703125, 0.578125, 0.5859375, 0.59375, 0.6015625, 0.609375, 0.6171875, 0.625, 0.6328125, 0.640625, 0.6484375, 0.65625, 0.6640625, 0.671875, 0.6796875, 0.6875, 0.6953125, 0.703125, 0.7109375, 0.71875, 0.7265625, 0.734375, 0.7421875, 0.75, 0.7578125, 0.765625, 0.7734375, 0.78125, 0.7890625, 0.796875, 0.8046875, 0.8125, 0.8203125, 0.828125, 0.8359375, 0.84375, 0.8515625, 0.859375, 0.8671875, 0.875, 0.8828125, 0.890625, 0.8984375, 0.90625, 0.9140625, 0.921875, 0.9296875, 0.9375, 0.9453125, 0.953125, 0.9609375, 0.96875, 0.9765625, 0.984375, 0.9921875, 1.0], "decode_rule": "dual_line_resample", "support_power": 1.0, "semantic_power": 1.0, "anchor_mode": "state", "cfg_scale": 0.0, "cfg_power": 1.0, "cfg_start": 0.0, "cfg_prior": "uniform", "decode_freq_penalty_alpha": 0.0, "decode_freq_penalty_beta": 0.0, "decode_freq_penalty_floor": 0.0, "decode_freq_penalty_start": 0.0, "decode_freq_penalty_end": 1.0, "decode_freq_penalty_power": 1.0, "start_t": 0.0, "start_init": "noise", "noise_init": "logistic_normal", "noise_sigma": -1.0, "dirichlet_concentration": 1.0, "concentration_min": 1.0, "concentration_max": 1024.0, "target_prob": 0.999, "endpoint_temp": 1.3, "final_from": "state", "final_sample_mode": "argmax", "final_sample_temp": 1.0, "final_top_k": 64, "final_top_p": 0.95, "final_freq_penalty_alpha": 0.0, "final_freq_penalty_beta": 0.0, "final_freq_penalty_floor": 0.0, "lock_bos": false, "n_samples": 16, "seed": 20260503}, "raw_genppl": {"ppl": 7.064495313258387, "nll_per_token": 1.955081578801945, "tokens": 13572, "kept_samples": 16, "total_samples": 16, "empty_rate": 0.0, "skipped_samples": 0}, "stripped_genppl": {"ppl": 17.572733752302447, "nll_per_token": 2.866348482144905, "tokens": 5451, "kept_samples": 16, "total_samples": 16, "empty_rate": 0.0, "skipped_samples": 0}, "diversity": {"sample_entropy": 1.2441357657584844, "unique_tokens": 22, "token_count": 16384, "distinct_1": 0.0013427734375, "distinct_2": 0.007270283479960899, "top_token_mass": 0.6495361328125}}
+[summary] {"type": "summary", "checkpoint": "runs/lta_owt_t5_adaln_adamw_wd0p1_rollin_p50_randk0_3_uniformt_temp1_synct_gbs512_2node8gpu_1m_t-20260518224737-tftgw/step_0450000.pt", "step": 450000, "decode": {"steps": 128, "model_t_mode": "post", "decode_time_schedule": "linear", "decode_s_min_frac": 0.0, "decode_s_max_frac": 0.25, "decode_force_final_t": true, "decode_time_grid": [0.0, 0.0078125, 0.015625, 0.0234375, 0.03125, 0.0390625, 0.046875, 0.0546875, 0.0625, 0.0703125, 0.078125, 0.0859375, 0.09375, 0.1015625, 0.109375, 0.1171875, 0.125, 0.1328125, 0.140625, 0.1484375, 0.15625, 0.1640625, 0.171875, 0.1796875, 0.1875, 0.1953125, 0.203125, 0.2109375, 0.21875, 0.2265625, 0.234375, 0.2421875, 0.25, 0.2578125, 0.265625, 0.2734375, 0.28125, 0.2890625, 0.296875, 0.3046875, 0.3125, 0.3203125, 0.328125, 0.3359375, 0.34375, 0.3515625, 0.359375, 0.3671875, 0.375, 0.3828125, 0.390625, 0.3984375, 0.40625, 0.4140625, 0.421875, 0.4296875, 0.4375, 0.4453125, 0.453125, 0.4609375, 0.46875, 0.4765625, 0.484375, 0.4921875, 0.5, 0.5078125, 0.515625, 0.5234375, 0.53125, 0.5390625, 0.546875, 0.5546875, 0.5625, 0.5703125, 0.578125, 0.5859375, 0.59375, 0.6015625, 0.609375, 0.6171875, 0.625, 0.6328125, 0.640625, 0.6484375, 0.65625, 0.6640625, 0.671875, 0.6796875, 0.6875, 0.6953125, 0.703125, 0.7109375, 0.71875, 0.7265625, 0.734375, 0.7421875, 0.75, 0.7578125, 0.765625, 0.7734375, 0.78125, 0.7890625, 0.796875, 0.8046875, 0.8125, 0.8203125, 0.828125, 0.8359375, 0.84375, 0.8515625, 0.859375, 0.8671875, 0.875, 0.8828125, 0.890625, 0.8984375, 0.90625, 0.9140625, 0.921875, 0.9296875, 0.9375, 0.9453125, 0.953125, 0.9609375, 0.96875, 0.9765625, 0.984375, 0.9921875, 1.0], "decode_rule": "dual_line_resample", "support_power": 1.0, "semantic_power": 1.0, "anchor_mode": "state", "cfg_scale": 0.0, "cfg_power": 1.0, "cfg_start": 0.0, "cfg_prior": "uniform", "decode_freq_penalty_alpha": 0.0, "decode_freq_penalty_beta": 0.0, "decode_freq_penalty_floor": 0.0, "decode_freq_penalty_start": 0.0, "decode_freq_penalty_end": 1.0, "decode_freq_penalty_power": 1.0, "start_t": 0.0, "start_init": "noise", "noise_init": "logistic_normal", "noise_sigma": -1.0, "dirichlet_concentration": 1.0, "concentration_min": 1.0, "concentration_max": 1024.0, "target_prob": 0.999, "endpoint_temp": 1.45, "final_from": "state", "final_sample_mode": "argmax", "final_sample_temp": 1.0, "final_top_k": 64, "final_top_p": 0.95, "final_freq_penalty_alpha": 0.0, "final_freq_penalty_beta": 0.0, "final_freq_penalty_floor": 0.0, "lock_bos": false, "n_samples": 16, "seed": 20260503}, "raw_genppl": {"ppl": 30.717215425545668, "nll_per_token": 3.4248232604713977, "tokens": 13513, "kept_samples": 16, "total_samples": 16, "empty_rate": 0.0, "skipped_samples": 0}, "stripped_genppl": {"ppl": 30.86467464857938, "nll_per_token": 3.429612314678342, "tokens": 11534, "kept_samples": 16, "total_samples": 16, "empty_rate": 0.0, "skipped_samples": 0}, "diversity": {"sample_entropy": 2.1715079167281877, "unique_tokens": 188, "token_count": 16384, "distinct_1": 0.011474609375, "distinct_2": 0.04233870967741935, "top_token_mass": 0.2110595703125}}
+[summary] {"type": "summary", "checkpoint": "runs/lta_owt_t5_adaln_adamw_wd0p1_rollin_p50_randk0_3_uniformt_temp1_synct_gbs512_2node8gpu_1m_t-20260518224737-tftgw/step_0450000.pt", "step": 450000, "decode": {"steps": 128, "model_t_mode": "post", "decode_time_schedule": "linear", "decode_s_min_frac": 0.0, "decode_s_max_frac": 0.25, "decode_force_final_t": true, "decode_time_grid": [0.0, 0.0078125, 0.015625, 0.0234375, 0.03125, 0.0390625, 0.046875, 0.0546875, 0.0625, 0.0703125, 0.078125, 0.0859375, 0.09375, 0.1015625, 0.109375, 0.1171875, 0.125, 0.1328125, 0.140625, 0.1484375, 0.15625, 0.1640625, 0.171875, 0.1796875, 0.1875, 0.1953125, 0.203125, 0.2109375, 0.21875, 0.2265625, 0.234375, 0.2421875, 0.25, 0.2578125, 0.265625, 0.2734375, 0.28125, 0.2890625, 0.296875, 0.3046875, 0.3125, 0.3203125, 0.328125, 0.3359375, 0.34375, 0.3515625, 0.359375, 0.3671875, 0.375, 0.3828125, 0.390625, 0.3984375, 0.40625, 0.4140625, 0.421875, 0.4296875, 0.4375, 0.4453125, 0.453125, 0.4609375, 0.46875, 0.4765625, 0.484375, 0.4921875, 0.5, 0.5078125, 0.515625, 0.5234375, 0.53125, 0.5390625, 0.546875, 0.5546875, 0.5625, 0.5703125, 0.578125, 0.5859375, 0.59375, 0.6015625, 0.609375, 0.6171875, 0.625, 0.6328125, 0.640625, 0.6484375, 0.65625, 0.6640625, 0.671875, 0.6796875, 0.6875, 0.6953125, 0.703125, 0.7109375, 0.71875, 0.7265625, 0.734375, 0.7421875, 0.75, 0.7578125, 0.765625, 0.7734375, 0.78125, 0.7890625, 0.796875, 0.8046875, 0.8125, 0.8203125, 0.828125, 0.8359375, 0.84375, 0.8515625, 0.859375, 0.8671875, 0.875, 0.8828125, 0.890625, 0.8984375, 0.90625, 0.9140625, 0.921875, 0.9296875, 0.9375, 0.9453125, 0.953125, 0.9609375, 0.96875, 0.9765625, 0.984375, 0.9921875, 1.0], "decode_rule": "dual_line_resample", "support_power": 1.0, "semantic_power": 1.0, "anchor_mode": "state", "cfg_scale": 0.0, "cfg_power": 1.0, "cfg_start": 0.0, "cfg_prior": "uniform", "decode_freq_penalty_alpha": 0.0, "decode_freq_penalty_beta": 0.0, "decode_freq_penalty_floor": 0.0, "decode_freq_penalty_start": 0.0, "decode_freq_penalty_end": 1.0, "decode_freq_penalty_power": 1.0, "start_t": 0.0, "start_init": "noise", "noise_init": "logistic_normal", "noise_sigma": -1.0, "dirichlet_concentration": 1.0, "concentration_min": 1.0, "concentration_max": 1024.0, "target_prob": 0.999, "endpoint_temp": 1.6, "final_from": "state", "final_sample_mode": "argmax", "final_sample_temp": 1.0, "final_top_k": 64, "final_top_p": 0.95, "final_freq_penalty_alpha": 0.0, "final_freq_penalty_beta": 0.0, "final_freq_penalty_floor": 0.0, "lock_bos": false, "n_samples": 16, "seed": 20260503}, "raw_genppl": {"ppl": 197.5653078639758, "nll_per_token": 5.2860692024099905, "tokens": 14554, "kept_samples": 16, "total_samples": 16, "empty_rate": 0.0, "skipped_samples": 0}, "stripped_genppl": {"ppl": 232.80731535973388, "nll_per_token": 5.4502111387301335, "tokens": 12773, "kept_samples": 16, "total_samples": 16, "empty_rate": 0.0, "skipped_samples": 0}, "diversity": {"sample_entropy": 3.243602888805284, "unique_tokens": 2007, "token_count": 16384, "distinct_1": 0.12249755859375, "distinct_2": 0.31030058651026393, "top_token_mass": 0.17779541015625}}
+[done] docs/lta_samples/metrics_20260522/owt_t5_rollin_p50_450k_dualline_state_steps128_n16/summary.jsonl

LTA_openwebtext_dualt/logs/lta_lm1b_classic_dirichlet_len512_gbs512_4gpu_10k_save1k_20260523.watch.pid

@@ -0,0 +1 @@
+993820

LTA_openwebtext_dualt/logs/lta_owt_c1024_len1024_gaussianflm_gbs128_4gpu_2k_resume.log

@@ -0,0 +1,64 @@
+
+*****************************************
+Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
+*****************************************
+NCCL version 2.25.1+cuda12.8
+resumed_from=runs/lta_owt_c1024_len1024_gaussianflm_gbs128_4gpu_2k/step_0000500.pt start_step=501
+{
+  "device": "cuda:0",
+  "rank": 0,
+  "world_size": 4,
+  "samples": "wrapped_stream_online_shuffle:1000",
+  "vocab_size": 50257,
+  "save_dir": "runs/lta_owt_c1024_len1024_gaussianflm_gbs128_4gpu_2k",
+  "batch_size": 16,
+  "grad_accum": 2,
+  "effective_batch_size": 128,
+  "global_batch_size": 128,
+  "lr_schedule": "constant_warmup",
+  "warmup_steps": 100,
+  "adam_beta1": 0.9,
+  "adam_beta2": 0.999,
+  "adam_eps": 1e-08,
+  "model_type": "ddit",
+  "dual_t": true,
+  "corrupt_t_mode": "same",
+  "corrupt_min_t": 0.0,
+  "corrupt_max_t": 1.0,
+  "dirichlet_endpoint_mode": "categorical_dual_t",
+  "dirichlet_semantic_t_mode": "same",
+  "dirichlet_semantic_t_value": 0.0,
+  "categorical_wrong_from_full_vocab": true,
+  "simplex_bridge_sampler": "dirichlet",
+  "logistic_normal_sigma_min": 0.18,
+  "logistic_normal_sigma_max": 2.2,
+  "logistic_normal_tau_min": 0.65,
+  "logistic_normal_tau_max": 1.15,
+  "torch_compile": false,
+  "compile_mode": "max-autotune",
+  "state_format": "prob",
+  "target_loss": "hard_ce",
+  "meanflow_weight": 0.0,
+  "bridge_noise_init": "logistic_normal",
+  "noise_sigma": -1.0,
+  "wrap": true,
+  "wrap_mode": "stream",
+  "wrap_record_buffer_size": 200,
+  "owt_cached_chunks": false,
+  "owt_chunk_cache_dir": "",
+  "owt_chunk_cache_rebuild": false,
+  "owt_chunk_cache_write_batch": 4096,
+  "online_chunk_shuffle": true,
+  "online_chunk_shuffle_buffer": 1000,
+  "openwebtext_split": "train_minus_100k",
+  "detokenizer": "auto",
+  "resolved_detokenizer": null,
+  "num_workers": 0,
+  "latest_every": 500,
+  "resume_path": "runs/lta_owt_c1024_len1024_gaussianflm_gbs128_4gpu_2k/step_0000500.pt"
+}
+step=600 micro_steps=1200 elapsed=81.0s lr=3.000000e-04 loss_all=7.5269 acc_all=0.0389 loss_corrupt=7.5269 acc_corrupt=0.0389 corrupt_frac=1.0000 loss=7.5269 loss_recon=7.5269 loss_meanflow=0.0000 mean_model_t=0.4969 mean_corrupt_t=0.4969 mean_loss_t_weight=1.0000 prior_center_loss_beta=0.0000 wrong_frac=0.0000 init_acc_corrupt=0.2011 init_gold_top10=0.2329 init_gold_top100=0.2787
+step=700 micro_steps=1400 elapsed=77.3s lr=3.000000e-04 loss_all=7.4719 acc_all=0.0440 loss_corrupt=7.4719 acc_corrupt=0.0440 corrupt_frac=1.0000 loss=7.4719 loss_recon=7.4719 loss_meanflow=0.0000 mean_model_t=0.4972 mean_corrupt_t=0.4972 mean_loss_t_weight=1.0000 prior_center_loss_beta=0.0000 wrong_frac=0.0000 init_acc_corrupt=0.1970 init_gold_top10=0.2301 init_gold_top100=0.2770
+step=800 micro_steps=1600 elapsed=77.9s lr=3.000000e-04 loss_all=7.4278 acc_all=0.0551 loss_corrupt=7.4278 acc_corrupt=0.0551 corrupt_frac=1.0000 loss=7.4278 loss_recon=7.4278 loss_meanflow=0.0000 mean_model_t=0.4958 mean_corrupt_t=0.4958 mean_loss_t_weight=1.0000 prior_center_loss_beta=0.0000 wrong_frac=0.0000 init_acc_corrupt=0.1973 init_gold_top10=0.2295 init_gold_top100=0.2756
+step=900 micro_steps=1800 elapsed=77.6s lr=3.000000e-04 loss_all=7.3457 acc_all=0.0738 loss_corrupt=7.3457 acc_corrupt=0.0738 corrupt_frac=1.0000 loss=7.3457 loss_recon=7.3457 loss_meanflow=0.0000 mean_model_t=0.5016 mean_corrupt_t=0.5016 mean_loss_t_weight=1.0000 prior_center_loss_beta=0.0000 wrong_frac=0.0000 init_acc_corrupt=0.2068 init_gold_top10=0.2383 init_gold_top100=0.2841
+step=1000 micro_steps=2000 elapsed=77.5s lr=3.000000e-04 loss_all=7.3143 acc_all=0.0879 loss_corrupt=7.3143 acc_corrupt=0.0879 corrupt_frac=1.0000 loss=7.3143 loss_recon=7.3143 loss_meanflow=0.0000 mean_model_t=0.5041 mean_corrupt_t=0.5041 mean_loss_t_weight=1.0000 prior_center_loss_beta=0.0000 wrong_frac=0.0000 init_acc_corrupt=0.1991 init_gold_top10=0.2344 init_gold_top100=0.2851

LTA_openwebtext_dualt/logs/lta_owt_gpt2cached_len1024_rollout1_p1_smoke4gpu_nofind_20260513_144753.log

