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.gitattributes

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Baseline/checkpoints/050000/pretrained_