@@ -0,0 +1,225 @@
+NCCL version 2.25.1+cuda12.8
+{
+  "device": "cuda:0",
+  "rank": 0,
+  "world_size": 4,
+  "samples": "owt_cached_chunks:8734897",
+  "vocab_size": 50257,
+  "tokenizer_vocab_size": 50257,
+  "save_dir": "runs/lta_owt_gpt2cached_len1024_rollout1_p1_smoke4gpu_nofind_20260513_144753",
+  "batch_size": 2,
+  "grad_accum": 1,
+  "effective_batch_size": 8,
+  "global_batch_size": 8,
+  "lr_schedule": "cosine",
+  "optimizer": "adamw",
+  "warmup_steps": 1,
+  "min_lr": 6e-05,
+  "weight_decay": 0.1,
+  "adamw_param_groups": "nanogpt",
+  "adam_beta1": 0.9,
+  "adam_beta2": 0.95,
+  "adam_eps": 1e-08,
+  "muon_momentum": 0.95,
+  "muon_ns_steps": 5,
+  "muon_update_scale": 1.0,
+  "ema_decay": 0.0,
+  "ema_start_step": 0,
+  "model_type": "ddit",
+  "dual_t": true,
+  "corrupt_t_mode": "same",
+  "corrupt_min_t": 0.0,
+  "corrupt_max_t": 1.0,
+  "prefix_block_prob": 0.0,
+  "prefix_block_len": 128,
+  "dirichlet_endpoint_mode": "categorical_dual_t",
+  "dirichlet_semantic_t_mode": "same",
+  "dirichlet_semantic_t_value": 0.0,
+  "categorical_wrong_from_full_vocab": true,
+  "categorical_wrong_from_batch_valid_tokens": false,
+  "mask_mixture_original_prob": 0.0,
+  "mask_mixture_lowk_prob": 0.0,
+  "mask_mixture_lowcorrupt_prob": 0.0,
+  "mask_mixture_block_prob": 0.0,
+  "mask_mixture_all_prob": 0.0,
+  "mask_mixture_lowk_clean_tokens": "1,2,4,8,16,32,64",
+  "mask_mixture_lowcorrupt_tokens": "1,2,4,8,16,32,64",
+  "mask_mixture_block_tokens": "64,128",
+  "simplex_bridge_sampler": "dirichlet",
+  "logistic_normal_sigma_min": 0.18,
+  "logistic_normal_sigma_max": 2.2,
+  "logistic_normal_tau_min": 0.65,
+  "logistic_normal_tau_max": 1.15,
+  "torch_compile": false,
+  "compile_mode": "max-autotune",
+  "state_format": "prob",
+  "target_loss": "hard_ce",
+  "meanflow_weight": 0.0,
+  "rollout_train_prob": 1.0,
+  "rollout_train_steps": 1,
+  "rollout_train_infer_steps": 64,
+  "rollout_train_temp": 1.45,
+  "rollout_train_max_gamma": 1.0,
+  "rollout_train_corrupt_only": true,
+  "bridge_noise_init": "logistic_normal",
+  "noise_sigma": -1.0,
+  "allow_tf32": true,
+  "activation_checkpointing": false,
+  "activation_checkpoint_interval": 1,
+  "ddp_static_graph": false,
+  "ddp_gradient_as_bucket_view": true,
+  "blocking_data_transfer": false,
+  "dataloader_prefetch_factor": 2,
+  "full_train_stats": false,
+  "record_pad_truncate": false,
+  "record_add_eos": false,
+  "record_add_special_tokens": false,
+  "record_pad_token": "pad",
+  "record_shuffle_buffer": 10000,
+  "wrap": true,
+  "wrap_mode": "stream",
+  "wrap_record_buffer_size": 200,
+  "owt_cached_chunks": true,
+  "owt_chunk_cache_dir": "/e2e-data/evad-tech-vla/wanghan58/data/small_benchmarks/langflow_2604_11748/openwebtext_lta_cached_chunks/gpt2_len1024_train_minus_100k",
+  "owt_chunk_cache_rebuild": false,
+  "owt_chunk_cache_write_batch": 4096,
+  "owt_exact_repeat_per_chunk": 0,
+  "online_chunk_shuffle": false,
+  "online_chunk_shuffle_buffer": 10000,
+  "openwebtext_split": "train_minus_100k",
+  "detokenizer": "auto",
+  "resolved_detokenizer": null,
+  "num_workers": 0,
+  "latest_every": 100000,
+  "resume_path": ""
+}
+[rank1]: Traceback (most recent call last):
+[rank1]:   File "/e2e-data/evad-tech-vla/wanghan58/workspace/LTA_openwebtext_dualt/train.py", line 1488, in <module>
+[rank1]:     main()
+[rank1]:   File "/e2e-data/evad-tech-vla/wanghan58/workspace/LTA_openwebtext_dualt/train.py", line 1373, in main
+[rank1]:     logits = trainable_model(loss_state, model_t, bridge.attn_mask)
+[rank1]:              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank1]:   File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py", line 1739, in _wrapped_call_impl
+[rank1]:     return self._call_impl(*args, **kwargs)
+[rank1]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank1]:   File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py", line 1750, in _call_impl
+[rank1]:     return forward_call(*args, **kwargs)
+[rank1]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank1]:   File "/usr/local/lib/python3.12/dist-packages/torch/nn/parallel/distributed.py", line 1650, in forward
+[rank1]:     inputs, kwargs = self._pre_forward(*inputs, **kwargs)
+[rank1]:                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank1]:   File "/usr/local/lib/python3.12/dist-packages/torch/nn/parallel/distributed.py", line 1539, in _pre_forward
+[rank1]:     if torch.is_grad_enabled() and self.reducer._rebuild_buckets():
+[rank1]:                                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank1]: RuntimeError: Expected to have finished reduction in the prior iteration before starting a new one. This error indicates that your module has parameters that were not used in producing loss. You can enable unused parameter detection by passing the keyword argument `find_unused_parameters=True` to `torch.nn.parallel.DistributedDataParallel`, and by 
+[rank1]: making sure all `forward` function outputs participate in calculating loss. 
+[rank1]: If you already have done the above, then the distributed data parallel module wasn't able to locate the output tensors in the return value of your module's `forward` function. Please include the loss function and the structure of the return value of `forward` of your module when reporting this issue (e.g. list, dict, iterable).
+[rank1]: Parameter indices which did not receive grad for rank 1: 0 1 2 3 4 6 7 9 10 11 12 13 14 16 17 19 20 21 22 23 24 26 27 29 30 31 32 33 34 36 37 39 40 41 42 43 44 46 47 49 50 51 52 53 54 56 57 59 60 61 62 63 64 66 67 69 70 71 72 73 74 76 77 79 80 81 82 83 84 86 87 89 90 91 92 93 94 96 97 99 100 101 102 103 104 106 107 109 110 111 112 113 114 116 117 119 120 121 122 123 ...
+[rank1]:  In addition, you can set the environment variable TORCH_DISTRIBUTED_DEBUG to either INFO or DETAIL to print out information about which particular parameters did not receive gradient on this rank as part of this error
+step=1 micro_steps=1 elapsed=1.1s lr=6.000000e-04 acc_all=0.0005 acc_corrupt=0.0011 corrupt_frac=0.4424 loss_all=10.8125 loss_corrupt=10.8125 acc_corrupt_t_0p0_0p2=0.0015 corrupt_frac_t_0p0_0p2=0.7163 acc_corrupt_t_0p8_1p0=0.0000 corrupt_frac_t_0p8_1p0=0.2837 loss=10.8125 loss_recon=10.8125 loss_meanflow=0.0000 mean_model_t=0.5059 mean_corrupt_t=0.5059 mean_loss_t_weight=1.0000 prior_center_loss_beta=0.0000 rollout_train_applied=1.0000 wrong_frac=0.6821 init_acc_corrupt=0.2815 init_gold_top10=0.3013 init_gold_top100=0.3918
+[rank0]: Traceback (most recent call last):
+[rank0]:   File "/e2e-data/evad-tech-vla/wanghan58/workspace/LTA_openwebtext_dualt/train.py", line 1488, in <module>
+[rank0]:     main()
+[rank0]:   File "/e2e-data/evad-tech-vla/wanghan58/workspace/LTA_openwebtext_dualt/train.py", line 1373, in main
+[rank0]:     logits = trainable_model(loss_state, model_t, bridge.attn_mask)
+[rank0]:              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank0]:   File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py", line 1739, in _wrapped_call_impl
+[rank0]:     return self._call_impl(*args, **kwargs)
+[rank0]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank0]:   File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py", line 1750, in _call_impl
+[rank0]:     return forward_call(*args, **kwargs)
+[rank0]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank0]:   File "/usr/local/lib/python3.12/dist-packages/torch/nn/parallel/distributed.py", line 1650, in forward
+[rank0]:     inputs, kwargs = self._pre_forward(*inputs, **kwargs)
+[rank0]:                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank0]:   File "/usr/local/lib/python3.12/dist-packages/torch/nn/parallel/distributed.py", line 1539, in _pre_forward
+[rank0]:     if torch.is_grad_enabled() and self.reducer._rebuild_buckets():
+[rank0]:                                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank0]: RuntimeError: Expected to have finished reduction in the prior iteration before starting a new one. This error indicates that your module has parameters that were not used in producing loss. You can enable unused parameter detection by passing the keyword argument `find_unused_parameters=True` to `torch.nn.parallel.DistributedDataParallel`, and by 
+[rank0]: making sure all `forward` function outputs participate in calculating loss. 
+[rank0]: If you already have done the above, then the distributed data parallel module wasn't able to locate the output tensors in the return value of your module's `forward` function. Please include the loss function and the structure of the return value of `forward` of your module when reporting this issue (e.g. list, dict, iterable).
+[rank0]: Parameter indices which did not receive grad for rank 0: 0 1 2 3 4 6 7 9 10 11 12 13 14 16 17 19 20 21 22 23 24 26 27 29 30 31 32 33 34 36 37 39 40 41 42 43 44 46 47 49 50 51 52 53 54 56 57 59 60 61 62 63 64 66 67 69 70 71 72 73 74 76 77 79 80 81 82 83 84 86 87 89 90 91 92 93 94 96 97 99 100 101 102 103 104 106 107 109 110 111 112 113 114 116 117 119 120 121 122 123 ...
+[rank0]:  In addition, you can set the environment variable TORCH_DISTRIBUTED_DEBUG to either INFO or DETAIL to print out information about which particular parameters did not receive gradient on this rank as part of this error
+[rank3]: Traceback (most recent call last):
+[rank3]:   File "/e2e-data/evad-tech-vla/wanghan58/workspace/LTA_openwebtext_dualt/train.py", line 1488, in <module>
+[rank3]:     main()
+[rank3]:   File "/e2e-data/evad-tech-vla/wanghan58/workspace/LTA_openwebtext_dualt/train.py", line 1373, in main
+[rank3]:     logits = trainable_model(loss_state, model_t, bridge.attn_mask)
+[rank3]:              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank3]:   File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py", line 1739, in _wrapped_call_impl
+[rank3]:     return self._call_impl(*args, **kwargs)
+[rank3]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank3]:   File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py", line 1750, in _call_impl
+[rank3]:     return forward_call(*args, **kwargs)
+[rank3]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank3]:   File "/usr/local/lib/python3.12/dist-packages/torch/nn/parallel/distributed.py", line 1650, in forward
+[rank3]:     inputs, kwargs = self._pre_forward(*inputs, **kwargs)
+[rank3]:                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank3]:   File "/usr/local/lib/python3.12/dist-packages/torch/nn/parallel/distributed.py", line 1539, in _pre_forward
+[rank3]:     if torch.is_grad_enabled() and self.reducer._rebuild_buckets():
+[rank3]:                                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank3]: RuntimeError: Expected to have finished reduction in the prior iteration before starting a new one. This error indicates that your module has parameters that were not used in producing loss. You can enable unused parameter detection by passing the keyword argument `find_unused_parameters=True` to `torch.nn.parallel.DistributedDataParallel`, and by 
+[rank3]: making sure all `forward` function outputs participate in calculating loss. 
+[rank3]: If you already have done the above, then the distributed data parallel module wasn't able to locate the output tensors in the return value of your module's `forward` function. Please include the loss function and the structure of the return value of `forward` of your module when reporting this issue (e.g. list, dict, iterable).
+[rank3]: Parameter indices which did not receive grad for rank 3: 0 1 2 3 4 6 7 9 10 11 12 13 14 16 17 19 20 21 22 23 24 26 27 29 30 31 32 33 34 36 37 39 40 41 42 43 44 46 47 49 50 51 52 53 54 56 57 59 60 61 62 63 64 66 67 69 70 71 72 73 74 76 77 79 80 81 82 83 84 86 87 89 90 91 92 93 94 96 97 99 100 101 102 103 104 106 107 109 110 111 112 113 114 116 117 119 120 121 122 123 ...
+[rank3]:  In addition, you can set the environment variable TORCH_DISTRIBUTED_DEBUG to either INFO or DETAIL to print out information about which particular parameters did not receive gradient on this rank as part of this error
+[rank2]: Traceback (most recent call last):
+[rank2]:   File "/e2e-data/evad-tech-vla/wanghan58/workspace/LTA_openwebtext_dualt/train.py", line 1488, in <module>
+[rank2]:     main()
+[rank2]:   File "/e2e-data/evad-tech-vla/wanghan58/workspace/LTA_openwebtext_dualt/train.py", line 1373, in main
+[rank2]:     logits = trainable_model(loss_state, model_t, bridge.attn_mask)
+[rank2]:              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank2]:   File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py", line 1739, in _wrapped_call_impl
+[rank2]:     return self._call_impl(*args, **kwargs)
+[rank2]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank2]:   File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py", line 1750, in _call_impl
+[rank2]:     return forward_call(*args, **kwargs)
+[rank2]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank2]:   File "/usr/local/lib/python3.12/dist-packages/torch/nn/parallel/distributed.py", line 1650, in forward
+[rank2]:     inputs, kwargs = self._pre_forward(*inputs, **kwargs)
+[rank2]:                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank2]:   File "/usr/local/lib/python3.12/dist-packages/torch/nn/parallel/distributed.py", line 1539, in _pre_forward
+[rank2]:     if torch.is_grad_enabled() and self.reducer._rebuild_buckets():
+[rank2]:                                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank2]: RuntimeError: Expected to have finished reduction in the prior iteration before starting a new one. This error indicates that your module has parameters that were not used in producing loss. You can enable unused parameter detection by passing the keyword argument `find_unused_parameters=True` to `torch.nn.parallel.DistributedDataParallel`, and by 
+[rank2]: making sure all `forward` function outputs participate in calculating loss. 
+[rank2]: If you already have done the above, then the distributed data parallel module wasn't able to locate the output tensors in the return value of your module's `forward` function. Please include the loss function and the structure of the return value of `forward` of your module when reporting this issue (e.g. list, dict, iterable).
+[rank2]: Parameter indices which did not receive grad for rank 2: 0 1 2 3 4 6 7 9 10 11 12 13 14 16 17 19 20 21 22 23 24 26 27 29 30 31 32 33 34 36 37 39 40 41 42 43 44 46 47 49 50 51 52 53 54 56 57 59 60 61 62 63 64 66 67 69 70 71 72 73 74 76 77 79 80 81 82 83 84 86 87 89 90 91 92 93 94 96 97 99 100 101 102 103 104 106 107 109 110 111 112 113 114 116 117 119 120 121 122 123 ...
+[rank2]:  In addition, you can set the environment variable TORCH_DISTRIBUTED_DEBUG to either INFO or DETAIL to print out information about which particular parameters did not receive gradient on this rank as part of this error
+[rank0]:[W513 14:48:01.833475754 ProcessGroupNCCL.cpp:1487] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
+W0513 14:48:01.550000 624985 torch/distributed/elastic/multiprocessing/api.py:898] Sending process 624989 closing signal SIGTERM
+W0513 14:48:01.550000 624985 torch/distributed/elastic/multiprocessing/api.py:898] Sending process 624991 closing signal SIGTERM
+E0513 14:48:01.779000 624985 torch/distributed/elastic/multiprocessing/api.py:870] failed (exitcode: 1) local_rank: 1 (pid: 624990) of binary: /usr/bin/python
+Traceback (most recent call last):
+  File "<frozen runpy>", line 198, in _run_module_as_main
+  File "<frozen runpy>", line 88, in _run_code
+  File "/usr/local/lib/python3.12/dist-packages/torch/distributed/run.py", line 922, in <module>
+    main()
+  File "/usr/local/lib/python3.12/dist-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 355, in wrapper
+    return f(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^
+  File "/usr/local/lib/python3.12/dist-packages/torch/distributed/run.py", line 918, in main
+    run(args)
+  File "/usr/local/lib/python3.12/dist-packages/torch/distributed/run.py", line 909, in run
+    elastic_launch(
+  File "/usr/local/lib/python3.12/dist-packages/torch/distributed/launcher/api.py", line 139, in __call__
+    return launch_agent(self._config, self._entrypoint, list(args))
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/usr/local/lib/python3.12/dist-packages/torch/distributed/launcher/api.py", line 270, in launch_agent
+    raise ChildFailedError(
+torch.distributed.elastic.multiprocessing.errors.ChildFailedError: 
+============================================================
+train.py FAILED
+------------------------------------------------------------
+Failures:
+  <NO_OTHER_FAILURES>
+------------------------------------------------------------
+Root Cause (first observed failure):
+[0]:
+  time      : 2026-05-13_14:48:01
+  host      : localhost
+  rank      : 1 (local_rank: 1)
+  exitcode  : 1 (pid: 624990)
+  error_file: <N/A>
+  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
+============================================================

LTA_openwebtext_dualt/logs/lta_owt_t5elf_absrope_time4_dirichlet_len1025_C1_to_1024_prebos_mask1_sameT_gbs512_b32_8gpu_1m_save10k_20260526_watcher.pid

@@ -0,0 +1 @@
+10250

LTA_openwebtext_dualt/logs/lta_owt_t5elf_dirichlet_len1024_Cv_to_2v_mask1_gbs512_b32_4gpu_20k_save1k_20260525.log

@@ -0,0 +1,197 @@
+
+*****************************************
+Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
+*****************************************
+NCCL version 2.25.1+cuda12.8
+{
+  "device": "cuda:0",
+  "rank": 0,
+  "world_size": 4,
+  "samples": "tokenized_hf:9737184:pad=0",
+  "vocab_size": 32100,
+  "tokenizer_vocab_size": 32100,
+  "save_dir": "runs/lta_owt_t5elf_dirichlet_len1024_Cv_to_2v_mask1_gbs512_b32_4gpu_20k_save1k_20260525",
+  "max_len": 1024,
+  "effective_model_max_len": 1024,
+  "batch_size": 32,
+  "grad_accum": 4,
+  "effective_batch_size": 512,
+  "global_batch_size": 512,
+  "lr_schedule": "constant_warmup",
+  "optimizer": "adamw",
+  "epochs": 0.0,
+  "steps_per_epoch": 19018,
+  "total_steps": 20000,
+  "warmup_steps": 2500,
+  "warmup_epochs": -1.0,
+  "min_lr": 6e-05,
+  "weight_decay": 0.0,
+  "output_weight_decay": -1.0,
+  "adamw_param_groups": "nanogpt",
+  "adam_beta1": 0.9,
+  "adam_beta2": 0.999,
+  "adam_eps": 1e-08,
+  "muon_impl": "legacy",
+  "muon_momentum": 0.95,
+  "muon_ns_steps": 5,
+  "muon_update_scale": 1.0,
+  "muon_nesterov": false,
+  "muon_width_scale": false,
+  "muon_grouping": "",
+  "muon_param_count": 0,
+  "muon_adam_param_count": 0,
+  "muon_param_names": [],
+  "muon_adam_param_names": [],
+  "muon_effective_nesterov": false,
+  "muon_effective_width_scale": false,
+  "muon_effective_weight_decay": 0.0,
+  "muon_adam_fallback_nesterov": false,
+  "muon_adam_fallback_weight_decay": 0.0,
+  "ema_decay": 0.0,
+  "ema_start_step": 0,
+  "model_type": "ddit",
+  "ddit_mlp_type": "gelu",
+  "block_anchor_every": 0,
+  "block_anchor_init_std": 0.02,
+  "bos_anchor_every": 0,
+  "bos_anchor_token_id": -1,
+  "bos_anchor_extra_len": 0,
+  "abs_pos_embed": false,
+  "abs_pos_init_std": 0.02,
+  "elf_num_time_tokens": 4,
+  "elf_num_model_mode_tokens": 0,
+  "qk_norm": true,
+  "output_bias": false,
+  "output_init_std": -1.0,
+  "norm_type": "rmsnorm",
+  "target_loss": "hard_ce",
+  "linear_soft_target_power": 1.0,
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+step=1500 epoch=1/2 epoch_step=1500/19018 micro_steps=6000 elapsed=199.6s lr=1.801200e-04 loss=3.3915 loss_recon=3.3915 loss_meanflow=0.0000 mean_model_t=0.5006 mean_corrupt_t=0.4996 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.5313 corrupt_frac=1.0000 acc_corrupt=0.5313 loss_corrupt=3.3915 wrong_frac=0.5006 init_acc_corrupt=0.4994 acc_corrupt_t_0p0_0p2=0.1336 corrupt_frac_t_0p0_0p2=0.1960 acc_corrupt_t_0p2_0p4=0.3407 corrupt_frac_t_0p2_0p4=0.2036 acc_corrupt_t_0p4_0p6=0.5395 corrupt_frac_t_0p4_0p6=0.2020 acc_corrupt_t_0p6_0p8=0.7314 corrupt_frac_t_0p6_0p8=0.2017 acc_corrupt_t_0p8_1p0=0.9111 corrupt_frac_t_0p8_1p0=0.1967 out_w_norm=34.6646 out_g_norm=0.2818 loss_all=3.3737 init_gold_top10=0.5000 init_gold_top100=0.5015
+step=1600 epoch=1/2 epoch_step=1600/19018 micro_steps=6400 elapsed=199.5s lr=1.921200e-04 loss=3.3626 loss_recon=3.3626 loss_meanflow=0.0000 mean_model_t=0.4998 mean_corrupt_t=0.5003 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.5335 corrupt_frac=1.0000 acc_corrupt=0.5335 loss_corrupt=3.3626 wrong_frac=0.4994 init_acc_corrupt=0.5006 acc_corrupt_t_0p0_0p2=0.1365 corrupt_frac_t_0p0_0p2=0.2023 acc_corrupt_t_0p2_0p4=0.3397 corrupt_frac_t_0p2_0p4=0.1965 acc_corrupt_t_0p4_0p6=0.5408 corrupt_frac_t_0p4_0p6=0.1999 acc_corrupt_t_0p6_0p8=0.7328 corrupt_frac_t_0p6_0p8=0.1957 acc_corrupt_t_0p8_1p0=0.9115 corrupt_frac_t_0p8_1p0=0.2065 out_w_norm=36.5363 out_g_norm=0.2637 loss_all=3.5901 init_gold_top10=0.4688 init_gold_top100=0.4702
+step=1700 epoch=1/2 epoch_step=1700/19018 micro_steps=6800 elapsed=199.6s lr=2.041200e-04 loss=3.3443 loss_recon=3.3443 loss_meanflow=0.0000 mean_model_t=0.4981 mean_corrupt_t=0.4997 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.5344 corrupt_frac=1.0000 acc_corrupt=0.5344 loss_corrupt=3.3443 wrong_frac=0.4994 init_acc_corrupt=0.5006 acc_corrupt_t_0p0_0p2=0.1348 corrupt_frac_t_0p0_0p2=0.2017 acc_corrupt_t_0p2_0p4=0.3426 corrupt_frac_t_0p2_0p4=0.1997 acc_corrupt_t_0p4_0p6=0.5449 corrupt_frac_t_0p4_0p6=0.1972 acc_corrupt_t_0p6_0p8=0.7340 corrupt_frac_t_0p6_0p8=0.1989 acc_corrupt_t_0p8_1p0=0.9131 corrupt_frac_t_0p8_1p0=0.2034 out_w_norm=38.5982 out_g_norm=0.2560 loss_all=3.2181 init_gold_top10=0.5173 init_gold_top100=0.5193

LTA_openwebtext_dualt/logs/owt_fully_categorical_gate_probe_step116k_n8.log

@@ -0,0 +1,44 @@
+[forbid_endpoint_ids] n=352 first=[94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125]
+[decode] steps32_c128_mtpre_t0p9_tpow1p0_noise0_state_categorical_argmax
+[summary] {"name": "steps32_c128_mtpre_t0p9_tpow1p0_noise0_state_categorical_argmax", "step": 118000, "n_samples": 8, "steps": 32, "concentration_max": 128.0, "temp_start": 0.9, "temp_end": 0.9, "temp_schedule": "const", "t_power": 1.0, "eta0": 0.0, "eta_schedule": "none", "noise_conc": 1.0, "final_from": "state", "final_decode": "argmax", "final_temp": 1.0, "final_top_k": 16, "final_uncertain_threshold": 0.85, "update_rule": "categorical_argmax", "model_t_mode": "pre", "lock_bos": true, "lock_final_eos": false, "detok_genppl": NaN, "sample_entropy": 1.7124667587101636, "distinct_1": 0.2037353515625, "distinct_2": 0.36779081133919844, "top_token_mass": 0.3330078125, "tokens_scored": 0, "readability_score": 2.0797317483616076, "mean_chars": 2024.625, "replacement_chars": 0.0}
+[decode] steps32_c128_mtpost_t0p9_tpow1p0_noise0_state_categorical_argmax
+[summary] {"name": "steps32_c128_mtpost_t0p9_tpow1p0_noise0_state_categorical_argmax", "step": 118000, "n_samples": 8, "steps": 32, "concentration_max": 128.0, "temp_start": 0.9, "temp_end": 0.9, "temp_schedule": "const", "t_power": 1.0, "eta0": 0.0, "eta_schedule": "none", "noise_conc": 1.0, "final_from": "state", "final_decode": "argmax", "final_temp": 1.0, "final_top_k": 16, "final_uncertain_threshold": 0.85, "update_rule": "categorical_argmax", "model_t_mode": "post", "lock_bos": true, "lock_final_eos": false, "detok_genppl": NaN, "sample_entropy": 2.072406150304612, "distinct_1": 0.2462158203125, "distinct_2": 0.4319403714565005, "top_token_mass": 0.363525390625, "tokens_scored": 0, "readability_score": 3.8781453333859375, "mean_chars": 2446.625, "replacement_chars": 0.0}
+[decode] steps32_c128_mtpre_t0p9_tpow1p0_noise0_state_catstep_top16_categorical_sample
+[summary] {"name": "steps32_c128_mtpre_t0p9_tpow1p0_noise0_state_catstep_top16_categorical_sample", "step": 118000, "n_samples": 8, "steps": 32, "concentration_max": 128.0, "temp_start": 0.9, "temp_end": 0.9, "temp_schedule": "const", "t_power": 1.0, "eta0": 0.0, "eta_schedule": "none", "noise_conc": 1.0, "final_from": "state", "final_decode": "argmax", "final_temp": 1.0, "final_top_k": 16, "final_uncertain_threshold": 0.85, "update_rule": "categorical_sample", "model_t_mode": "pre", "lock_bos": true, "lock_final_eos": false, "detok_genppl": NaN, "sample_entropy": 1.4362033482302652, "distinct_1": 0.0692138671875, "distinct_2": 0.14442815249266863, "top_token_mass": 0.4456787109375, "tokens_scored": 0, "readability_score": 1.912107983179559, "mean_chars": 1382.875, "replacement_chars": 0.0}
+[decode] steps32_c128_mtpost_t0p9_tpow1p0_noise0_state_catstep_top16_categorical_sample
+[summary] {"name": "steps32_c128_mtpost_t0p9_tpow1p0_noise0_state_catstep_top16_categorical_sample", "step": 118000, "n_samples": 8, "steps": 32, "concentration_max": 128.0, "temp_start": 0.9, "temp_end": 0.9, "temp_schedule": "const", "t_power": 1.0, "eta0": 0.0, "eta_schedule": "none", "noise_conc": 1.0, "final_from": "state", "final_decode": "argmax", "final_temp": 1.0, "final_top_k": 16, "final_uncertain_threshold": 0.85, "update_rule": "categorical_sample", "model_t_mode": "post", "lock_bos": true, "lock_final_eos": false, "detok_genppl": NaN, "sample_entropy": 1.3343823647761228, "distinct_1": 0.055908203125, "distinct_2": 0.11632453567937438, "top_token_mass": 0.48876953125, "tokens_scored": 0, "readability_score": 2.46729427930036, "mean_chars": 1457.375, "replacement_chars": 0.0}
+[decode] steps32_c128_mtpre_t0p9_tpow1p0_noise0_state_categorical_argmax
+[summary] {"name": "steps32_c128_mtpre_t0p9_tpow1p0_noise0_state_categorical_argmax", "step": 118000, "n_samples": 8, "steps": 32, "concentration_max": 128.0, "temp_start": 0.9, "temp_end": 0.9, "temp_schedule": "const", "t_power": 1.0, "eta0": 0.0, "eta_schedule": "none", "noise_conc": 1.0, "final_from": "state", "final_decode": "argmax", "final_temp": 1.0, "final_top_k": 64, "final_uncertain_threshold": 0.85, "update_rule": "categorical_argmax", "model_t_mode": "pre", "lock_bos": true, "lock_final_eos": false, "detok_genppl": NaN, "sample_entropy": 1.7124667587101636, "distinct_1": 0.2037353515625, "distinct_2": 0.36779081133919844, "top_token_mass": 0.3330078125, "tokens_scored": 0, "readability_score": 2.0797317483616076, "mean_chars": 2024.625, "replacement_chars": 0.0}
+[decode] steps32_c128_mtpost_t0p9_tpow1p0_noise0_state_categorical_argmax
+[summary] {"name": "steps32_c128_mtpost_t0p9_tpow1p0_noise0_state_categorical_argmax", "step": 118000, "n_samples": 8, "steps": 32, "concentration_max": 128.0, "temp_start": 0.9, "temp_end": 0.9, "temp_schedule": "const", "t_power": 1.0, "eta0": 0.0, "eta_schedule": "none", "noise_conc": 1.0, "final_from": "state", "final_decode": "argmax", "final_temp": 1.0, "final_top_k": 64, "final_uncertain_threshold": 0.85, "update_rule": "categorical_argmax", "model_t_mode": "post", "lock_bos": true, "lock_final_eos": false, "detok_genppl": NaN, "sample_entropy": 2.072406150304612, "distinct_1": 0.2462158203125, "distinct_2": 0.4319403714565005, "top_token_mass": 0.363525390625, "tokens_scored": 0, "readability_score": 3.8781453333859375, "mean_chars": 2446.625, "replacement_chars": 0.0}
+[decode] steps32_c128_mtpre_t0p9_tpow1p0_noise0_state_catstep_top64_categorical_sample
+[summary] {"name": "steps32_c128_mtpre_t0p9_tpow1p0_noise0_state_catstep_top64_categorical_sample", "step": 118000, "n_samples": 8, "steps": 32, "concentration_max": 128.0, "temp_start": 0.9, "temp_end": 0.9, "temp_schedule": "const", "t_power": 1.0, "eta0": 0.0, "eta_schedule": "none", "noise_conc": 1.0, "final_from": "state", "final_decode": "argmax", "final_temp": 1.0, "final_top_k": 64, "final_uncertain_threshold": 0.85, "update_rule": "categorical_sample", "model_t_mode": "pre", "lock_bos": true, "lock_final_eos": false, "detok_genppl": NaN, "sample_entropy": 1.800012647460023, "distinct_1": 0.0850830078125, "distinct_2": 0.19293743890518084, "top_token_mass": 0.471923828125, "tokens_scored": 0, "readability_score": 3.803961697268313, "mean_chars": 1491.875, "replacement_chars": 0.0}
+[decode] steps32_c128_mtpost_t0p9_tpow1p0_noise0_state_catstep_top64_categorical_sample
+[summary] {"name": "steps32_c128_mtpost_t0p9_tpow1p0_noise0_state_catstep_top64_categorical_sample", "step": 118000, "n_samples": 8, "steps": 32, "concentration_max": 128.0, "temp_start": 0.9, "temp_end": 0.9, "temp_schedule": "const", "t_power": 1.0, "eta0": 0.0, "eta_schedule": "none", "noise_conc": 1.0, "final_from": "state", "final_decode": "argmax", "final_temp": 1.0, "final_top_k": 64, "final_uncertain_threshold": 0.85, "update_rule": "categorical_sample", "model_t_mode": "post", "lock_bos": true, "lock_final_eos": false, "detok_genppl": NaN, "sample_entropy": 1.9604407576571412, "distinct_1": 0.1053466796875, "distinct_2": 0.22886119257086998, "top_token_mass": 0.3310546875, "tokens_scored": 0, "readability_score": 3.1418669071837146, "mean_chars": 2092.5, "replacement_chars": 0.0}
+[decode] steps32_c128_mtpre_t0p9_tpow1p0_noise0_endpoint_categorical_argmax
+[summary] {"name": "steps32_c128_mtpre_t0p9_tpow1p0_noise0_endpoint_categorical_argmax", "step": 118000, "n_samples": 8, "steps": 32, "concentration_max": 128.0, "temp_start": 0.9, "temp_end": 0.9, "temp_schedule": "const", "t_power": 1.0, "eta0": 0.0, "eta_schedule": "none", "noise_conc": 1.0, "final_from": "endpoint", "final_decode": "argmax", "final_temp": 1.0, "final_top_k": 16, "final_uncertain_threshold": 0.85, "update_rule": "categorical_argmax", "model_t_mode": "pre", "lock_bos": true, "lock_final_eos": false, "detok_genppl": NaN, "sample_entropy": 1.7124667587101636, "distinct_1": 0.2037353515625, "distinct_2": 0.36779081133919844, "top_token_mass": 0.3330078125, "tokens_scored": 0, "readability_score": 2.0797317483616076, "mean_chars": 2024.625, "replacement_chars": 0.0}
+[decode] steps32_c128_mtpost_t0p9_tpow1p0_noise0_endpoint_categorical_argmax
+[summary] {"name": "steps32_c128_mtpost_t0p9_tpow1p0_noise0_endpoint_categorical_argmax", "step": 118000, "n_samples": 8, "steps": 32, "concentration_max": 128.0, "temp_start": 0.9, "temp_end": 0.9, "temp_schedule": "const", "t_power": 1.0, "eta0": 0.0, "eta_schedule": "none", "noise_conc": 1.0, "final_from": "endpoint", "final_decode": "argmax", "final_temp": 1.0, "final_top_k": 16, "final_uncertain_threshold": 0.85, "update_rule": "categorical_argmax", "model_t_mode": "post", "lock_bos": true, "lock_final_eos": false, "detok_genppl": NaN, "sample_entropy": 2.072406150304612, "distinct_1": 0.2462158203125, "distinct_2": 0.4319403714565005, "top_token_mass": 0.363525390625, "tokens_scored": 0, "readability_score": 3.8781453333859375, "mean_chars": 2446.625, "replacement_chars": 0.0}
+[decode] steps32_c128_mtpre_t0p9_tpow1p0_noise0_endpoint_catstep_top16_categorical_sample
+[summary] {"name": "steps32_c128_mtpre_t0p9_tpow1p0_noise0_endpoint_catstep_top16_categorical_sample", "step": 118000, "n_samples": 8, "steps": 32, "concentration_max": 128.0, "temp_start": 0.9, "temp_end": 0.9, "temp_schedule": "const", "t_power": 1.0, "eta0": 0.0, "eta_schedule": "none", "noise_conc": 1.0, "final_from": "endpoint", "final_decode": "argmax", "final_temp": 1.0, "final_top_k": 16, "final_uncertain_threshold": 0.85, "update_rule": "categorical_sample", "model_t_mode": "pre", "lock_bos": true, "lock_final_eos": false, "detok_genppl": NaN, "sample_entropy": 1.4495944570112436, "distinct_1": 0.0716552734375, "distinct_2": 0.14821603128054742, "top_token_mass": 0.4456787109375, "tokens_scored": 0, "readability_score": 1.9542281177140906, "mean_chars": 1389.25, "replacement_chars": 0.0}
+[decode] steps32_c128_mtpost_t0p9_tpow1p0_noise0_endpoint_catstep_top16_categorical_sample
+[summary] {"name": "steps32_c128_mtpost_t0p9_tpow1p0_noise0_endpoint_catstep_top16_categorical_sample", "step": 118000, "n_samples": 8, "steps": 32, "concentration_max": 128.0, "temp_start": 0.9, "temp_end": 0.9, "temp_schedule": "const", "t_power": 1.0, "eta0": 0.0, "eta_schedule": "none", "noise_conc": 1.0, "final_from": "endpoint", "final_decode": "argmax", "final_temp": 1.0, "final_top_k": 16, "final_uncertain_threshold": 0.85, "update_rule": "categorical_sample", "model_t_mode": "post", "lock_bos": true, "lock_final_eos": false, "detok_genppl": NaN, "sample_entropy": 1.3316715807580835, "distinct_1": 0.05615234375, "distinct_2": 0.1170576735092864, "top_token_mass": 0.4906005859375, "tokens_scored": 0, "readability_score": 2.485367356707624, "mean_chars": 1454.875, "replacement_chars": 0.0}
+[decode] steps32_c128_mtpre_t0p9_tpow1p0_noise0_endpoint_categorical_argmax
+[summary] {"name": "steps32_c128_mtpre_t0p9_tpow1p0_noise0_endpoint_categorical_argmax", "step": 118000, "n_samples": 8, "steps": 32, "concentration_max": 128.0, "temp_start": 0.9, "temp_end": 0.9, "temp_schedule": "const", "t_power": 1.0, "eta0": 0.0, "eta_schedule": "none", "noise_conc": 1.0, "final_from": "endpoint", "final_decode": "argmax", "final_temp": 1.0, "final_top_k": 64, "final_uncertain_threshold": 0.85, "update_rule": "categorical_argmax", "model_t_mode": "pre", "lock_bos": true, "lock_final_eos": false, "detok_genppl": NaN, "sample_entropy": 1.7124667587101636, "distinct_1": 0.2037353515625, "distinct_2": 0.36779081133919844, "top_token_mass": 0.3330078125, "tokens_scored": 0, "readability_score": 2.0797317483616076, "mean_chars": 2024.625, "replacement_chars": 0.0}
+[decode] steps32_c128_mtpost_t0p9_tpow1p0_noise0_endpoint_categorical_argmax
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+[decode] steps32_c128_mtpre_t0p9_tpow1p0_noise0_endpoint_catstep_top64_categorical_sample
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+[decode] steps32_c128_mtpost_t0p9_tpow1p0_noise0_endpoint_catstep_top64_categorical_sample
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+[decode] steps32_c128_mtpre_t1p0_tpow1p0_noise0_state_categorical_argmax
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LTA_openwebtext_dualt/logs/softendpoint_mn_pilot_4gpu/pilot_softendpoint_mn_n128_m8_16_32_onehot_20260516_softendpoint_mn_pilot.log

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LTA_openwebtext_dualt/logs/softendpoint_mn_pilot_4gpu/train8_combo_len256_logistic_unigram_shared_C1024_20260517_170456.log

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+}
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+step=1200 epoch=1200/2000 epoch_step=1/1 micro_steps=1200 elapsed=3.8s lr=2.000000e-03 loss=0.1933 loss_recon=0.1933 loss_meanflow=0.0000 mean_model_t=0.2086 mean_corrupt_t=0.2086 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.9462 corrupt_frac=1.0000 acc_corrupt=0.9462 loss_corrupt=0.1933 wrong_frac=0.7913 init_acc_corrupt=0.2087 acc_corrupt_t_0p0_0p2=0.9044 corrupt_frac_t_0p0_0p2=0.5588 acc_corrupt_t_0p2_0p4=0.9990 corrupt_frac_t_0p2_0p4=0.3575 acc_corrupt_t_0p4_0p6=0.9998 corrupt_frac_t_0p4_0p6=0.0753 acc_corrupt_t_0p6_0p8=0.9997 corrupt_frac_t_0p6_0p8=0.0130 out_w_norm=11.4992 out_g_norm=0.2630 acc_corrupt_t_0p8_1p0=1.0000 corrupt_frac_t_0p8_1p0=0.0078 loss_all=0.2213 init_gold_top10=0.2240 init_gold_top100=0.2987
+step=1300 epoch=1300/2000 epoch_step=1/1 micro_steps=1300 elapsed=3.8s lr=2.000000e-03 loss=0.1427 loss_recon=0.1427 loss_meanflow=0.0000 mean_model_t=0.2113 mean_corrupt_t=0.2113 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.9602 corrupt_frac=1.0000 acc_corrupt=0.9602 loss_corrupt=0.1427 wrong_frac=0.7886 init_acc_corrupt=0.2114 acc_corrupt_t_0p0_0p2=0.9283 corrupt_frac_t_0p0_0p2=0.5517 acc_corrupt_t_0p2_0p4=0.9994 corrupt_frac_t_0p2_0p4=0.3592 acc_corrupt_t_0p4_0p6=0.9997 corrupt_frac_t_0p4_0p6=0.0802 acc_corrupt_t_0p6_0p8=0.9995 corrupt_frac_t_0p6_0p8=0.0121 out_w_norm=11.5852 out_g_norm=0.2256 acc_corrupt_t_0p8_1p0=1.0000 corrupt_frac_t_0p8_1p0=0.0078 loss_all=0.1299 init_gold_top10=0.2068 init_gold_top100=0.2804
+step=1400 epoch=1400/2000 epoch_step=1/1 micro_steps=1400 elapsed=3.8s lr=2.000000e-03 loss=0.1327 loss_recon=0.1327 loss_meanflow=0.0000 mean_model_t=0.2095 mean_corrupt_t=0.2095 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.9637 corrupt_frac=1.0000 acc_corrupt=0.9637 loss_corrupt=0.1327 wrong_frac=0.7907 init_acc_corrupt=0.2093 acc_corrupt_t_0p0_0p2=0.9349 corrupt_frac_t_0p0_0p2=0.5551 acc_corrupt_t_0p2_0p4=0.9997 corrupt_frac_t_0p2_0p4=0.3560 acc_corrupt_t_0p4_0p6=0.9999 corrupt_frac_t_0p4_0p6=0.0795 acc_corrupt_t_0p6_0p8=0.9997 corrupt_frac_t_0p6_0p8=0.0129 out_w_norm=11.6459 out_g_norm=0.2093 acc_corrupt_t_0p8_1p0=1.0000 corrupt_frac_t_0p8_1p0=0.0078 loss_all=0.0916 init_gold_top10=0.2126 init_gold_top100=0.2850
+W0517 17:10:36.514000 234504 torch/distributed/elastic/agent/server/api.py:719] Received 15 death signal, shutting down workers
+W0517 17:10:36.515000 234504 torch/distributed/elastic/multiprocessing/api.py:898] Sending process 234508 closing signal SIGTERM
+W0517 17:10:36.515000 234504 torch/distributed/elastic/multiprocessing/api.py:898] Sending process 234509 closing signal SIGTERM
+W0517 17:10:36.516000 234504 torch/distributed/elastic/multiprocessing/api.py:898] Sending process 234510 closing signal SIGTERM
+W0517 17:10:36.516000 234504 torch/distributed/elastic/multiprocessing/api.py:898] Sending process 234511 closing signal SIGTERM
+Traceback (most recent call last):
+  File "<frozen runpy>", line 198, in _run_module_as_main
+  File "<frozen runpy>", line 88, in _run_code
+  File "/usr/local/lib/python3.12/dist-packages/torch/distributed/run.py", line 922, in <module>
+    main()
+  File "/usr/local/lib/python3.12/dist-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 355, in wrapper
+    return f(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^
+  File "/usr/local/lib/python3.12/dist-packages/torch/distributed/run.py", line 918, in main
+    run(args)
+  File "/usr/local/lib/python3.12/dist-packages/torch/distributed/run.py", line 909, in run
+    elastic_launch(
+  File "/usr/local/lib/python3.12/dist-packages/torch/distributed/launcher/api.py", line 139, in __call__
+    return launch_agent(self._config, self._entrypoint, list(args))
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/usr/local/lib/python3.12/dist-packages/torch/distributed/launcher/api.py", line 261, in launch_agent
+    result = agent.run()
+             ^^^^^^^^^^^
+  File "/usr/local/lib/python3.12/dist-packages/torch/distributed/elastic/metrics/api.py", line 137, in wrapper
+    result = f(*args, **kwargs)
+             ^^^^^^^^^^^^^^^^^^
+  File "/usr/local/lib/python3.12/dist-packages/torch/distributed/elastic/agent/server/api.py", line 711, in run
+    result = self._invoke_run(role)
+             ^^^^^^^^^^^^^^^^^^^^^^
+  File "/usr/local/lib/python3.12/dist-packages/torch/distributed/elastic/agent/server/api.py", line 870, in _invoke_run
+    time.sleep(monitor_interval)
+  File "/usr/local/lib/python3.12/dist-packages/torch/distributed/elastic/multiprocessing/api.py", line 84, in _terminate_process_handler
+    raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval)
+torch.distributed.elastic.multiprocessing.api.SignalException: Process 234504 got signal: 15

LTA_openwebtext_dualt/logs/softendpoint_mn_pilot_4gpu/train8_ctx1024_path_p50_path3_unif0_0p25_outwdm1_ctx1024_path_tradeoff_sde_20260517_232950.log

@@ -0,0 +1,617 @@
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+  "grad_accum": 1,
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+  "min_lr": 0.0,
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+    "output_layer.linear.weight",
+    "output_layer.adaLN_modulation.weight"
+  ],
+  "muon_adam_param_names": [
+    "sigma_map.net.0.bias",
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+    "blocks.0.norm2.weight",
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+    "blocks.1.norm2.weight",
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+    "blocks.2.norm1.weight",
+    "blocks.2.norm2.weight",
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+    "blocks.2.mlp.2.bias",
+    "blocks.2.adaLN_modulation.bias",
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+    "output_layer.adaLN_modulation.bias"
+  ],
+  "muon_effective_nesterov": false,
+  "muon_effective_width_scale": false,
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+  "ema_start_step": 0,
+  "model_type": "ddit",
+  "ddit_mlp_type": "gelu",
+  "elf_num_time_tokens": 4,
+  "elf_num_model_mode_tokens": 0,
+  "qk_norm": true,
+  "output_bias": false,
+  "output_init_std": -1.0,
+  "norm_type": "rmsnorm",
+  "target_loss": "hard_ce",
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+  "bridge_noise_init": "logistic_normal",
+  "noise_sigma": -1.0,
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+  "activation_checkpointing": false,
+  "activation_checkpoint_interval": 1,
+  "activation_checkpoint_scope": "block",
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+  "ddp_gradient_as_bucket_view": true,
+  "blocking_data_transfer": false,
+  "dataloader_prefetch_factor": 4,
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+  "wrap": true,
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+  "owt_cached_chunks": true,
+  "owt_chunk_cache_dir": "/e2e-data/evad-tech-vla/wanghan58/data/small_benchmarks/langflow_2604_11748/openwebtext_lta_cached_chunks/gpt2_len1024_train8_compact_overfit",
+  "owt_chunk_cache_rebuild": false,
+  "owt_chunk_cache_write_batch": 4096,
+  "owt_exact_repeat_per_chunk": 64,
+  "online_chunk_shuffle": false,
+  "online_chunk_shuffle_buffer": 10000,
+  "openwebtext_split": "train_minus_100k",
+  "detokenizer": "auto",
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+}
+step=100 epoch=100/1000 epoch_step=1/1 micro_steps=100 elapsed=21.5s lr=2.000000e-03 loss=7.7229 loss_recon=7.7229 loss_meanflow=0.0000 mean_model_t=0.2076 mean_corrupt_t=0.2076 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.5055 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.0942 corrupt_frac=1.0000 acc_corrupt=0.0942 loss_corrupt=7.7229 wrong_frac=0.7923 init_acc_corrupt=0.1148 acc_corrupt_t_0p0_0p2=0.0501 corrupt_frac_t_0p0_0p2=0.5602 acc_corrupt_t_0p2_0p4=0.1248 corrupt_frac_t_0p2_0p4=0.3545 acc_corrupt_t_0p4_0p6=0.2468 corrupt_frac_t_0p4_0p6=0.0781 acc_corrupt_t_0p6_0p8=0.3541 corrupt_frac_t_0p6_0p8=0.0123 out_w_norm=0.9887 out_g_norm=1.1051 acc_corrupt_t_0p8_1p0=0.4805 corrupt_frac_t_0p8_1p0=0.0078 loss_all=7.4886 init_gold_top10=0.1877 init_gold_top100=0.4027 rollout_applied_pos_frac=0.4688 init_acc_rollout_applied=0.1012 init_acc_rollout_kept=0.1215 logit_acc_rollout_applied=0.0875 logit_acc_rollout_kept=0.1014
+step=200 epoch=200/1000 epoch_step=1/1 micro_steps=200 elapsed=20.4s lr=2.000000e-03 loss=7.0910 loss_recon=7.0910 loss_meanflow=0.0000 mean_model_t=0.2097 mean_corrupt_t=0.2097 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.4901 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.1032 corrupt_frac=1.0000 acc_corrupt=0.1032 loss_corrupt=7.0910 wrong_frac=0.7903 init_acc_corrupt=0.1175 acc_corrupt_t_0p0_0p2=0.0559 corrupt_frac_t_0p0_0p2=0.5515 acc_corrupt_t_0p2_0p4=0.1372 corrupt_frac_t_0p2_0p4=0.3636 acc_corrupt_t_0p4_0p6=0.2539 corrupt_frac_t_0p4_0p6=0.0764 out_w_norm=2.8096 out_g_norm=1.7834 acc_corrupt_t_0p6_0p8=0.3611 corrupt_frac_t_0p6_0p8=0.0139 acc_corrupt_t_0p8_1p0=0.3809 corrupt_frac_t_0p8_1p0=0.0078 loss_all=6.6724 init_gold_top10=0.2130 init_gold_top100=0.4321 rollout_applied_pos_frac=0.4531 init_acc_rollout_applied=0.1605 init_acc_rollout_kept=0.1231 logit_acc_rollout_applied=0.1265 logit_acc_rollout_kept=0.1099
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+}
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LTA_openwebtext_dualt/logs/softendpoint_mn_pilot_4gpu/train8_ctx1024_randk_p50_path3_rand1_3_unif0_0p25_ctx1024_uniformt_temp1_randk_20260518_010217.log

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+  "mask_mixture_lowk_clean_tokens": "0",
+  "mask_mixture_lowcorrupt_tokens": "1,2,4,8,16,32,64",
+  "mask_mixture_block_tokens": "64,128",
+  "simplex_bridge_sampler": "dirichlet",
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+  "logistic_normal_sigma_max": 1.0,
+  "logistic_normal_tau_min": 1.0,
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+  "compile_mode": "max-autotune",
+  "state_format": "prob",
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+  "rollout_train_steps_min": 1,
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+  "rollout_train_s_dist": "uniform",
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+  "rollout_train_corrupt_only": true,
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+  "rollout_train_sync_t": true,
+  "bridge_noise_init": "logistic_normal",
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+  "activation_checkpointing": false,
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+  "openwebtext_split": "train_minus_100k",
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+  "resume_path": "runs/train8_ctx1024_randk_p50_path3_rand1_3_unif0_0p25_ctx1024_uniformt_temp1_randk_20260518_010217/latest.pt"
+}
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+}
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+}
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LTA_openwebtext_dualt/logs/softendpoint_mn_pilot_4gpu/train8_ctx1024_t5tok_p35_rand0_4_unif0_0p25_outwdm1_t5tok_ctx1024_randk_20260518_014800.log

@@ -0,0 +1,1026 @@
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+}
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+step=2000 epoch=2000/2000 epoch_step=1/1 micro_steps=2000 elapsed=21.6s lr=2.000000e-03 loss=0.4949 loss_recon=0.4949 loss_meanflow=0.0000 mean_model_t=0.5005 mean_corrupt_t=0.5005 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.3489 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.8686 corrupt_frac=1.0000 acc_corrupt=0.8686 loss_corrupt=0.4949 wrong_frac=0.4995 init_acc_corrupt=0.5134 acc_corrupt_t_0p0_0p2=0.3647 corrupt_frac_t_0p0_0p2=0.2029 acc_corrupt_t_0p2_0p4=0.9877 corrupt_frac_t_0p2_0p4=0.1965 acc_corrupt_t_0p4_0p6=0.9996 corrupt_frac_t_0p4_0p6=0.1988 acc_corrupt_t_0p6_0p8=0.9999 corrupt_frac_t_0p6_0p8=0.2028 acc_corrupt_t_0p8_1p0=1.0000 corrupt_frac_t_0p8_1p0=0.1990 out_w_norm=13.1851 out_g_norm=0.5259 loss_all=0.2158 init_gold_top10=0.6500 init_gold_top100=0.7168 rollout_applied_pos_frac=0.3125 init_acc_rollout_applied=0.6294 init_acc_rollout_kept=0.5045 logit_acc_rollout_applied=0.9802 logit_acc_rollout_kept=0.9204
+NCCL version 2.25.1+cuda12.8
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+{
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+}
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+step=3200 epoch=3200/4000 epoch_step=1/1 micro_steps=3200 elapsed=21.4s lr=2.000000e-03 loss=0.2604 loss_recon=0.2604 loss_meanflow=0.0000 mean_model_t=0.4985 mean_corrupt_t=0.4985 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.3512 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.9118 corrupt_frac=1.0000 acc_corrupt=0.9118 loss_corrupt=0.2604 wrong_frac=0.5014 init_acc_corrupt=0.5157 acc_corrupt_t_0p0_0p2=0.5677 corrupt_frac_t_0p0_0p2=0.2037 acc_corrupt_t_0p2_0p4=0.9994 corrupt_frac_t_0p2_0p4=0.1982 acc_corrupt_t_0p4_0p6=1.0000 corrupt_frac_t_0p4_0p6=0.1956 acc_corrupt_t_0p6_0p8=1.0000 corrupt_frac_t_0p6_0p8=0.2056 acc_corrupt_t_0p8_1p0=1.0000 corrupt_frac_t_0p8_1p0=0.1969 out_w_norm=12.8221 out_g_norm=0.3907 loss_all=0.2038 init_gold_top10=0.6119 init_gold_top100=0.7017 rollout_applied_pos_frac=0.3438 init_acc_rollout_applied=0.6757 init_acc_rollout_kept=0.4511 logit_acc_rollout_applied=0.9773 logit_acc_rollout_kept=0.9039
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+}
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LTA_openwebtext_dualt/logs/softendpoint_mn_pilot_4gpu/train8_ctx1024_t5tok_p50_rand0_3_unif0_0p25_outwdm1_t5tok_ctx1024_k03_20260518_022728.log

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+  "wrap": true,
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+  "owt_cached_chunks": true,
+  "owt_chunk_cache_dir": "/e2e-data/evad-tech-vla/wanghan58/data/small_benchmarks/langflow_2604_11748/openwebtext_lta_cached_chunks/t5_len1024_train8_compact_overfit",
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+  "openwebtext_split": "train_minus_100k",
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+  "resume_path": "runs/train8_ctx1024_t5tok_p50_rand0_3_unif0_0p25_outwdm1_t5tok_ctx1024_k03_20260518_022728/latest.pt"
+}
+step=2100 epoch=2100/3000 epoch_step=1/1 micro_steps=2100 elapsed=22.9s lr=2.000000e-03 loss=0.4227 loss_recon=0.4227 loss_meanflow=0.0000 mean_model_t=0.4971 mean_corrupt_t=0.4971 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.5052 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.8784 corrupt_frac=1.0000 acc_corrupt=0.8784 loss_corrupt=0.4227 wrong_frac=0.5028 init_acc_corrupt=0.5099 acc_corrupt_t_0p0_0p2=0.4207 corrupt_frac_t_0p0_0p2=0.2071 acc_corrupt_t_0p2_0p4=0.9922 corrupt_frac_t_0p2_0p4=0.1988 acc_corrupt_t_0p4_0p6=0.9997 corrupt_frac_t_0p4_0p6=0.1976 acc_corrupt_t_0p6_0p8=0.9999 corrupt_frac_t_0p6_0p8=0.1951 acc_corrupt_t_0p8_1p0=1.0000 corrupt_frac_t_0p8_1p0=0.2015 out_w_norm=13.1370 out_g_norm=0.5656 loss_all=0.3018 init_gold_top10=0.6387 init_gold_top100=0.7422 rollout_applied_pos_frac=0.4688 init_acc_rollout_applied=0.5405 init_acc_rollout_kept=0.4382 logit_acc_rollout_applied=0.8902 logit_acc_rollout_kept=0.9257
+step=2200 epoch=2200/3000 epoch_step=1/1 micro_steps=2200 elapsed=21.9s lr=2.000000e-03 loss=0.3744 loss_recon=0.3744 loss_meanflow=0.0000 mean_model_t=0.5015 mean_corrupt_t=0.5015 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.5026 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.8896 corrupt_frac=1.0000 acc_corrupt=0.8896 loss_corrupt=0.3744 wrong_frac=0.4984 init_acc_corrupt=0.5127 acc_corrupt_t_0p0_0p2=0.4549 corrupt_frac_t_0p0_0p2=0.2005 acc_corrupt_t_0p2_0p4=0.9946 corrupt_frac_t_0p2_0p4=0.1979 acc_corrupt_t_0p4_0p6=0.9998 corrupt_frac_t_0p4_0p6=0.1994 acc_corrupt_t_0p6_0p8=0.9999 corrupt_frac_t_0p6_0p8=0.2007 acc_corrupt_t_0p8_1p0=1.0000 corrupt_frac_t_0p8_1p0=0.2015 out_w_norm=13.1374 out_g_norm=0.5428 loss_all=0.4181 init_gold_top10=0.6663 init_gold_top100=0.7683 rollout_applied_pos_frac=0.5391 init_acc_rollout_applied=0.5834 init_acc_rollout_kept=0.4670 logit_acc_rollout_applied=0.9081 logit_acc_rollout_kept=0.8371
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LTA_openwebtext_dualt/logs/softendpoint_mn_pilot_4gpu/train8_ctx1024_t5tok_p50_rand0_4_unif0_0p25_outwdm1_t5tok_ctx1024_randk_20260518_014436.log

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LTA_openwebtext_dualt/logs/softendpoint_mn_pilot_4gpu/train8_n1024_linear_soft_kl_onehot_20260517_train8_overfit.log

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+step=125 epoch=63/500 epoch_step=1/2 micro_steps=125 elapsed=7.3s lr=2.000000e-03 loss=1.5721 loss_recon=1.5721 loss_meanflow=0.0000 mean_model_t=0.2141 mean_corrupt_t=0.2141 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.2141 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.1815 corrupt_frac=0.8700 acc_corrupt=0.1145 loss_corrupt=1.8442 wrong_frac=0.7877 init_acc_corrupt=0.1171 acc_corrupt_t_0p0_0p2=0.0596 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=3.1369 out_g_norm=0.6446 acc_corrupt_t_0p4_0p6=0.2577 corrupt_frac_t_0p4_0p6=1.0000 acc_corrupt_t_0p2_0p4=0.1357 corrupt_frac_t_0p2_0p4=1.0000 loss_all=8.5182 init_gold_top10=0.2305 init_gold_top100=0.3047
+step=150 epoch=75/500 epoch_step=2/2 micro_steps=150 elapsed=3.2s lr=2.000000e-03 loss=1.3855 loss_recon=1.3855 loss_meanflow=0.0000 mean_model_t=0.2050 mean_corrupt_t=0.2050 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.2050 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2116 corrupt_frac=0.8400 acc_corrupt=0.1265 loss_corrupt=1.6168 wrong_frac=0.7929 init_acc_corrupt=0.1216 acc_corrupt_t_0p0_0p2=0.0653 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=3.7735 out_g_norm=0.6714 acc_corrupt_t_0p4_0p6=0.3057 corrupt_frac_t_0p4_0p6=1.0000 acc_corrupt_t_0p2_0p4=0.1802 corrupt_frac_t_0p2_0p4=1.0000 loss_all=7.4310 init_gold_top10=0.3073 init_gold_top100=0.3164
+step=175 epoch=88/500 epoch_step=1/2 micro_steps=175 elapsed=7.1s lr=2.000000e-03 loss=1.4102 loss_recon=1.4102 loss_meanflow=0.0000 mean_model_t=0.2183 mean_corrupt_t=0.2183 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.2183 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2136 corrupt_frac=0.8625 acc_corrupt=0.1376 loss_corrupt=1.7792 wrong_frac=0.7800 init_acc_corrupt=0.1370 acc_corrupt_t_0p2_0p4=0.1907 corrupt_frac_t_0p2_0p4=1.0000 out_w_norm=4.3787 out_g_norm=0.5865 acc_corrupt_t_0p0_0p2=0.0610 corrupt_frac_t_0p0_0p2=1.0000 acc_corrupt_t_0p4_0p6=0.3194 corrupt_frac_t_0p4_0p6=1.0000 loss_all=8.7739 init_gold_top10=0.0424 init_gold_top100=0.1719
+step=200 epoch=100/500 epoch_step=2/2 micro_steps=200 elapsed=3.2s lr=2.000000e-03 loss=1.1934 loss_recon=1.1934 loss_meanflow=0.0000 mean_model_t=0.1829 mean_corrupt_t=0.1829 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.1829 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2005 corrupt_frac=0.8450 acc_corrupt=0.1132 loss_corrupt=1.5587 wrong_frac=0.8122 init_acc_corrupt=0.0871 acc_corrupt_t_0p2_0p4=0.1561 corrupt_frac_t_0p2_0p4=1.0000 out_w_norm=4.7917 out_g_norm=0.5517 acc_corrupt_t_0p0_0p2=0.0642 corrupt_frac_t_0p0_0p2=1.0000 acc_corrupt_t_0p4_0p6=0.2998 corrupt_frac_t_0p4_0p6=1.0000 loss_all=8.7217 init_gold_top10=0.1198 init_gold_top100=0.2510
+step=225 epoch=113/500 epoch_step=1/2 micro_steps=225 elapsed=6.4s lr=2.000000e-03 loss=1.3249 loss_recon=1.3249 loss_meanflow=0.0000 mean_model_t=0.2100 mean_corrupt_t=0.2100 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.2100 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2210 corrupt_frac=0.8525 acc_corrupt=0.1355 loss_corrupt=1.6210 wrong_frac=0.7966 init_acc_corrupt=0.1112 acc_corrupt_t_0p2_0p4=0.1706 corrupt_frac_t_0p2_0p4=1.0000 out_w_norm=5.1207 out_g_norm=0.5293 acc_corrupt_t_0p0_0p2=0.0646 corrupt_frac_t_0p0_0p2=1.0000 acc_corrupt_t_0p4_0p6=0.3460 corrupt_frac_t_0p4_0p6=1.0000 loss_all=7.9293 init_gold_top10=0.2292 init_gold_top100=0.3083
+step=250 epoch=125/500 epoch_step=2/2 micro_steps=250 elapsed=3.2s lr=2.000000e-03 loss=0.9987 loss_recon=0.9987 loss_meanflow=0.0000 mean_model_t=0.1634 mean_corrupt_t=0.1634 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.1634 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.1912 corrupt_frac=0.8650 acc_corrupt=0.1063 loss_corrupt=1.3599 wrong_frac=0.8360 init_acc_corrupt=0.0634 acc_corrupt_t_0p2_0p4=0.1724 corrupt_frac_t_0p2_0p4=1.0000 out_w_norm=5.4342 out_g_norm=0.4328 acc_corrupt_t_0p0_0p2=0.0714 corrupt_frac_t_0p0_0p2=1.0000 acc_corrupt_t_0p4_0p6=0.3451 corrupt_frac_t_0p4_0p6=1.0000 loss_all=8.4824 init_gold_top10=0.0542 init_gold_top100=0.1740
+step=275 epoch=138/500 epoch_step=1/2 micro_steps=275 elapsed=7.4s lr=2.000000e-03 loss=1.2546 loss_recon=1.2546 loss_meanflow=0.0000 mean_model_t=0.2121 mean_corrupt_t=0.2121 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.2121 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2141 corrupt_frac=0.8775 acc_corrupt=0.1391 loss_corrupt=1.5386 wrong_frac=0.7930 init_acc_corrupt=0.1115 acc_corrupt_t_0p4_0p6=0.3962 corrupt_frac_t_0p4_0p6=1.0000 out_w_norm=5.7373 out_g_norm=0.4318 acc_corrupt_t_0p0_0p2=0.0664 corrupt_frac_t_0p0_0p2=1.0000 acc_corrupt_t_0p2_0p4=0.1931 corrupt_frac_t_0p2_0p4=1.0000 loss_all=7.5609 init_gold_top10=0.1663 init_gold_top100=0.2779
+step=300 epoch=150/500 epoch_step=2/2 micro_steps=300 elapsed=3.2s lr=2.000000e-03 loss=1.1000 loss_recon=1.1000 loss_meanflow=0.0000 mean_model_t=0.1955 mean_corrupt_t=0.1955 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.1955 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2408 corrupt_frac=0.8325 acc_corrupt=0.1357 loss_corrupt=1.3590 wrong_frac=0.8023 init_acc_corrupt=0.1065 acc_corrupt_t_0p2_0p4=0.2241 corrupt_frac_t_0p2_0p4=1.0000 out_w_norm=6.0310 out_g_norm=0.4127 acc_corrupt_t_0p0_0p2=0.0673 corrupt_frac_t_0p0_0p2=1.0000 acc_corrupt_t_0p4_0p6=0.5078 corrupt_frac_t_0p4_0p6=1.0000 loss_all=6.7362 init_gold_top10=0.1211 init_gold_top100=0.2526
+step=325 epoch=163/500 epoch_step=1/2 micro_steps=325 elapsed=7.2s lr=2.000000e-03 loss=0.9793 loss_recon=0.9793 loss_meanflow=0.0000 mean_model_t=0.1678 mean_corrupt_t=0.1678 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.1678 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2080 corrupt_frac=0.8550 acc_corrupt=0.1132 loss_corrupt=1.2116 wrong_frac=0.8324 init_acc_corrupt=0.0730 acc_corrupt_t_0p0_0p2=0.0721 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=6.3253 out_g_norm=0.4276 acc_corrupt_t_0p2_0p4=0.1988 corrupt_frac_t_0p2_0p4=1.0000 loss_all=6.4874 init_gold_top10=0.1589 init_gold_top100=0.2708
+step=350 epoch=175/500 epoch_step=2/2 micro_steps=350 elapsed=3.2s lr=2.000000e-03 loss=1.0407 loss_recon=1.0407 loss_meanflow=0.0000 mean_model_t=0.1906 mean_corrupt_t=0.1906 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.1906 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2343 corrupt_frac=0.8425 acc_corrupt=0.1336 loss_corrupt=1.2750 wrong_frac=0.8084 init_acc_corrupt=0.1011 acc_corrupt_t_0p0_0p2=0.0646 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=6.6144 out_g_norm=0.3903 acc_corrupt_t_0p2_0p4=0.1983 corrupt_frac_t_0p2_0p4=1.0000 acc_corrupt_t_0p4_0p6=0.3315 corrupt_frac_t_0p4_0p6=1.0000 loss_all=6.5952 init_gold_top10=0.2396 init_gold_top100=0.3047
+step=375 epoch=188/500 epoch_step=1/2 micro_steps=375 elapsed=6.0s lr=2.000000e-03 loss=1.1322 loss_recon=1.1322 loss_meanflow=0.0000 mean_model_t=0.2456 mean_corrupt_t=0.2456 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.2456 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2746 corrupt_frac=0.8675 acc_corrupt=0.1932 loss_corrupt=1.1824 wrong_frac=0.7527 init_acc_corrupt=0.1760 acc_corrupt_t_0p2_0p4=0.2197 corrupt_frac_t_0p2_0p4=1.0000 out_w_norm=6.8983 out_g_norm=0.3729 acc_corrupt_t_0p0_0p2=0.0641 corrupt_frac_t_0p0_0p2=1.0000 acc_corrupt_t_0p6_0p8=0.5789 corrupt_frac_t_0p6_0p8=1.0000 acc_corrupt_t_0p4_0p6=0.4135 corrupt_frac_t_0p4_0p6=1.0000 loss_all=3.3921 init_gold_top10=0.7824 init_gold_top100=0.7846
+step=400 epoch=200/500 epoch_step=2/2 micro_steps=400 elapsed=3.2s lr=2.000000e-03 loss=0.9991 loss_recon=0.9991 loss_meanflow=0.0000 mean_model_t=0.1998 mean_corrupt_t=0.1998 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.1998 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2586 corrupt_frac=0.8425 acc_corrupt=0.1536 loss_corrupt=1.2177 wrong_frac=0.8029 init_acc_corrupt=0.1143 acc_corrupt_t_0p2_0p4=0.2608 corrupt_frac_t_0p2_0p4=1.0000 out_w_norm=7.1964 out_g_norm=0.3839 acc_corrupt_t_0p0_0p2=0.0717 corrupt_frac_t_0p0_0p2=1.0000 loss_all=6.4061 init_gold_top10=0.3240 init_gold_top100=0.3250
+step=425 epoch=213/500 epoch_step=1/2 micro_steps=425 elapsed=6.6s lr=2.000000e-03 loss=1.0999 loss_recon=1.0999 loss_meanflow=0.0000 mean_model_t=0.2141 mean_corrupt_t=0.2141 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.2141 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2632 corrupt_frac=0.8525 acc_corrupt=0.1637 loss_corrupt=1.4368 wrong_frac=0.7856 init_acc_corrupt=0.1222 acc_corrupt_t_0p0_0p2=0.0835 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=7.5028 out_g_norm=0.3774 acc_corrupt_t_0p2_0p4=0.2364 corrupt_frac_t_0p2_0p4=1.0000 acc_corrupt_t_0p4_0p6=0.3469 corrupt_frac_t_0p4_0p6=1.0000 loss_all=7.9881 init_gold_top10=0.1000 init_gold_top100=0.2000
+step=450 epoch=225/500 epoch_step=2/2 micro_steps=450 elapsed=3.2s lr=2.000000e-03 loss=1.0844 loss_recon=1.0844 loss_meanflow=0.0000 mean_model_t=0.2233 mean_corrupt_t=0.2233 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.2233 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2737 corrupt_frac=0.8650 acc_corrupt=0.1816 loss_corrupt=1.3110 wrong_frac=0.7728 init_acc_corrupt=0.1468 acc_corrupt_t_0p2_0p4=0.2386 corrupt_frac_t_0p2_0p4=1.0000 out_w_norm=7.7913 out_g_norm=0.3997 acc_corrupt_t_0p0_0p2=0.0642 corrupt_frac_t_0p0_0p2=1.0000 acc_corrupt_t_0p4_0p6=0.4325 corrupt_frac_t_0p4_0p6=1.0000 loss_all=6.3435 init_gold_top10=0.2645 init_gold_top100=0.3292
+step=475 epoch=238/500 epoch_step=1/2 micro_steps=475 elapsed=7.1s lr=2.000000e-03 loss=0.9691 loss_recon=0.9691 loss_meanflow=0.0000 mean_model_t=0.1984 mean_corrupt_t=0.1984 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.1984 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2405 corrupt_frac=0.8700 acc_corrupt=0.1471 loss_corrupt=1.2060 wrong_frac=0.8033 init_acc_corrupt=0.0984 acc_corrupt_t_0p2_0p4=0.2344 corrupt_frac_t_0p2_0p4=1.0000 out_w_norm=8.0605 out_g_norm=0.3808 acc_corrupt_t_0p0_0p2=0.0813 corrupt_frac_t_0p0_0p2=1.0000 acc_corrupt_t_0p4_0p6=0.4854 corrupt_frac_t_0p4_0p6=1.0000 loss_all=6.1833 init_gold_top10=0.1551 init_gold_top100=0.2533
+step=500 epoch=250/500 epoch_step=2/2 micro_steps=500 elapsed=3.2s lr=2.000000e-03 loss=0.9673 loss_recon=0.9673 loss_meanflow=0.0000 mean_model_t=0.2001 mean_corrupt_t=0.2001 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.2001 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2664 corrupt_frac=0.8500 acc_corrupt=0.1540 loss_corrupt=1.2035 wrong_frac=0.8004 init_acc_corrupt=0.1043 acc_corrupt_t_0p0_0p2=0.0815 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=8.3192 out_g_norm=0.3685 acc_corrupt_t_0p2_0p4=0.2433 corrupt_frac_t_0p2_0p4=1.0000 acc_corrupt_t_0p4_0p6=0.4016 corrupt_frac_t_0p4_0p6=1.0000 loss_all=6.1066 init_gold_top10=0.0885 init_gold_top100=0.2018
+step=525 epoch=263/500 epoch_step=1/2 micro_steps=525 elapsed=6.0s lr=2.000000e-03 loss=0.9982 loss_recon=0.9982 loss_meanflow=0.0000 mean_model_t=0.2159 mean_corrupt_t=0.2159 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.2159 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2740 corrupt_frac=0.8600 acc_corrupt=0.1689 loss_corrupt=1.2558 wrong_frac=0.7809 init_acc_corrupt=0.1125 acc_corrupt_t_0p2_0p4=0.1805 corrupt_frac_t_0p2_0p4=1.0000 out_w_norm=8.5659 out_g_norm=0.3468 acc_corrupt_t_0p0_0p2=0.0852 corrupt_frac_t_0p0_0p2=1.0000 acc_corrupt_t_0p4_0p6=0.4840 corrupt_frac_t_0p4_0p6=1.0000 loss_all=6.4480 init_gold_top10=0.1574 init_gold_top100=0.2444
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LTA_openwebtext_dualt/logs/softendpoint_mn_pilot_4gpu/train8_n8_allcorrupt_linear_soft_kl_20260517_train8ctx8_allcorrupt.log

@@ -0,0 +1,326 @@
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+step=130 epoch=65/250 epoch_step=2/2 micro_steps=130 elapsed=4.6s lr=2.000000e-03 loss=1.4130 loss_recon=1.4130 loss_meanflow=0.0000 mean_model_t=0.2286 mean_corrupt_t=0.2286 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.2286 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2375 corrupt_frac=1.0000 acc_corrupt=0.2375 loss_corrupt=2.1573 wrong_frac=0.7875 init_acc_corrupt=0.1375 acc_corrupt_t_0p0_0p2=0.1667 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=1.1300 out_g_norm=2.4490 acc_corrupt_t_0p2_0p4=0.2679 corrupt_frac_t_0p2_0p4=1.0000 loss_all=8.8047 init_gold_top10=0.1250 init_gold_top100=0.1250
+step=140 epoch=70/250 epoch_step=2/2 micro_steps=140 elapsed=4.4s lr=2.000000e-03 loss=1.0509 loss_recon=1.0509 loss_meanflow=0.0000 mean_model_t=0.1682 mean_corrupt_t=0.1682 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.1682 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.1875 corrupt_frac=1.0000 acc_corrupt=0.1875 loss_corrupt=1.9215 wrong_frac=0.8375 init_acc_corrupt=0.0500 acc_corrupt_t_0p0_0p2=0.1250 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=1.2307 out_g_norm=2.0366 acc_corrupt_t_0p2_0p4=0.3333 corrupt_frac_t_0p2_0p4=1.0000 loss_all=9.1719 init_gold_top10=0.1250 init_gold_top100=0.1250
+step=150 epoch=75/250 epoch_step=2/2 micro_steps=150 elapsed=4.1s lr=2.000000e-03 loss=1.3402 loss_recon=1.3402 loss_meanflow=0.0000 mean_model_t=0.2326 mean_corrupt_t=0.2326 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.2326 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2625 corrupt_frac=1.0000 acc_corrupt=0.2625 loss_corrupt=1.9238 wrong_frac=0.7625 init_acc_corrupt=0.1375 acc_corrupt_t_0p0_0p2=0.1750 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=1.3304 out_g_norm=1.9295 acc_corrupt_t_0p2_0p4=0.3125 corrupt_frac_t_0p2_0p4=1.0000 acc_corrupt_t_0p4_0p6=0.5000 corrupt_frac_t_0p4_0p6=1.0000 loss_all=8.0742 init_gold_top10=0.2500 init_gold_top100=0.2500
+step=160 epoch=80/250 epoch_step=2/2 micro_steps=160 elapsed=4.5s lr=2.000000e-03 loss=0.8337 loss_recon=0.8337 loss_meanflow=0.0000 mean_model_t=0.1882 mean_corrupt_t=0.1882 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.1882 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.3375 corrupt_frac=1.0000 acc_corrupt=0.3375 loss_corrupt=1.6679 wrong_frac=0.7500 init_acc_corrupt=0.1750 acc_corrupt_t_0p2_0p4=0.5000 corrupt_frac_t_0p2_0p4=1.0000 out_w_norm=1.4319 out_g_norm=1.8421 acc_corrupt_t_0p0_0p2=0.1750 corrupt_frac_t_0p0_0p2=1.0000 loss_all=8.7930 init_gold_top10=0.0000 init_gold_top100=0.0000
+step=170 epoch=85/250 epoch_step=2/2 micro_steps=170 elapsed=5.3s lr=2.000000e-03 loss=0.9756 loss_recon=0.9756 loss_meanflow=0.0000 mean_model_t=0.2017 mean_corrupt_t=0.2017 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.2017 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2250 corrupt_frac=1.0000 acc_corrupt=0.2250 loss_corrupt=1.3851 wrong_frac=0.7250 init_acc_corrupt=0.1000 acc_corrupt_t_0p2_0p4=0.3750 corrupt_frac_t_0p2_0p4=1.0000 out_w_norm=1.5338 out_g_norm=1.6552 acc_corrupt_t_0p0_0p2=0.0750 corrupt_frac_t_0p0_0p2=1.0000 loss_all=4.8604 init_gold_top10=0.5000 init_gold_top100=0.6250
+step=180 epoch=90/250 epoch_step=2/2 micro_steps=180 elapsed=4.1s lr=2.000000e-03 loss=1.0499 loss_recon=1.0499 loss_meanflow=0.0000 mean_model_t=0.2081 mean_corrupt_t=0.2081 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.2081 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2375 corrupt_frac=1.0000 acc_corrupt=0.2375 loss_corrupt=1.7474 wrong_frac=0.7875 init_acc_corrupt=0.1250 acc_corrupt_t_0p0_0p2=0.1667 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=1.6309 out_g_norm=1.7936 acc_corrupt_t_0p2_0p4=0.3438 corrupt_frac_t_0p2_0p4=1.0000 loss_all=8.0332 init_gold_top10=0.3750 init_gold_top100=0.5000
+step=190 epoch=95/250 epoch_step=2/2 micro_steps=190 elapsed=4.5s lr=2.000000e-03 loss=0.9665 loss_recon=0.9665 loss_meanflow=0.0000 mean_model_t=0.1942 mean_corrupt_t=0.1942 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.1942 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2250 corrupt_frac=1.0000 acc_corrupt=0.2250 loss_corrupt=1.7928 wrong_frac=0.8625 init_acc_corrupt=0.0625 acc_corrupt_t_0p0_0p2=0.1458 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=1.7201 out_g_norm=2.0560 acc_corrupt_t_0p2_0p4=0.3438 corrupt_frac_t_0p2_0p4=1.0000 loss_all=8.8516 init_gold_top10=0.1250 init_gold_top100=0.1250
+step=200 epoch=100/250 epoch_step=2/2 micro_steps=200 elapsed=4.4s lr=2.000000e-03 loss=1.0310 loss_recon=1.0310 loss_meanflow=0.0000 mean_model_t=0.1970 mean_corrupt_t=0.1970 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.1970 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.1625 corrupt_frac=1.0000 acc_corrupt=0.1625 loss_corrupt=1.8039 wrong_frac=0.8250 init_acc_corrupt=0.0625 acc_corrupt_t_0p0_0p2=0.1250 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=1.8020 out_g_norm=2.0184 acc_corrupt_t_0p2_0p4=0.2500 corrupt_frac_t_0p2_0p4=1.0000 loss_all=7.9961 init_gold_top10=0.0000 init_gold_top100=0.3750
+step=210 epoch=105/250 epoch_step=2/2 micro_steps=210 elapsed=4.1s lr=2.000000e-03 loss=0.8071 loss_recon=0.8071 loss_meanflow=0.0000 mean_model_t=0.1880 mean_corrupt_t=0.1880 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.1880 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2500 corrupt_frac=1.0000 acc_corrupt=0.2500 loss_corrupt=1.2439 wrong_frac=0.8500 init_acc_corrupt=0.0875 acc_corrupt_t_0p2_0p4=0.3125 corrupt_frac_t_0p2_0p4=1.0000 out_w_norm=1.8702 out_g_norm=2.0898 acc_corrupt_t_0p0_0p2=0.2083 corrupt_frac_t_0p0_0p2=1.0000 loss_all=4.9170 init_gold_top10=0.2500 init_gold_top100=0.3750
+step=220 epoch=110/250 epoch_step=2/2 micro_steps=220 elapsed=4.5s lr=2.000000e-03 loss=0.8528 loss_recon=0.8528 loss_meanflow=0.0000 mean_model_t=0.1718 mean_corrupt_t=0.1718 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.1718 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2000 corrupt_frac=1.0000 acc_corrupt=0.2000 loss_corrupt=1.6071 wrong_frac=0.8500 init_acc_corrupt=0.0375 acc_corrupt_t_0p0_0p2=0.2083 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=1.9181 out_g_norm=1.7059 acc_corrupt_t_0p2_0p4=0.1875 corrupt_frac_t_0p2_0p4=1.0000 loss_all=8.7812 init_gold_top10=0.1250 init_gold_top100=0.1250
+step=230 epoch=115/250 epoch_step=2/2 micro_steps=230 elapsed=4.4s lr=2.000000e-03 loss=0.8167 loss_recon=0.8167 loss_meanflow=0.0000 mean_model_t=0.3049 mean_corrupt_t=0.3049 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.3049 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.4375 corrupt_frac=1.0000 acc_corrupt=0.4375 loss_corrupt=1.4772 wrong_frac=0.6500 init_acc_corrupt=0.2875 acc_corrupt_t_0p4_0p6=0.7917 corrupt_frac_t_0p4_0p6=1.0000 out_w_norm=1.9604 out_g_norm=2.2768 acc_corrupt_t_0p2_0p4=0.4583 corrupt_frac_t_0p2_0p4=1.0000 acc_corrupt_t_0p0_0p2=0.1562 corrupt_frac_t_0p0_0p2=1.0000 loss_all=7.4824 init_gold_top10=0.2500 init_gold_top100=0.2500
+step=240 epoch=120/250 epoch_step=2/2 micro_steps=240 elapsed=4.1s lr=2.000000e-03 loss=0.8203 loss_recon=0.8203 loss_meanflow=0.0000 mean_model_t=0.2693 mean_corrupt_t=0.2693 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.2693 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.3375 corrupt_frac=1.0000 acc_corrupt=0.3375 loss_corrupt=1.1419 wrong_frac=0.7125 init_acc_corrupt=0.2000 acc_corrupt_t_0p0_0p2=0.1875 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=2.0042 out_g_norm=2.2456 acc_corrupt_t_0p4_0p6=0.6250 corrupt_frac_t_0p4_0p6=1.0000 acc_corrupt_t_0p2_0p4=0.3438 corrupt_frac_t_0p2_0p4=1.0000 loss_all=4.4014 init_gold_top10=0.2500 init_gold_top100=0.2500
+step=250 epoch=125/250 epoch_step=2/2 micro_steps=250 elapsed=4.5s lr=2.000000e-03 loss=0.5640 loss_recon=0.5640 loss_meanflow=0.0000 mean_model_t=0.1964 mean_corrupt_t=0.1964 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.1964 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2875 corrupt_frac=1.0000 acc_corrupt=0.2875 loss_corrupt=0.8368 wrong_frac=0.7500 init_acc_corrupt=0.1250 acc_corrupt_t_0p0_0p2=0.1719 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=2.0422 out_g_norm=1.8608 acc_corrupt_t_0p6_0p8=0.8750 corrupt_frac_t_0p6_0p8=1.0000 acc_corrupt_t_0p2_0p4=0.6250 corrupt_frac_t_0p2_0p4=1.0000 loss_all=3.4316 init_gold_top10=0.5000 init_gold_top100=0.5000
+step=260 epoch=130/250 epoch_step=2/2 micro_steps=260 elapsed=4.4s lr=2.000000e-03 loss=0.6435 loss_recon=0.6435 loss_meanflow=0.0000 mean_model_t=0.1517 mean_corrupt_t=0.1517 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.1517 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.1500 corrupt_frac=1.0000 acc_corrupt=0.1500 loss_corrupt=1.1853 wrong_frac=0.8750 init_acc_corrupt=0.0250 acc_corrupt_t_0p0_0p2=0.1429 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=2.0589 out_g_norm=1.9505 acc_corrupt_t_0p2_0p4=0.1667 corrupt_frac_t_0p2_0p4=1.0000 loss_all=5.7266 init_gold_top10=0.1250 init_gold_top100=0.5000
+step=270 epoch=135/250 epoch_step=2/2 micro_steps=270 elapsed=4.0s lr=2.000000e-03 loss=0.6368 loss_recon=0.6368 loss_meanflow=0.0000 mean_model_t=0.1781 mean_corrupt_t=0.1781 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.1781 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2500 corrupt_frac=1.0000 acc_corrupt=0.2500 loss_corrupt=1.0227 wrong_frac=0.7750 init_acc_corrupt=0.0875 acc_corrupt_t_0p0_0p2=0.1786 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=2.0719 out_g_norm=2.0863 acc_corrupt_t_0p2_0p4=0.4167 corrupt_frac_t_0p2_0p4=1.0000 loss_all=4.4688 init_gold_top10=0.3750 init_gold_top100=0.3750
+step=280 epoch=140/250 epoch_step=2/2 micro_steps=280 elapsed=4.5s lr=2.000000e-03 loss=0.8962 loss_recon=0.8962 loss_meanflow=0.0000 mean_model_t=0.2199 mean_corrupt_t=0.2199 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.2199 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.1750 corrupt_frac=1.0000 acc_corrupt=0.1750 loss_corrupt=1.4513 wrong_frac=0.8000 init_acc_corrupt=0.0625 acc_corrupt_t_0p4_0p6=0.0000 corrupt_frac_t_0p4_0p6=1.0000 out_w_norm=2.0855 out_g_norm=2.2083 acc_corrupt_t_0p0_0p2=0.1000 corrupt_frac_t_0p0_0p2=1.0000 acc_corrupt_t_0p2_0p4=0.3125 corrupt_frac_t_0p2_0p4=1.0000 loss_all=6.1973 init_gold_top10=0.1250 init_gold_top100=0.2500
+step=290 epoch=145/250 epoch_step=2/2 micro_steps=290 elapsed=4.4s lr=2.000000e-03 loss=0.6349 loss_recon=0.6349 loss_meanflow=0.0000 mean_model_t=0.2100 mean_corrupt_t=0.2100 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.2100 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.3000 corrupt_frac=1.0000 acc_corrupt=0.3000 loss_corrupt=1.0752 wrong_frac=0.7625 init_acc_corrupt=0.1625 acc_corrupt_t_0p2_0p4=0.3750 corrupt_frac_t_0p2_0p4=1.0000 out_w_norm=2.1006 out_g_norm=2.0542 acc_corrupt_t_0p0_0p2=0.2250 corrupt_frac_t_0p0_0p2=1.0000 loss_all=5.5759 init_gold_top10=0.2500 init_gold_top100=0.2500
+step=300 epoch=150/250 epoch_step=2/2 micro_steps=300 elapsed=4.1s lr=2.000000e-03 loss=0.6497 loss_recon=0.6497 loss_meanflow=0.0000 mean_model_t=0.2037 mean_corrupt_t=0.2037 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.2037 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2625 corrupt_frac=1.0000 acc_corrupt=0.2625 loss_corrupt=1.3369 wrong_frac=0.7250 init_acc_corrupt=0.0875 acc_corrupt_t_0p0_0p2=0.1750 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=2.1204 out_g_norm=2.0059 acc_corrupt_t_0p2_0p4=0.3500 corrupt_frac_t_0p2_0p4=1.0000 loss_all=7.2656 init_gold_top10=0.1250 init_gold_top100=0.5000
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LTA_openwebtext_dualt/logs/softendpoint_mn_pilot_4gpu/train8_n8_hard_ce_bridge_20260517_train8ctx8_overfit.log

@@ -0,0 +1,326 @@
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+step=130 epoch=65/250 epoch_step=2/2 micro_steps=130 elapsed=5.2s lr=2.000000e-03 loss=9.1183 loss_recon=9.1183 loss_meanflow=0.0000 mean_model_t=0.2275 mean_corrupt_t=0.2275 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.1625 corrupt_frac=0.6375 acc_corrupt=0.1176 loss_corrupt=9.1183 wrong_frac=0.7451 init_acc_corrupt=0.1176 acc_corrupt_t_0p0_0p2=0.1111 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=1.0883 out_g_norm=10.5466 acc_corrupt_t_0p2_0p4=0.1304 corrupt_frac_t_0p2_0p4=1.0000 acc_corrupt_t_0p4_0p6=0.1000 corrupt_frac_t_0p4_0p6=1.0000 loss_all=9.5859 init_gold_top10=0.2500 init_gold_top100=0.2500
+step=140 epoch=70/250 epoch_step=2/2 micro_steps=140 elapsed=4.5s lr=2.000000e-03 loss=8.4357 loss_recon=8.4357 loss_meanflow=0.0000 mean_model_t=0.2169 mean_corrupt_t=0.2169 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2000 corrupt_frac=0.6500 acc_corrupt=0.2115 loss_corrupt=8.4357 wrong_frac=0.8077 init_acc_corrupt=0.0769 acc_corrupt_t_0p2_0p4=0.2857 corrupt_frac_t_0p2_0p4=1.0000 out_w_norm=1.1685 out_g_norm=11.3089 acc_corrupt_t_0p0_0p2=0.1875 corrupt_frac_t_0p0_0p2=1.0000 acc_corrupt_t_0p4_0p6=0.1667 corrupt_frac_t_0p4_0p6=1.0000 loss_all=8.0703 init_gold_top10=0.3333 init_gold_top100=0.3333
+step=150 epoch=75/250 epoch_step=2/2 micro_steps=150 elapsed=4.1s lr=2.000000e-03 loss=7.9952 loss_recon=7.9952 loss_meanflow=0.0000 mean_model_t=0.2117 mean_corrupt_t=0.2117 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.3625 corrupt_frac=0.7250 acc_corrupt=0.3276 loss_corrupt=7.9952 wrong_frac=0.6724 init_acc_corrupt=0.1552 acc_corrupt_t_0p0_0p2=0.1905 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=1.2502 out_g_norm=11.1589 acc_corrupt_t_0p2_0p4=0.4054 corrupt_frac_t_0p2_0p4=1.0000 loss_all=8.3125 init_gold_top10=0.0000 init_gold_top100=0.2500
+step=160 epoch=80/250 epoch_step=2/2 micro_steps=160 elapsed=4.6s lr=2.000000e-03 loss=7.8244 loss_recon=7.8244 loss_meanflow=0.0000 mean_model_t=0.1973 mean_corrupt_t=0.1973 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2250 corrupt_frac=0.6875 acc_corrupt=0.1818 loss_corrupt=7.8244 wrong_frac=0.8727 init_acc_corrupt=0.0545 acc_corrupt_t_0p0_0p2=0.1538 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=1.3346 out_g_norm=11.5378 acc_corrupt_t_0p2_0p4=0.2069 corrupt_frac_t_0p2_0p4=1.0000 loss_all=6.5117 init_gold_top10=0.0000 init_gold_top100=0.0000
+step=170 epoch=85/250 epoch_step=2/2 micro_steps=170 elapsed=4.4s lr=2.000000e-03 loss=8.0737 loss_recon=8.0737 loss_meanflow=0.0000 mean_model_t=0.1858 mean_corrupt_t=0.1858 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2375 corrupt_frac=0.7000 acc_corrupt=0.1429 loss_corrupt=8.0737 wrong_frac=0.8929 init_acc_corrupt=0.0179 acc_corrupt_t_0p0_0p2=0.1429 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=1.4206 out_g_norm=11.3048 acc_corrupt_t_0p2_0p4=0.1429 corrupt_frac_t_0p2_0p4=1.0000 loss_all=7.0273 init_gold_top10=0.1667 init_gold_top100=0.5000
+step=180 epoch=90/250 epoch_step=2/2 micro_steps=180 elapsed=4.0s lr=2.000000e-03 loss=7.6565 loss_recon=7.6565 loss_meanflow=0.0000 mean_model_t=0.1801 mean_corrupt_t=0.1801 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.3000 corrupt_frac=0.7000 acc_corrupt=0.2321 loss_corrupt=7.6565 wrong_frac=0.8214 init_acc_corrupt=0.0893 acc_corrupt_t_0p0_0p2=0.1579 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=1.5044 out_g_norm=11.5652 acc_corrupt_t_0p2_0p4=0.2857 corrupt_frac_t_0p2_0p4=1.0000 acc_corrupt_t_0p4_0p6=0.7500 corrupt_frac_t_0p4_0p6=1.0000 loss_all=4.9751 init_gold_top10=0.5000 init_gold_top100=0.5000
+step=190 epoch=95/250 epoch_step=2/2 micro_steps=190 elapsed=4.5s lr=2.000000e-03 loss=7.0855 loss_recon=7.0855 loss_meanflow=0.0000 mean_model_t=0.2116 mean_corrupt_t=0.2116 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2875 corrupt_frac=0.7000 acc_corrupt=0.2321 loss_corrupt=7.0855 wrong_frac=0.7679 init_acc_corrupt=0.1250 acc_corrupt_t_0p0_0p2=0.0870 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=1.5845 out_g_norm=11.7599 acc_corrupt_t_0p2_0p4=0.3103 corrupt_frac_t_0p2_0p4=1.0000 acc_corrupt_t_0p4_0p6=0.5000 corrupt_frac_t_0p4_0p6=1.0000 loss_all=8.2539 init_gold_top10=0.0000 init_gold_top100=0.3333
+step=200 epoch=100/250 epoch_step=2/2 micro_steps=200 elapsed=4.4s lr=2.000000e-03 loss=7.0000 loss_recon=7.0000 loss_meanflow=0.0000 mean_model_t=0.1888 mean_corrupt_t=0.1888 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2500 corrupt_frac=0.6500 acc_corrupt=0.2115 loss_corrupt=7.0000 wrong_frac=0.7692 init_acc_corrupt=0.0769 acc_corrupt_t_0p2_0p4=0.2353 corrupt_frac_t_0p2_0p4=1.0000 out_w_norm=1.6635 out_g_norm=11.4887 acc_corrupt_t_0p0_0p2=0.1667 corrupt_frac_t_0p0_0p2=1.0000 loss_all=4.9365 init_gold_top10=0.0000 init_gold_top100=0.2500
+step=210 epoch=105/250 epoch_step=2/2 micro_steps=210 elapsed=4.1s lr=2.000000e-03 loss=6.3846 loss_recon=6.3846 loss_meanflow=0.0000 mean_model_t=0.2222 mean_corrupt_t=0.2222 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.4125 corrupt_frac=0.7250 acc_corrupt=0.3276 loss_corrupt=6.3846 wrong_frac=0.7759 init_acc_corrupt=0.0862 acc_corrupt_t_0p2_0p4=0.4500 corrupt_frac_t_0p2_0p4=1.0000 out_w_norm=1.7426 out_g_norm=11.8660 acc_corrupt_t_0p0_0p2=0.0556 corrupt_frac_t_0p0_0p2=1.0000 loss_all=2.2339 init_gold_top10=0.7500 init_gold_top100=0.7500
+step=220 epoch=110/250 epoch_step=2/2 micro_steps=220 elapsed=4.5s lr=2.000000e-03 loss=6.1355 loss_recon=6.1355 loss_meanflow=0.0000 mean_model_t=0.1720 mean_corrupt_t=0.1720 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.3125 corrupt_frac=0.7000 acc_corrupt=0.2500 loss_corrupt=6.1355 wrong_frac=0.8214 init_acc_corrupt=0.0893 acc_corrupt_t_0p0_0p2=0.1714 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=1.8209 out_g_norm=11.3624 acc_corrupt_t_0p2_0p4=0.3810 corrupt_frac_t_0p2_0p4=1.0000 loss_all=7.0828 init_gold_top10=0.1667 init_gold_top100=0.1667
+step=230 epoch=115/250 epoch_step=2/2 micro_steps=230 elapsed=4.4s lr=2.000000e-03 loss=4.8994 loss_recon=4.8994 loss_meanflow=0.0000 mean_model_t=0.3280 mean_corrupt_t=0.3280 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.5250 corrupt_frac=0.6000 acc_corrupt=0.3542 loss_corrupt=4.8994 wrong_frac=0.6250 init_acc_corrupt=0.2500 acc_corrupt_t_0p0_0p2=0.2000 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=1.8942 out_g_norm=11.3461 acc_corrupt_t_0p6_0p8=0.7500 corrupt_frac_t_0p6_0p8=1.0000 acc_corrupt_t_0p4_0p6=0.5000 corrupt_frac_t_0p4_0p6=1.0000 acc_corrupt_t_0p2_0p4=0.2667 corrupt_frac_t_0p2_0p4=1.0000 loss_all=1.5292 init_gold_top10=0.5000 init_gold_top100=0.5000
+step=240 epoch=120/250 epoch_step=2/2 micro_steps=240 elapsed=4.1s lr=2.000000e-03 loss=5.5022 loss_recon=5.5022 loss_meanflow=0.0000 mean_model_t=0.2149 mean_corrupt_t=0.2149 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.3625 corrupt_frac=0.7000 acc_corrupt=0.2857 loss_corrupt=5.5022 wrong_frac=0.8393 init_acc_corrupt=0.0893 acc_corrupt_t_0p0_0p2=0.2051 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=1.9660 out_g_norm=11.3102 acc_corrupt_t_0p6_0p8=0.3333 corrupt_frac_t_0p6_0p8=1.0000 acc_corrupt_t_0p2_0p4=0.5455 corrupt_frac_t_0p2_0p4=1.0000 loss_all=3.3633 init_gold_top10=0.2857 init_gold_top100=0.2857
+step=250 epoch=125/250 epoch_step=2/2 micro_steps=250 elapsed=4.5s lr=2.000000e-03 loss=5.3018 loss_recon=5.3018 loss_meanflow=0.0000 mean_model_t=0.1982 mean_corrupt_t=0.1982 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2625 corrupt_frac=0.7625 acc_corrupt=0.2459 loss_corrupt=5.3018 wrong_frac=0.8197 init_acc_corrupt=0.0984 acc_corrupt_t_0p2_0p4=0.4286 corrupt_frac_t_0p2_0p4=1.0000 out_w_norm=2.0377 out_g_norm=11.2366 acc_corrupt_t_0p0_0p2=0.1707 corrupt_frac_t_0p0_0p2=1.0000 acc_corrupt_t_0p4_0p6=0.3333 corrupt_frac_t_0p4_0p6=1.0000 loss_all=6.6230 init_gold_top10=0.0000 init_gold_top100=0.0000
+step=260 epoch=130/250 epoch_step=2/2 micro_steps=260 elapsed=4.4s lr=2.000000e-03 loss=6.4312 loss_recon=6.4312 loss_meanflow=0.0000 mean_model_t=0.1619 mean_corrupt_t=0.1619 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.1750 corrupt_frac=0.7250 acc_corrupt=0.1552 loss_corrupt=6.4312 wrong_frac=0.8621 init_acc_corrupt=0.0345 acc_corrupt_t_0p0_0p2=0.1463 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=2.1092 out_g_norm=11.2571 acc_corrupt_t_0p2_0p4=0.1765 corrupt_frac_t_0p2_0p4=1.0000 loss_all=2.7271 init_gold_top10=0.0000 init_gold_top100=0.5000
+step=270 epoch=135/250 epoch_step=2/2 micro_steps=270 elapsed=4.1s lr=2.000000e-03 loss=5.7238 loss_recon=5.7238 loss_meanflow=0.0000 mean_model_t=0.1655 mean_corrupt_t=0.1655 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.3125 corrupt_frac=0.7875 acc_corrupt=0.2381 loss_corrupt=5.7238 wrong_frac=0.8889 init_acc_corrupt=0.0794 acc_corrupt_t_0p0_0p2=0.1957 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=2.1768 out_g_norm=11.7862 acc_corrupt_t_0p2_0p4=0.3529 corrupt_frac_t_0p2_0p4=1.0000 loss_all=1.9777 init_gold_top10=0.0000 init_gold_top100=0.0000
+step=280 epoch=140/250 epoch_step=2/2 micro_steps=280 elapsed=4.5s lr=2.000000e-03 loss=4.8150 loss_recon=4.8150 loss_meanflow=0.0000 mean_model_t=0.1937 mean_corrupt_t=0.1937 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.3250 corrupt_frac=0.6750 acc_corrupt=0.1852 loss_corrupt=4.8150 wrong_frac=0.8333 init_acc_corrupt=0.0556 acc_corrupt_t_0p0_0p2=0.1429 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=2.2385 out_g_norm=12.7686 acc_corrupt_t_0p4_0p6=0.2500 corrupt_frac_t_0p4_0p6=1.0000 acc_corrupt_t_0p2_0p4=0.3750 corrupt_frac_t_0p2_0p4=1.0000 loss_all=5.2206 init_gold_top10=0.0000 init_gold_top100=0.1667
+step=290 epoch=145/250 epoch_step=2/2 micro_steps=290 elapsed=4.4s lr=2.000000e-03 loss=5.0242 loss_recon=5.0242 loss_meanflow=0.0000 mean_model_t=0.1749 mean_corrupt_t=0.1749 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2750 corrupt_frac=0.7500 acc_corrupt=0.2333 loss_corrupt=5.0242 wrong_frac=0.9167 init_acc_corrupt=0.0667 acc_corrupt_t_0p0_0p2=0.1622 corrupt_frac_t_0p0_0p2=1.0000 out_w_norm=2.2959 out_g_norm=12.2660 acc_corrupt_t_0p2_0p4=0.3478 corrupt_frac_t_0p2_0p4=1.0000 loss_all=2.4589 init_gold_top10=0.3333 init_gold_top100=0.3333
+step=300 epoch=150/250 epoch_step=2/2 micro_steps=300 elapsed=4.0s lr=2.000000e-03 loss=5.8570 loss_recon=5.8570 loss_meanflow=0.0000 mean_model_t=0.1787 mean_corrupt_t=0.1787 mean_loss_t_weight=1.0000 linear_soft_target_mean_conf=0.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.2750 corrupt_frac=0.6375 acc_corrupt=0.1176 loss_corrupt=5.8570 wrong_frac=0.8627 init_acc_corrupt=0.0392 acc_corrupt_t_0p2_0p4=0.2000 corrupt_frac_t_0p2_0p4=1.0000 out_w_norm=2.3494 out_g_norm=12.8436 acc_corrupt_t_0p0_0p2=0.0385 corrupt_frac_t_0p0_0p2=1.0000 loss_all=2.8794 init_gold_top10=0.0000 init_gold_top100=0.5000
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LTA_openwebtext_dualt/logs/softendpoint_mn_pilot_4gpu/train8_noisegeo_len256_allcorrupt_seqrand1_dirC1_1024_20260517_163805.log

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LTA_openwebtext_dualt/logs/softendpoint_mn_pilot_4gpu/train8_noisegeo_len256_allcorrupt_seqrand1_highC64_4096_20260517_163805.log

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+}
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+}
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LTA_openwebtext_dualt/logs/softendpoint_mn_pilot_4gpu/train8_rollin_len256_rollin_p10_s4_i32_20260517_171654.log

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LTA_openwebtext_dualt/logs/softendpoint_mn_pilot_4gpu/train8_rollin_len256_rollin_p25_s8_i64_20260517_171654.log

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LTA_openwebtext_dualt/logs/softendpoint_mn_pilot_4gpu/train8_rollin_synct_len256_synct_p50_s4_i32_20260517_1800synct.log

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LTA_openwebtext_dualt/logs/wmt14_deen_elfofficial_4gpu_debug/debug_wmt14_deen_elfofficial_t5_len128_in64_dditelf_muon_logitnormal_m1p5_s0p8_none_floor0p0_zeroout_4gpu_20260516_020150.log

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LTA_openwebtext_dualt/logs/wmt14_deen_elfofficial_4gpu_debug/debug_wmt14_deen_elfofficial_t5_len256_in128_dditelf_muon_logitnormal_m1p5_s0p8_none_floor0p0_zeroout_4gpu_20260516_014755.log

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+[demo] target: Wir warten nun ab und sehen den nächsten Schritten entgegen.
+[rank0]: Traceback (most recent call last):
+[rank0]:   File "/e2e-data/evad-tech-vla/wanghan58/workspace/LTA_openwebtext_dualt/train.py", line 2490, in <module>
+[rank0]:     main()
+[rank0]:   File "/e2e-data/evad-tech-vla/wanghan58/workspace/LTA_openwebtext_dualt/train.py", line 2476, in main
+[rank0]:     run_demo(args, unwrap_model(trainable_model), tokenizer, last_batch, device)
+[rank0]:   File "/usr/local/lib/python3.12/dist-packages/torch/utils/_contextlib.py", line 116, in decorate_context
+[rank0]:     return func(*args, **kwargs)
+[rank0]:            ^^^^^^^^^^^^^^^^^^^^^
+[rank0]:   File "/e2e-data/evad-tech-vla/wanghan58/workspace/LTA_openwebtext_dualt/train.py", line 1608, in run_demo
+[rank0]:     init, mask = fill_blank_init(
+[rank0]:                  ^^^^^^^^^^^^^^^^
+[rank0]:   File "/usr/local/lib/python3.12/dist-packages/torch/utils/_contextlib.py", line 116, in decorate_context
+[rank0]:     return func(*args, **kwargs)
+[rank0]:            ^^^^^^^^^^^^^^^^^^^^^
+[rank0]:   File "/e2e-data/evad-tech-vla/wanghan58/workspace/LTA_openwebtext_dualt/flowtext_lab/decode.py", line 193, in fill_blank_init
+[rank0]:     noise = sample_noise_simplex(
+[rank0]:             ^^^^^^^^^^^^^^^^^^^^^
+[rank0]:   File "/e2e-data/evad-tech-vla/wanghan58/workspace/LTA_openwebtext_dualt/flowtext_lab/decode.py", line 32, in sample_noise_simplex
+[rank0]:     sigma = resolve_noise_sigma(vocab_size, target_prob, noise_sigma)
+[rank0]:             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank0]:   File "/e2e-data/evad-tech-vla/wanghan58/workspace/LTA_openwebtext_dualt/flowtext_lab/decode.py", line 16, in resolve_noise_sigma
+[rank0]:     return float(margin_for_target_prob(vocab_size, target_prob))
+[rank0]:                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank0]:   File "/e2e-data/evad-tech-vla/wanghan58/workspace/LTA_openwebtext_dualt/flowtext_lab/bridges.py", line 111, in margin_for_target_prob
+[rank0]:     raise ValueError(f"target_prob must be in (0, 1), got {q}")
+[rank0]: ValueError: target_prob must be in (0, 1), got 1.0
+[rank0]:[W516 01:49:18.259818969 ProcessGroupNCCL.cpp:1487] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())

LTA_openwebtext_dualt/logs/wmt14_deen_elfofficial_4gpu_debug/debug_wmt14_deen_elfofficial_t5_len256_in128_dditelf_muon_logitnormal_m1p5_s0p8_none_floor0p0_zeroout_4gpu_20260516_015703.log

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+    "blocks.11.attn_out.weight",
+    "blocks.11.mlp.w12.weight",
+    "blocks.11.mlp.w3.weight"
+  ],
+  "muon_adam_param_names": [
+    "time_tokens",
+    "vocab_embed.embedding",
+    "sigma_map.net.0.weight",
+    "sigma_map.net.0.bias",
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+    "blocks.0.q_norm.weight",
+    "blocks.0.k_norm.weight",
+    "blocks.0.norm2.weight",
+    "blocks.0.mlp.w12.bias",
+    "blocks.0.mlp.w3.bias",
+    "blocks.1.norm1.weight",
+    "blocks.1.attn_qkv.bias",
+    "blocks.1.attn_out.bias",
+    "blocks.1.q_norm.weight",
+    "blocks.1.k_norm.weight",
+    "blocks.1.norm2.weight",
+    "blocks.1.mlp.w12.bias",
+    "blocks.1.mlp.w3.bias",
+    "blocks.2.norm1.weight",
+    "blocks.2.attn_qkv.bias",
+    "blocks.2.attn_out.bias",
+    "blocks.2.q_norm.weight",
+    "blocks.2.k_norm.weight",
+    "blocks.2.norm2.weight",
+    "blocks.2.mlp.w12.bias",
+    "blocks.2.mlp.w3.bias",
+    "blocks.3.norm1.weight",
+    "blocks.3.attn_qkv.bias",
+    "blocks.3.attn_out.bias",
+    "blocks.3.q_norm.weight",
+    "blocks.3.k_norm.weight",
+    "blocks.3.norm2.weight",
+    "blocks.3.mlp.w12.bias",
+    "blocks.3.mlp.w3.bias",
+    "blocks.4.norm1.weight",
+    "blocks.4.attn_qkv.bias",
+    "blocks.4.attn_out.bias",
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+    "blocks.4.k_norm.weight",
+    "blocks.4.norm2.weight",
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+    "blocks.4.mlp.w3.bias",
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+    "blocks.5.k_norm.weight",
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+    "blocks.5.mlp.w3.bias",
+    "blocks.6.norm1.weight",
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+    "blocks.6.k_norm.weight",
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+    "blocks.6.mlp.w3.bias",
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+    "blocks.8.attn_out.bias",
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+    "blocks.8.k_norm.weight",
+    "blocks.8.norm2.weight",
+    "blocks.8.mlp.w12.bias",
+    "blocks.8.mlp.w3.bias",
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+    "blocks.9.attn_qkv.bias",
+    "blocks.9.attn_out.bias",
+    "blocks.9.q_norm.weight",
+    "blocks.9.k_norm.weight",
+    "blocks.9.norm2.weight",
+    "blocks.9.mlp.w12.bias",
+    "blocks.9.mlp.w3.bias",
+    "blocks.10.norm1.weight",
+    "blocks.10.attn_qkv.bias",
+    "blocks.10.attn_out.bias",
+    "blocks.10.q_norm.weight",
+    "blocks.10.k_norm.weight",
+    "blocks.10.norm2.weight",
+    "blocks.10.mlp.w12.bias",
+    "blocks.10.mlp.w3.bias",
+    "blocks.11.norm1.weight",
+    "blocks.11.attn_qkv.bias",
+    "blocks.11.attn_out.bias",
+    "blocks.11.q_norm.weight",
+    "blocks.11.k_norm.weight",
+    "blocks.11.norm2.weight",
+    "blocks.11.mlp.w12.bias",
+    "blocks.11.mlp.w3.bias",
+    "output_layer.norm_final.weight",
+    "output_layer.linear.weight"
+  ],
+  "muon_effective_nesterov": true,
+  "muon_effective_width_scale": true,
+  "muon_effective_weight_decay": 0.0,
+  "muon_adam_fallback_nesterov": true,
+  "muon_adam_fallback_weight_decay": 0.0,
+  "ema_decay": 0.9999,
+  "ema_start_step": 0,
+  "model_type": "ddit_elf",
+  "elf_num_time_tokens": 4,
+  "elf_num_model_mode_tokens": 0,
+  "qk_norm": true,
+  "output_bias": false,
+  "output_init_std": 0.0,
+  "norm_type": "rmsnorm",
+  "t_sampling_mode": "logit_normal",
+  "t_sampling_power": 1.0,
+  "t_sampling_eps": 0.0001,
+  "t_sampling_logit_mean": -1.5,
+  "t_sampling_logit_std": 0.8,
+  "dual_t": true,
+  "corrupt_t_mode": "same",
+  "corrupt_min_t": 0.0,
+  "corrupt_max_t": 1.0,
+  "prefix_block_prob": 0.0,
+  "prefix_block_len": 128,
+  "mask_ratio_floor_schedule": "none",
+  "dirichlet_endpoint_mode": "categorical_dual_t",
+  "dirichlet_semantic_t_mode": "same",
+  "dirichlet_semantic_t_value": 0.0,
+  "dirichlet_semantic_t_curve": "linear",
+  "dirichlet_semantic_t_power": 1.0,
+  "endpoint_sequence_random_prob_alpha": 0.0,
+  "categorical_wrong_from_full_vocab": true,
+  "categorical_wrong_from_batch_valid_tokens": false,
+  "categorical_wrong_basin_token_ids": "",
+  "categorical_wrong_basin_prob": 0.0,
+  "categorical_wrong_unigram_prob": 0.0,
+  "categorical_wrong_uniform_prob": 0.0,
+  "categorical_wrong_corpus_unigram_path": "",
+  "categorical_wrong_corpus_unigram_alpha": 1.0,
+  "categorical_wrong_basin_shared_prob": 0.0,
+  "categorical_wrong_unigram_shared_prob": 0.0,
+  "mask_mixture_original_prob": 0.0,
+  "mask_mixture_lowk_prob": 0.0,
+  "mask_mixture_lowcorrupt_prob": 0.0,
+  "mask_mixture_block_prob": 0.0,
+  "mask_mixture_all_prob": 0.0,
+  "mask_mixture_lowk_clean_tokens": "1,2,4,8,16,32,64",
+  "mask_mixture_lowcorrupt_tokens": "1,2,4,8,16,32,64",
+  "mask_mixture_block_tokens": "64,128",
+  "simplex_bridge_sampler": "dirichlet",
+  "logistic_normal_sigma_min": 0.18,
+  "logistic_normal_sigma_max": 2.2,
+  "logistic_normal_tau_min": 0.65,
+  "logistic_normal_tau_max": 1.15,
+  "torch_compile": false,
+  "compile_mode": "max-autotune",
+  "state_format": "prob",
+  "target_loss": "hard_ce",
+  "meanflow_weight": 0.0,
+  "rollout_train_prob": 0.0,
+  "rollout_train_steps": 1,
+  "rollout_train_infer_steps": 64,
+  "rollout_train_temp": 1.45,
+  "rollout_train_max_gamma": 1.0,
+  "rollout_train_corrupt_only": true,
+  "rollout_train_samplewise": false,
+  "rollout_train_compute_always": false,
+  "bridge_noise_init": "logistic_normal",
+  "noise_sigma": -1.0,
+  "allow_tf32": true,
+  "activation_checkpointing": true,
+  "activation_checkpoint_interval": 1,
+  "activation_checkpoint_scope": "mlp",
+  "ddp_static_graph": false,
+  "ddp_gradient_as_bucket_view": true,
+  "blocking_data_transfer": false,
+  "dataloader_prefetch_factor": 4,
+  "full_train_stats": false,
+  "tokenized_hf": false,
+  "tokenized_pad_token": "pad",
+  "elf_conditional_hf": true,
+  "record_pad_truncate": false,
+  "record_add_eos": false,
+  "record_add_special_tokens": false,
+  "record_pad_token": "pad",
+  "record_shuffle_buffer": 10000,
+  "wrap": false,
+  "wrap_mode": "stream",
+  "wrap_record_buffer_size": 200,
+  "owt_cached_chunks": false,
+  "owt_chunk_cache_dir": "",
+  "owt_chunk_cache_rebuild": false,
+  "owt_chunk_cache_write_batch": 4096,
+  "owt_exact_repeat_per_chunk": 0,
+  "online_chunk_shuffle": false,
+  "online_chunk_shuffle_buffer": 10000,
+  "openwebtext_split": "all",
+  "detokenizer": "auto",
+  "resolved_detokenizer": null,
+  "num_workers": 8,
+  "latest_every": 10,
+  "resume_path": ""
+}
+step=5 epoch=1/2 epoch_step=5/16 micro_steps=20 elapsed=5.2s lr=7.500000e-04 loss=9.9922 loss_recon=9.9922 loss_meanflow=0.0000 mean_model_t=0.2073 mean_corrupt_t=0.2073 mean_loss_t_weight=1.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.0125 corrupt_frac=0.2568 acc_corrupt=0.0172 loss_corrupt=9.9922 wrong_frac=0.7924 init_acc_corrupt=0.1154 acc_corrupt_t_0p0_0p2=0.0162 corrupt_frac_t_0p0_0p2=0.5674 acc_corrupt_t_0p2_0p4=0.0191 corrupt_frac_t_0p2_0p4=0.3424 out_w_norm=2.5805 out_g_norm=1.3452 acc_corrupt_t_0p4_0p6=0.0137 corrupt_frac_t_0p4_0p6=0.0888 acc_corrupt_t_0p6_0p8=0.0313 corrupt_frac_t_0p6_0p8=0.0376 loss_all=9.7110 init_gold_top10=0.1817 init_gold_top100=0.5263
+step=10 epoch=1/2 epoch_step=10/16 micro_steps=40 elapsed=4.3s lr=1.000000e-03 loss=7.8761 loss_recon=7.8761 loss_meanflow=0.0000 mean_model_t=0.2004 mean_corrupt_t=0.2004 mean_loss_t_weight=1.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.0000 grad_enabled_before_rollout=1.0000 grad_enabled_after_rollout=1.0000 logits_requires_grad=1.0000 raw_loss_requires_grad=1.0000 acc_all=0.0298 corrupt_frac=0.2547 acc_corrupt=0.0496 loss_corrupt=7.8761 wrong_frac=0.8020 init_acc_corrupt=0.1007 acc_corrupt_t_0p0_0p2=0.0507 corrupt_frac_t_0p0_0p2=0.5641 acc_corrupt_t_0p2_0p4=0.0468 corrupt_frac_t_0p2_0p4=0.3677 acc_corrupt_t_0p4_0p6=0.0535 corrupt_frac_t_0p4_0p6=0.0669 out_w_norm=15.1139 out_g_norm=1.6920 acc_corrupt_t_0p6_0p8=0.0784 corrupt_frac_t_0p6_0p8=0.0454 loss_all=8.5945 init_gold_top10=0.1755 init_gold_top100=0.5223
+[demo] target: Wir warten nun ab und sehen den nächsten Schritten entgegen.
+[rank0]: Traceback (most recent call last):
+[rank0]:   File "/e2e-data/evad-tech-vla/wanghan58/workspace/LTA_openwebtext_dualt/train.py", line 2490, in <module>
+[rank0]:     main()
+[rank0]:   File "/e2e-data/evad-tech-vla/wanghan58/workspace/LTA_openwebtext_dualt/train.py", line 2476, in main
+[rank0]:     run_demo(args, unwrap_model(trainable_model), tokenizer, last_batch, device)
+[rank0]:   File "/usr/local/lib/python3.12/dist-packages/torch/utils/_contextlib.py", line 116, in decorate_context
+[rank0]:     return func(*args, **kwargs)
+[rank0]:            ^^^^^^^^^^^^^^^^^^^^^
+[rank0]:   File "/e2e-data/evad-tech-vla/wanghan58/workspace/LTA_openwebtext_dualt/train.py", line 1608, in run_demo
+[rank0]:     init, mask = fill_blank_init(
+[rank0]:                  ^^^^^^^^^^^^^^^^
+[rank0]:   File "/usr/local/lib/python3.12/dist-packages/torch/utils/_contextlib.py", line 116, in decorate_context
+[rank0]:     return func(*args, **kwargs)
+[rank0]:            ^^^^^^^^^^^^^^^^^^^^^
+[rank0]:   File "/e2e-data/evad-tech-vla/wanghan58/workspace/LTA_openwebtext_dualt/flowtext_lab/decode.py", line 193, in fill_blank_init
+[rank0]:     noise = sample_noise_simplex(
+[rank0]:             ^^^^^^^^^^^^^^^^^^^^^
+[rank0]:   File "/e2e-data/evad-tech-vla/wanghan58/workspace/LTA_openwebtext_dualt/flowtext_lab/decode.py", line 32, in sample_noise_simplex
+[rank0]:     sigma = resolve_noise_sigma(vocab_size, target_prob, noise_sigma)
+[rank0]:             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank0]:   File "/e2e-data/evad-tech-vla/wanghan58/workspace/LTA_openwebtext_dualt/flowtext_lab/decode.py", line 16, in resolve_noise_sigma
+[rank0]:     return float(margin_for_target_prob(vocab_size, target_prob))
+[rank0]:                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank0]:   File "/e2e-data/evad-tech-vla/wanghan58/workspace/LTA_openwebtext_dualt/flowtext_lab/bridges.py", line 111, in margin_for_target_prob
+[rank0]:     raise ValueError(f"target_prob must be in (0, 1), got {q}")
+[rank0]: ValueError: target_prob must be in (0, 1), got 1.0
+[rank0]:[W516 01:58:18.216258226 ProcessGroupNCCL.cpp:1487] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())

LTA_openwebtext_dualt/mini_owt_fit/model.py

@@ -0,0 +1,121 @@
+from __future__ import annotations
+
+import math
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+def apply_rope(x: torch.Tensor, prefix_len: int) -> torch.Tensor:
+    """RoPE for q/k tensors shaped [B, heads, length, head_dim].
+
+    Time prefix tokens are not rotated. Text token position starts at 0.
+    """
+
+    prefix = x[..., :prefix_len, :]
+    body = x[..., prefix_len:, :]
+    b, h, l, d = body.shape
+    half = d // 2
+
+    pos = torch.arange(l, device=x.device, dtype=torch.float32)
+    freq = torch.exp(-math.log(10000.0) * torch.arange(half, device=x.device) / half)
+    angle = pos[:, None] * freq[None, :]
+    cos = angle.cos()[None, None]
+    sin = angle.sin()[None, None]
+
+    pair = body.float().reshape(b, h, l, half, 2)
+    x0 = pair[..., 0]
+    x1 = pair[..., 1]
+    body = torch.stack((x0 * cos - x1 * sin, x0 * sin + x1 * cos), dim=-1).flatten(-2)
+    body = body.to(dtype=x.dtype)
+    return torch.cat([prefix, body], dim=-2)
+
+
+class Block(nn.Module):
+    def __init__(self, dim: int, heads: int, mlp_dim: int) -> None:
+        super().__init__()
+        self.heads = heads
+        self.head_dim = dim // heads
+        self.norm1 = nn.LayerNorm(dim)
+        self.qkv = nn.Linear(dim, 3 * dim, bias=False)
+        self.proj = nn.Linear(dim, dim, bias=False)
+        self.norm2 = nn.LayerNorm(dim)
+        self.mlp = nn.Sequential(
+            nn.Linear(dim, mlp_dim),
+            nn.GELU(approximate="tanh"),
+            nn.Linear(mlp_dim, dim),
+        )
+
+    def forward(self, x: torch.Tensor, prefix_len: int, use_rope: bool) -> torch.Tensor:
+        b, l, d = x.shape
+        qkv = self.qkv(self.norm1(x)).view(b, l, 3, self.heads, self.head_dim)
+        q, k, v = qkv.unbind(dim=2)
+        q = q.transpose(1, 2)
+        k = k.transpose(1, 2)
+        v = v.transpose(1, 2)
+        if use_rope:
+            q = apply_rope(q, prefix_len)
+            k = apply_rope(k, prefix_len)
+        y = F.scaled_dot_product_attention(q, k, v, is_causal=False)
+        y = y.transpose(1, 2).reshape(b, l, d)
+        x = x + self.proj(y)
+        x = x + self.mlp(self.norm2(x))
+        return x
+
+
+class TinyFlowLM(nn.Module):
+    """Endpoint predictor for probability-cloud inputs.
+
+    Input:
+        probs: [batch, length, vocab]
+        t:     [batch]
+
+    Conditioning:
+        four learnable time tokens are prepended. Each receives the same
+        continuous time embedding. No AdaLN, no gates, no hidden-stream FiLM.
+    """
+
+    def __init__(
+        self,
+        *,
+        vocab_size: int,
+        max_len: int,
+        dim: int = 256,
+        layers: int = 3,
+        heads: int = 4,
+        mlp_dim: int = 1024,
+        time_tokens: int = 4,
+        abs_pos: bool = True,
+        rope: bool = True,
+    ) -> None:
+        super().__init__()
+        self.vocab_size = vocab_size
+        self.max_len = max_len
+        self.time_tokens = time_tokens
+        self.rope = rope
+
+        self.token_embed = nn.Parameter(torch.randn(vocab_size, dim) * 0.02)
+        self.pos = nn.Embedding(max_len, dim) if abs_pos else None
+        self.time_base = nn.Parameter(torch.randn(1, time_tokens, dim) * 0.02)
+        self.time_mlp = nn.Sequential(nn.Linear(1, dim), nn.SiLU(), nn.Linear(dim, dim))
+        self.blocks = nn.ModuleList(Block(dim, heads, mlp_dim) for _ in range(layers))
+        self.norm = nn.LayerNorm(dim)
+        self.head = nn.Linear(dim, vocab_size, bias=False)
+
+    def forward(self, probs: torch.Tensor, t: torch.Tensor) -> torch.Tensor:
+        b, l, _ = probs.shape
+        x = probs @ self.token_embed
+        if self.pos is not None:
+            pos = torch.arange(l, device=x.device)
+            x = x + self.pos(pos)[None]
+
+        t_embed = self.time_mlp(t.float().view(b, 1))
+        prefix = self.time_base.expand(b, -1, -1) + t_embed[:, None]
+        x = torch.cat([prefix, x], dim=1)
+
+        for block in self.blocks:
+            x = block(x, prefix_len=self.time_tokens, use_rope=self.rope)
+
+        x = x[:, self.time_tokens:]
+        return self.head(self.norm(x))

LTA_openwebtext_dualt/mini_owt_fit/run_standard_owt_t5_8gpu.sh

@@ -0,0 +1,60 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+cd /e2e-data/evad-tech-vla/wanghan58/workspace/LTA_openwebtext_dualt/mini_owt_fit
+
+export CUDA_VISIBLE_DEVICES="${CUDA_VISIBLE_DEVICES:-0,1,2,3,4,5,6,7}"
+export TOKENIZERS_PARALLELISM=false
+export PYTHONUNBUFFERED=1
+
+NNODES=${MLP_WORKER_NUM:-1}
+NODE_RANK=${MLP_ROLE_INDEX:-0}
+MASTER_ADDR=${MLP_WORKER_0_HOST:-127.0.0.1}
+MASTER_PORT=${MLP_WORKER_0_PORT:-29500}
+
+DATA_PATH="${DATA_PATH:-/e2e-data/evad-tech-vla/wanghan58/data/small_benchmarks/langflow_2604_11748/openwebtext}"
+TOKENIZER_PATH="${TOKENIZER_PATH:-/e2e-data/evad-tech-vla/wanghan58/models/hf/t5-small/tokenizer.json}"
+CACHE_PATH="${CACHE_PATH:-cache/owt_t5_payload1022_appendeos1.pt}"
+
+DATE_TAG="${DATE_TAG:-$(date +%Y%m%d_%H%M%S)}"
+RUN_NAME="${RUN_NAME:-mini_owt_fit_t5_bernoulliwrong_len1024_bos_eos_C1_to_1024_absrope_time4_d768_l12_h12_native_nofloor_full_gbs512_8gpu_${DATE_TAG}}"
+OUT_DIR="${OUT_DIR:-runs/${RUN_NAME}}"
+LOG_FILE="${LOG_FILE:-logs/${RUN_NAME}.log}"
+
+mkdir -p "$(dirname "${LOG_FILE}")" "${OUT_DIR}"
+
+torchrun \
+  --nnodes="${NNODES}" \
+  --node_rank="${NODE_RANK}" \
+  --master_addr="${MASTER_ADDR}" \
+  --master_port="${MASTER_PORT}" \
+  --nproc_per_node="${NPROC_PER_NODE:-8}" \
+  train.py \
+  --data_path "${DATA_PATH}" \
+  --tokenizer_path "${TOKENIZER_PATH}" \
+  --out_dir "${OUT_DIR}" \
+  --subset_size "${SUBSET_SIZE:-0}" \
+  --payload_len "${PAYLOAD_LEN:-1022}" \
+  --append_eos "${APPEND_EOS:-1}" \
+  --log_skips "${LOG_SKIPS:-20}" \
+  --cache_path "${CACHE_PATH}" \
+  --rebuild_cache "${REBUILD_CACHE:-0}" \
+  --online_data "${ONLINE_DATA:-0}" \
+  --online_buffer_size "${ONLINE_BUFFER_SIZE:-8192}" \
+  --steps "${STEPS:-1000000}" \
+  --batch_size "${BATCH_SIZE:-32}" \
+  --grad_accum "${GRAD_ACCUM:-2}" \
+  --lr "${LR:-3e-4}" \
+  --log_every "${LOG_EVERY:-50}" \
+  --save_every "${SAVE_EVERY:-1000}" \
+  --dim "${DIM:-768}" \
+  --layers "${LAYERS:-12}" \
+  --heads "${HEADS:-12}" \
+  --mlp_dim "${MLP_DIM:-3072}" \
+  --time_tokens "${TIME_TOKENS:-4}" \
+  --abs_pos "${ABS_POS:-1}" \
+  --rope "${ROPE:-1}" \
+  --c_min "${CMIN:-1}" \
+  --c_max "${CMAX:-1024}" \
+  --seed "${SEED:-1234}" \
+  2>&1 | tee -a "${LOG_FILE}"

LTA_openwebtext_dualt/mini_owt_logdirichlet/.venv_qwen35_uv/lib/python3.12/site-packages/hf_xet-1.5.0.dist-info/licenses/LICENSE

@@ -0,0 +1,201 @@
+                                 Apache License
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LTA_openwebtext_dualt/mini_owt_logdirichlet/.venv_qwen35_uv/lib/python3.12/site-packages/pygments-2.20.0.dist-info/WHEEL

@@ -0,0 +1,4 @@
+Wheel-Version: 1.0
+Generator: hatchling 1.29.0
+Root-Is-Purelib: true
+Tag: py3-none-any

LTA_openwebtext_dualt/mini_owt_logdirichlet/.venv_qwen35_uv/lib/python3.12/site-packages/typer/__init__.py

@@ -0,0 +1,39 @@
+"""Typer, build great CLIs. Easy to code. Based on Python type hints."""
+
+__version__ = "0.25.1"
+
+from shutil import get_terminal_size as get_terminal_size
+
+from click.exceptions import Abort as Abort
+from click.exceptions import BadParameter as BadParameter
+from click.exceptions import Exit as Exit
+from click.termui import clear as clear
+from click.termui import confirm as confirm
+from click.termui import echo_via_pager as echo_via_pager
+from click.termui import edit as edit
+from click.termui import getchar as getchar
+from click.termui import pause as pause
+from click.termui import progressbar as progressbar
+from click.termui import prompt as prompt
+from click.termui import secho as secho
+from click.termui import style as style
+from click.termui import unstyle as unstyle
+from click.utils import echo as echo
+from click.utils import format_filename as format_filename
+from click.utils import get_app_dir as get_app_dir
+from click.utils import get_binary_stream as get_binary_stream
+from click.utils import get_text_stream as get_text_stream
+from click.utils import open_file as open_file
+
+from . import colors as colors
+from .main import Typer as Typer
+from .main import launch as launch
+from .main import run as run
+from .models import CallbackParam as CallbackParam
+from .models import Context as Context
+from .models import FileBinaryRead as FileBinaryRead
+from .models import FileBinaryWrite as FileBinaryWrite
+from .models import FileText as FileText
+from .models import FileTextWrite as FileTextWrite
+from .params import Argument as Argument
+from .params import Option as Option

LTA_openwebtext_dualt/mini_owt_logdirichlet/.venv_qwen35_uv/lib/python3.12/site-packages/typer/__main__.py

@@ -0,0 +1,3 @@
+from .cli import main
+
+main()

LTA_openwebtext_dualt/mini_owt_logdirichlet/.venv_qwen35_uv/lib/python3.12/site-packages/typer/_completion_classes.py

@@ -0,0 +1,199 @@
+import importlib.util
+import os
+import re
+import sys
+from typing import Any
+
+import click
+import click.parser
+import click.shell_completion
+from click.shell_completion import split_arg_string as click_split_arg_string
+
+from ._completion_shared import (
+    COMPLETION_SCRIPT_BASH,
+    COMPLETION_SCRIPT_FISH,
+    COMPLETION_SCRIPT_POWER_SHELL,
+    COMPLETION_SCRIPT_ZSH,
+    Shells,
+)
+
+
+def _sanitize_help_text(text: str) -> str:
+    """Sanitizes the help text by removing rich tags"""
+    if not importlib.util.find_spec("rich"):
+        return text
+    from . import rich_utils
+
+    return rich_utils.rich_render_text(text)
+
+
+class BashComplete(click.shell_completion.BashComplete):
+    name = Shells.bash.value
+    source_template = COMPLETION_SCRIPT_BASH
+
+    def source_vars(self) -> dict[str, Any]:
+        return {
+            "complete_func": self.func_name,
+            "autocomplete_var": self.complete_var,
+            "prog_name": self.prog_name,
+        }
+
+    def get_completion_args(self) -> tuple[list[str], str]:
+        cwords = click_split_arg_string(os.environ["COMP_WORDS"])
+        cword = int(os.environ["COMP_CWORD"])
+        args = cwords[1:cword]
+
+        try:
+            incomplete = cwords[cword]
+        except IndexError:
+            incomplete = ""
+
+        return args, incomplete
+
+    def format_completion(self, item: click.shell_completion.CompletionItem) -> str:
+        # TODO: Explore replicating the new behavior from Click, with item types and
+        # triggering completion for files and directories
+        # return f"{item.type},{item.value}"
+        return f"{item.value}"
+
+    def complete(self) -> str:
+        args, incomplete = self.get_completion_args()
+        completions = self.get_completions(args, incomplete)
+        out = [self.format_completion(item) for item in completions]
+        return "\n".join(out)
+
+
+class ZshComplete(click.shell_completion.ZshComplete):
+    name = Shells.zsh.value
+    source_template = COMPLETION_SCRIPT_ZSH
+
+    def source_vars(self) -> dict[str, Any]:
+        return {
+            "complete_func": self.func_name,
+            "autocomplete_var": self.complete_var,
+            "prog_name": self.prog_name,
+        }
+
+    def get_completion_args(self) -> tuple[list[str], str]:
+        completion_args = os.getenv("_TYPER_COMPLETE_ARGS", "")
+        cwords = click_split_arg_string(completion_args)
+        args = cwords[1:]
+        if args and not completion_args.endswith(" "):
+            incomplete = args[-1]
+            args = args[:-1]
+        else:
+            incomplete = ""
+        return args, incomplete
+
+    def format_completion(self, item: click.shell_completion.CompletionItem) -> str:
+        def escape(s: str) -> str:
+            return (
+                s.replace('"', '""')
+                .replace("'", "''")
+                .replace("$", "\\$")
+                .replace("`", "\\`")
+                .replace(":", r"\\:")
+            )
+
+        # TODO: Explore replicating the new behavior from Click, pay attention to
+        # the difference with and without escape
+        # return f"{item.type}\n{item.value}\n{item.help if item.help else '_'}"
+        if item.help:
+            return f'"{escape(item.value)}":"{_sanitize_help_text(escape(item.help))}"'
+        else:
+            return f'"{escape(item.value)}"'
+
+    def complete(self) -> str:
+        args, incomplete = self.get_completion_args()
+        completions = self.get_completions(args, incomplete)
+        res = [self.format_completion(item) for item in completions]
+        if res:
+            args_str = "\n".join(res)
+            return f"_arguments '*: :(({args_str}))'"
+        else:
+            return "_files"
+
+
+class FishComplete(click.shell_completion.FishComplete):
+    name = Shells.fish.value
+    source_template = COMPLETION_SCRIPT_FISH
+
+    def source_vars(self) -> dict[str, Any]:
+        return {
+            "complete_func": self.func_name,
+            "autocomplete_var": self.complete_var,
+            "prog_name": self.prog_name,
+        }
+
+    def get_completion_args(self) -> tuple[list[str], str]:
+        completion_args = os.getenv("_TYPER_COMPLETE_ARGS", "")
+        cwords = click_split_arg_string(completion_args)
+        args = cwords[1:]
+        if args and not completion_args.endswith(" "):
+            incomplete = args[-1]
+            args = args[:-1]
+        else:
+            incomplete = ""
+        return args, incomplete
+
+    def format_completion(self, item: click.shell_completion.CompletionItem) -> str:
+        # TODO: Explore replicating the new behavior from Click, pay attention to
+        # the difference with and without formatted help
+        # if item.help:
+        #     return f"{item.type},{item.value}\t{item.help}"
+
+        # return f"{item.type},{item.value}
+        if item.help:
+            formatted_help = re.sub(r"\s", " ", item.help)
+            return f"{item.value}\t{_sanitize_help_text(formatted_help)}"
+        else:
+            return f"{item.value}"
+
+    def complete(self) -> str:
+        complete_action = os.getenv("_TYPER_COMPLETE_FISH_ACTION", "")
+        args, incomplete = self.get_completion_args()
+        completions = self.get_completions(args, incomplete)
+        show_args = [self.format_completion(item) for item in completions]
+        if complete_action == "get-args":
+            if show_args:
+                return "\n".join(show_args)
+        elif complete_action == "is-args":
+            if show_args:
+                # Activate complete args (no files)
+                sys.exit(0)
+            else:
+                # Deactivate complete args (allow files)
+                sys.exit(1)
+        return ""  # pragma: no cover
+
+
+class PowerShellComplete(click.shell_completion.ShellComplete):
+    name = Shells.powershell.value
+    source_template = COMPLETION_SCRIPT_POWER_SHELL
+
+    def source_vars(self) -> dict[str, Any]:
+        return {
+            "complete_func": self.func_name,
+            "autocomplete_var": self.complete_var,
+            "prog_name": self.prog_name,
+        }
+
+    def get_completion_args(self) -> tuple[list[str], str]:
+        completion_args = os.getenv("_TYPER_COMPLETE_ARGS", "")
+        incomplete = os.getenv("_TYPER_COMPLETE_WORD_TO_COMPLETE", "")
+        cwords = click_split_arg_string(completion_args)
+        args = cwords[1:-1] if incomplete else cwords[1:]
+        return args, incomplete
+
+    def format_completion(self, item: click.shell_completion.CompletionItem) -> str:
+        return f"{item.value}:::{_sanitize_help_text(item.help) if item.help else ' '}"
+
+
+def completion_init() -> None:
+    click.shell_completion.add_completion_class(BashComplete, Shells.bash.value)
+    click.shell_completion.add_completion_class(ZshComplete, Shells.zsh.value)
+    click.shell_completion.add_completion_class(FishComplete, Shells.fish.value)
+    click.shell_completion.add_completion_class(
+        PowerShellComplete, Shells.powershell.value
+    )
+    click.shell_completion.add_completion_class(PowerShellComplete, Shells.pwsh.value)

LTA_openwebtext_dualt/mini_owt_logdirichlet/.venv_qwen35_uv/lib/python3.12/site-packages/typer/_completion_shared.py

@@ -0,0 +1,252 @@
+import os
+import re
+import subprocess
+from enum import Enum
+from pathlib import Path
+
+import click
+import shellingham
+
+
+class Shells(str, Enum):
+    bash = "bash"
+    zsh = "zsh"
+    fish = "fish"
+    powershell = "powershell"
+    pwsh = "pwsh"
+
+
+COMPLETION_SCRIPT_BASH = """
+%(complete_func)s() {
+    local IFS=$'\n'
+    COMPREPLY=( $( env COMP_WORDS="${COMP_WORDS[*]}" \\
+                   COMP_CWORD=$COMP_CWORD \\
+                   %(autocomplete_var)s=complete_bash $1 ) )
+    return 0
+}
+
+complete -o default -F %(complete_func)s %(prog_name)s
+"""
+
+COMPLETION_SCRIPT_ZSH = """
+#compdef %(prog_name)s
+
+%(complete_func)s() {
+  eval $(env _TYPER_COMPLETE_ARGS="${words[1,$CURRENT]}" %(autocomplete_var)s=complete_zsh %(prog_name)s)
+}
+
+compdef %(complete_func)s %(prog_name)s
+"""
+
+COMPLETION_SCRIPT_FISH = 'complete --command %(prog_name)s --no-files --arguments "(env %(autocomplete_var)s=complete_fish _TYPER_COMPLETE_FISH_ACTION=get-args _TYPER_COMPLETE_ARGS=(commandline -cp) %(prog_name)s)" --condition "env %(autocomplete_var)s=complete_fish _TYPER_COMPLETE_FISH_ACTION=is-args _TYPER_COMPLETE_ARGS=(commandline -cp) %(prog_name)s"'
+
+COMPLETION_SCRIPT_POWER_SHELL = """
+Import-Module PSReadLine
+Set-PSReadLineKeyHandler -Chord Tab -Function MenuComplete
+$scriptblock = {
+    param($wordToComplete, $commandAst, $cursorPosition)
+    $Env:%(autocomplete_var)s = "complete_powershell"
+    $Env:_TYPER_COMPLETE_ARGS = $commandAst.ToString()
+    $Env:_TYPER_COMPLETE_WORD_TO_COMPLETE = $wordToComplete
+    %(prog_name)s | ForEach-Object {
+        $commandArray = $_ -Split ":::"
+        $command = $commandArray[0]
+        $helpString = $commandArray[1]
+        [System.Management.Automation.CompletionResult]::new(
+            $command, $command, 'ParameterValue', $helpString)
+    }
+    $Env:%(autocomplete_var)s = ""
+    $Env:_TYPER_COMPLETE_ARGS = ""
+    $Env:_TYPER_COMPLETE_WORD_TO_COMPLETE = ""
+}
+Register-ArgumentCompleter -Native -CommandName %(prog_name)s -ScriptBlock $scriptblock
+"""
+
+_completion_scripts = {
+    "bash": COMPLETION_SCRIPT_BASH,
+    "zsh": COMPLETION_SCRIPT_ZSH,
+    "fish": COMPLETION_SCRIPT_FISH,
+    "powershell": COMPLETION_SCRIPT_POWER_SHELL,
+    "pwsh": COMPLETION_SCRIPT_POWER_SHELL,
+}
+
+# TODO: Probably refactor this, copied from Click 7.x
+_invalid_ident_char_re = re.compile(r"[^a-zA-Z0-9_]")
+
+
+def get_completion_script(*, prog_name: str, complete_var: str, shell: str) -> str:
+    cf_name = _invalid_ident_char_re.sub("", prog_name.replace("-", "_"))
+    script = _completion_scripts.get(shell)
+    if script is None:
+        click.echo(f"Shell {shell} not supported.", err=True)
+        raise click.exceptions.Exit(1)
+    return (
+        script
+        % {
+            "complete_func": f"_{cf_name}_completion",
+            "prog_name": prog_name,
+            "autocomplete_var": complete_var,
+        }
+    ).strip()
+
+
+def install_bash(*, prog_name: str, complete_var: str, shell: str) -> Path:
+    # Ref: https://github.com/scop/bash-completion#faq
+    # It seems bash-completion is the official completion system for bash:
+    # Ref: https://www.gnu.org/software/bash/manual/html_node/A-Programmable-Completion-Example.html
+    # But installing in the locations from the docs doesn't seem to have effect
+    completion_path = Path.home() / ".bash_completions" / f"{prog_name}.sh"
+    rc_path = Path.home() / ".bashrc"
+    rc_path.parent.mkdir(parents=True, exist_ok=True)
+    rc_content = ""
+    if rc_path.is_file():
+        rc_content = rc_path.read_text()
+    completion_init_lines = [f"source '{completion_path}'"]
+    for line in completion_init_lines:
+        if line not in rc_content:  # pragma: no cover
+            rc_content += f"\n{line}"
+    rc_content += "\n"
+    rc_path.write_text(rc_content)
+    # Install completion
+    completion_path.parent.mkdir(parents=True, exist_ok=True)
+    script_content = get_completion_script(
+        prog_name=prog_name, complete_var=complete_var, shell=shell
+    )
+    completion_path.write_text(script_content)
+    return completion_path
+
+
+def install_zsh(*, prog_name: str, complete_var: str, shell: str) -> Path:
+    # Setup Zsh and load ~/.zfunc
+    zshrc_path = Path.home() / ".zshrc"
+    zshrc_path.parent.mkdir(parents=True, exist_ok=True)
+    zshrc_content = ""
+    if zshrc_path.is_file():
+        zshrc_content = zshrc_path.read_text()
+    completion_line = "fpath+=~/.zfunc; autoload -Uz compinit; compinit"
+    if completion_line not in zshrc_content:
+        zshrc_content += f"\n{completion_line}\n"
+    style_line = "zstyle ':completion:*' menu select"
+    # TODO: consider setting the style only for the current program
+    # style_line = f"zstyle ':completion:*:*:{prog_name}:*' menu select"
+    # Install zstyle completion config only if the user doesn't have a customization
+    if "zstyle" not in zshrc_content:
+        zshrc_content += f"\n{style_line}\n"
+    zshrc_content = f"{zshrc_content.strip()}\n"
+    zshrc_path.write_text(zshrc_content)
+    # Install completion under ~/.zfunc/
+    path_obj = Path.home() / f".zfunc/_{prog_name}"
+    path_obj.parent.mkdir(parents=True, exist_ok=True)
+    script_content = get_completion_script(
+        prog_name=prog_name, complete_var=complete_var, shell=shell
+    )
+    path_obj.write_text(script_content)
+    return path_obj
+
+
+def install_fish(*, prog_name: str, complete_var: str, shell: str) -> Path:
+    path_obj = Path.home() / f".config/fish/completions/{prog_name}.fish"
+    parent_dir: Path = path_obj.parent
+    parent_dir.mkdir(parents=True, exist_ok=True)
+    script_content = get_completion_script(
+        prog_name=prog_name, complete_var=complete_var, shell=shell
+    )
+    path_obj.write_text(f"{script_content}\n")
+    return path_obj
+
+
+def install_powershell(*, prog_name: str, complete_var: str, shell: str) -> Path:
+    subprocess.run(
+        [
+            shell,
+            "-Command",
+            "Set-ExecutionPolicy",
+            "Unrestricted",
+            "-Scope",
+            "CurrentUser",
+        ]
+    )
+    result = subprocess.run(
+        [shell, "-NoProfile", "-Command", "echo", "$profile"],
+        check=True,
+        stdout=subprocess.PIPE,
+    )
+    if result.returncode != 0:  # pragma: no cover
+        click.echo("Couldn't get PowerShell user profile", err=True)
+        raise click.exceptions.Exit(result.returncode)
+    path_str = ""
+    if isinstance(result.stdout, str):  # pragma: no cover
+        path_str = result.stdout
+    if isinstance(result.stdout, bytes):
+        for encoding in ["windows-1252", "utf8", "cp850"]:
+            try:
+                path_str = result.stdout.decode(encoding)
+                break
+            except UnicodeDecodeError:  # pragma: no cover
+                pass
+        if not path_str:  # pragma: no cover
+            click.echo("Couldn't decode the path automatically", err=True)
+            raise click.exceptions.Exit(1)
+    path_obj = Path(path_str.strip())
+    parent_dir: Path = path_obj.parent
+    parent_dir.mkdir(parents=True, exist_ok=True)
+    script_content = get_completion_script(
+        prog_name=prog_name, complete_var=complete_var, shell=shell
+    )
+    with path_obj.open(mode="a") as f:
+        f.write(f"{script_content}\n")
+    return path_obj
+
+
+def install(
+    shell: str | None = None,
+    prog_name: str | None = None,
+    complete_var: str | None = None,
+) -> tuple[str, Path]:
+    prog_name = prog_name or click.get_current_context().find_root().info_name
+    assert prog_name
+    if complete_var is None:
+        complete_var = "_{}_COMPLETE".format(prog_name.replace("-", "_").upper())
+    test_disable_detection = os.getenv("_TYPER_COMPLETE_TEST_DISABLE_SHELL_DETECTION")
+    if shell is None and not test_disable_detection:
+        shell = _get_shell_name()
+    if shell == "bash":
+        installed_path = install_bash(
+            prog_name=prog_name, complete_var=complete_var, shell=shell
+        )
+        return shell, installed_path
+    elif shell == "zsh":
+        installed_path = install_zsh(
+            prog_name=prog_name, complete_var=complete_var, shell=shell
+        )
+        return shell, installed_path
+    elif shell == "fish":
+        installed_path = install_fish(
+            prog_name=prog_name, complete_var=complete_var, shell=shell
+        )
+        return shell, installed_path
+    elif shell in {"powershell", "pwsh"}:
+        installed_path = install_powershell(
+            prog_name=prog_name, complete_var=complete_var, shell=shell
+        )
+        return shell, installed_path
+    else:
+        click.echo(f"Shell {shell} is not supported.")
+        raise click.exceptions.Exit(1)
+
+
+def _get_shell_name() -> str | None:
+    """Get the current shell name, if available.
+
+    The name will always be lowercase. If the shell cannot be detected, None is
+    returned.
+    """
+    name: str | None  # N.B. shellingham is untyped
+    try:
+        # N.B. detect_shell returns a tuple of (shell name, shell command).
+        # We only need the name.
+        name, _cmd = shellingham.detect_shell()  # noqa: TID251
+    except shellingham.ShellDetectionFailure:  # pragma: no cover
+        name = None
+
+    return name

LTA_openwebtext_dualt/mini_owt_logdirichlet/.venv_qwen35_uv/lib/python3.12/site-packages/typer/_types.py

@@ -0,0 +1,27 @@
+from enum import Enum
+from typing import TypeVar
+
+import click
+
+ParamTypeValue = TypeVar("ParamTypeValue")
+
+
+class TyperChoice(click.Choice[ParamTypeValue]):
+    def normalize_choice(
+        self, choice: ParamTypeValue, ctx: click.Context | None
+    ) -> str:
+        # Click 8.2.0 added a new method `normalize_choice` to the `Choice` class
+        # to support enums, but it uses the enum names, while Typer has always used the
+        # enum values.
+        # This class overrides that method to maintain the previous behavior.
+        # In Click:
+        # normed_value = choice.name if isinstance(choice, Enum) else str(choice)
+        normed_value = str(choice.value) if isinstance(choice, Enum) else str(choice)
+
+        if ctx is not None and ctx.token_normalize_func is not None:
+            normed_value = ctx.token_normalize_func(normed_value)
+
+        if not self.case_sensitive:
+            normed_value = normed_value.casefold()
+
+        return normed_value

LTA_openwebtext_dualt/mini_owt_logdirichlet/.venv_qwen35_uv/lib/python3.12/site-packages/typer/_typing.py

@@ -0,0 +1,73 @@
+# Copied from pydantic 1.9.2 (the latest version to support python 3.6.)
+# https://github.com/pydantic/pydantic/blob/v1.9.2/pydantic/typing.py
+# Reduced drastically to only include Typer-specific 3.9+ functionality
+# mypy: ignore-errors
+
+import types
+from collections.abc import Callable
+from typing import (
+    Annotated,
+    Any,
+    Literal,
+    Union,
+    get_args,
+    get_origin,
+    get_type_hints,
+)
+
+
+def is_union(tp: type[Any] | None) -> bool:
+    return tp is Union or tp is types.UnionType  # noqa: E721
+
+
+__all__ = (
+    "NoneType",
+    "is_none_type",
+    "is_callable_type",
+    "is_literal_type",
+    "all_literal_values",
+    "is_union",
+    "Annotated",
+    "Literal",
+    "get_args",
+    "get_origin",
+    "get_type_hints",
+)
+
+
+NoneType = None.__class__
+
+
+NONE_TYPES: tuple[Any, Any, Any] = (None, NoneType, Literal[None])
+
+
+def is_none_type(type_: Any) -> bool:
+    for none_type in NONE_TYPES:
+        if type_ is none_type:
+            return True
+    return False
+
+
+def is_callable_type(type_: type[Any]) -> bool:
+    return type_ is Callable or get_origin(type_) is Callable
+
+
+def is_literal_type(type_: type[Any]) -> bool:
+    return get_origin(type_) is Literal
+
+
+def literal_values(type_: type[Any]) -> tuple[Any, ...]:
+    return get_args(type_)
+
+
+def all_literal_values(type_: type[Any]) -> tuple[Any, ...]:
+    """
+    This method is used to retrieve all Literal values as
+    Literal can be used recursively (see https://www.python.org/dev/peps/pep-0586)
+    e.g. `Literal[Literal[Literal[1, 2, 3], "foo"], 5, None]`
+    """
+    if not is_literal_type(type_):
+        return (type_,)
+
+    values = literal_values(type_)
+    return tuple(x for value in values for x in all_literal_values(value))

LTA_openwebtext_dualt/mini_owt_logdirichlet/.venv_qwen35_uv/lib/python3.12/site-packages/typer/cli.py

@@ -0,0 +1,318 @@
+import importlib.util
+import re
+import sys
+from pathlib import Path
+from typing import Any
+
+import click
+import typer
+import typer.core
+from click import Command, Group, Option
+
+from . import __version__
+from .core import HAS_RICH, MARKUP_MODE_KEY
+
+default_app_names = ("app", "cli", "main")
+default_func_names = ("main", "cli", "app")
+
+app = typer.Typer()
+utils_app = typer.Typer(help="Extra utility commands for Typer apps.")
+app.add_typer(utils_app, name="utils")
+
+
+class State:
+    def __init__(self) -> None:
+        self.app: str | None = None
+        self.func: str | None = None
+        self.file: Path | None = None
+        self.module: str | None = None
+
+
+state = State()
+
+
+def maybe_update_state(ctx: click.Context) -> None:
+    path_or_module = ctx.params.get("path_or_module")
+    if path_or_module:
+        file_path = Path(path_or_module)
+        if file_path.exists() and file_path.is_file():
+            state.file = file_path
+        else:
+            if not re.fullmatch(r"[a-zA-Z_]\w*(\.[a-zA-Z_]\w*)*", path_or_module):
+                typer.echo(
+                    f"Not a valid file or Python module: {path_or_module}", err=True
+                )
+                sys.exit(1)
+            state.module = path_or_module
+    app_name = ctx.params.get("app")
+    if app_name:
+        state.app = app_name
+    func_name = ctx.params.get("func")
+    if func_name:
+        state.func = func_name
+
+
+class TyperCLIGroup(typer.core.TyperGroup):
+    def list_commands(self, ctx: click.Context) -> list[str]:
+        self.maybe_add_run(ctx)
+        return super().list_commands(ctx)
+
+    def get_command(self, ctx: click.Context, name: str) -> Command | None:  # ty: ignore[invalid-method-override]
+        self.maybe_add_run(ctx)
+        return super().get_command(ctx, name)
+
+    def invoke(self, ctx: click.Context) -> Any:
+        self.maybe_add_run(ctx)
+        return super().invoke(ctx)
+
+    def maybe_add_run(self, ctx: click.Context) -> None:
+        maybe_update_state(ctx)
+        maybe_add_run_to_cli(self)
+
+
+def get_typer_from_module(module: Any) -> typer.Typer | None:
+    # Try to get defined app
+    if state.app:
+        obj = getattr(module, state.app, None)
+        if not isinstance(obj, typer.Typer):
+            typer.echo(f"Not a Typer object: --app {state.app}", err=True)
+            sys.exit(1)
+        return obj
+    # Try to get defined function
+    if state.func:
+        func_obj = getattr(module, state.func, None)
+        if not callable(func_obj):
+            typer.echo(f"Not a function: --func {state.func}", err=True)
+            raise typer.Exit(1)
+        sub_app = typer.Typer()
+        sub_app.command()(func_obj)
+        return sub_app
+    # Iterate and get a default object to use as CLI
+    local_names = dir(module)
+    local_names_set = set(local_names)
+    # Try to get a default Typer app
+    for name in default_app_names:
+        if name in local_names_set:
+            obj = getattr(module, name, None)
+            if isinstance(obj, typer.Typer):
+                return obj
+    # Try to get any Typer app
+    for name in local_names_set - set(default_app_names):
+        obj = getattr(module, name)
+        if isinstance(obj, typer.Typer):
+            return obj
+    # Try to get a default function
+    for func_name in default_func_names:
+        func_obj = getattr(module, func_name, None)
+        if callable(func_obj):
+            sub_app = typer.Typer()
+            sub_app.command()(func_obj)
+            return sub_app
+    # Try to get any func app
+    for func_name in local_names_set - set(default_func_names):
+        func_obj = getattr(module, func_name)
+        if callable(func_obj):
+            sub_app = typer.Typer()
+            sub_app.command()(func_obj)
+            return sub_app
+    return None
+
+
+def get_typer_from_state() -> typer.Typer | None:
+    spec = None
+    if state.file:
+        module_name = state.file.name
+        spec = importlib.util.spec_from_file_location(module_name, str(state.file))
+    elif state.module:
+        spec = importlib.util.find_spec(state.module)
+    if spec is None:
+        if state.file:
+            typer.echo(f"Could not import as Python file: {state.file}", err=True)
+        else:
+            typer.echo(f"Could not import as Python module: {state.module}", err=True)
+        sys.exit(1)
+    assert spec is not None
+    module = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(module)  # type: ignore
+    obj = get_typer_from_module(module)
+    return obj
+
+
+def maybe_add_run_to_cli(cli: click.Group) -> None:
+    if "run" not in cli.commands:
+        if state.file or state.module:
+            obj = get_typer_from_state()
+            if obj:
+                obj._add_completion = False
+                click_obj = typer.main.get_command(obj)
+                click_obj.name = "run"
+                if not click_obj.help:
+                    click_obj.help = "Run the provided Typer app."
+                cli.add_command(click_obj)
+
+
+def print_version(ctx: click.Context, param: Option, value: bool) -> None:
+    if not value or ctx.resilient_parsing:
+        return
+    typer.echo(f"Typer version: {__version__}")
+    raise typer.Exit()
+
+
+@app.callback(cls=TyperCLIGroup, no_args_is_help=True)
+def callback(
+    ctx: typer.Context,
+    *,
+    path_or_module: str = typer.Argument(None),
+    app: str = typer.Option(None, help="The typer app object/variable to use."),
+    func: str = typer.Option(None, help="The function to convert to Typer."),
+    version: bool = typer.Option(
+        False,
+        "--version",
+        help="Print version and exit.",
+        callback=print_version,
+    ),
+) -> None:
+    """
+    Run Typer scripts with completion, without having to create a package.
+
+    You probably want to install completion for the typer command:
+
+    $ typer --install-completion
+
+    https://typer.tiangolo.com/
+    """
+    maybe_update_state(ctx)
+
+
+def get_docs_for_click(
+    *,
+    obj: Command,
+    ctx: typer.Context,
+    indent: int = 0,
+    name: str = "",
+    call_prefix: str = "",
+    title: str | None = None,
+) -> str:
+    docs = "#" * (1 + indent)
+    command_name = name or obj.name
+    if call_prefix:
+        command_name = f"{call_prefix} {command_name}"
+    if not title:
+        title = f"`{command_name}`" if command_name else "CLI"
+    docs += f" {title}\n\n"
+    rich_markup_mode = None
+    if hasattr(ctx, "obj") and isinstance(ctx.obj, dict):
+        rich_markup_mode = ctx.obj.get(MARKUP_MODE_KEY, None)
+    to_parse: bool = bool(HAS_RICH and (rich_markup_mode == "rich"))
+    if obj.help:
+        docs += f"{_parse_html(to_parse, obj.help)}\n\n"
+    usage_pieces = obj.collect_usage_pieces(ctx)
+    if usage_pieces:
+        docs += "**Usage**:\n\n"
+        docs += "```console\n"
+        docs += "$ "
+        if command_name:
+            docs += f"{command_name} "
+        docs += f"{' '.join(usage_pieces)}\n"
+        docs += "```\n\n"
+    args = []
+    opts = []
+    for param in obj.get_params(ctx):
+        rv = param.get_help_record(ctx)
+        if rv is not None:
+            if param.param_type_name == "argument":
+                args.append(rv)
+            elif param.param_type_name == "option":
+                opts.append(rv)
+    if args:
+        docs += "**Arguments**:\n\n"
+        for arg_name, arg_help in args:
+            docs += f"* `{arg_name}`"
+            if arg_help:
+                docs += f": {_parse_html(to_parse, arg_help)}"
+            docs += "\n"
+        docs += "\n"
+    if opts:
+        docs += "**Options**:\n\n"
+        for opt_name, opt_help in opts:
+            docs += f"* `{opt_name}`"
+            if opt_help:
+                docs += f": {_parse_html(to_parse, opt_help)}"
+            docs += "\n"
+        docs += "\n"
+    if obj.epilog:
+        docs += f"{obj.epilog}\n\n"
+    if isinstance(obj, Group):
+        group = obj
+        commands = group.list_commands(ctx)
+        if commands:
+            docs += "**Commands**:\n\n"
+            for command in commands:
+                command_obj = group.get_command(ctx, command)
+                assert command_obj
+                docs += f"* `{command_obj.name}`"
+                command_help = command_obj.get_short_help_str()
+                if command_help:
+                    docs += f": {_parse_html(to_parse, command_help)}"
+                docs += "\n"
+            docs += "\n"
+        for command in commands:
+            command_obj = group.get_command(ctx, command)
+            assert command_obj
+            use_prefix = ""
+            if command_name:
+                use_prefix += f"{command_name}"
+            docs += get_docs_for_click(
+                obj=command_obj, ctx=ctx, indent=indent + 1, call_prefix=use_prefix
+            )
+    return docs
+
+
+def _parse_html(to_parse: bool, input_text: str) -> str:
+    if not to_parse:
+        return input_text
+    from . import rich_utils
+
+    return rich_utils.rich_to_html(input_text)
+
+
+@utils_app.command()
+def docs(
+    ctx: typer.Context,
+    name: str = typer.Option("", help="The name of the CLI program to use in docs."),
+    output: Path | None = typer.Option(
+        None,
+        help="An output file to write docs to, like README.md.",
+        file_okay=True,
+        dir_okay=False,
+    ),
+    title: str | None = typer.Option(
+        None,
+        help="The title for the documentation page. If not provided, the name of "
+        "the program is used.",
+    ),
+) -> None:
+    """
+    Generate Markdown docs for a Typer app.
+    """
+    typer_obj = get_typer_from_state()
+    if not typer_obj:
+        typer.echo("No Typer app found", err=True)
+        raise typer.Abort()
+    if hasattr(typer_obj, "rich_markup_mode"):
+        if not hasattr(ctx, "obj") or ctx.obj is None:
+            ctx.ensure_object(dict)
+        if isinstance(ctx.obj, dict):
+            ctx.obj[MARKUP_MODE_KEY] = typer_obj.rich_markup_mode
+    click_obj = typer.main.get_command(typer_obj)
+    docs = get_docs_for_click(obj=click_obj, ctx=ctx, name=name, title=title)
+    clean_docs = f"{docs.strip()}\n"
+    if output:
+        output.write_text(clean_docs)
+        typer.echo(f"Docs saved to: {output}")
+    else:
+        typer.echo(clean_docs)
+
+
+def main() -> Any:
+    return app()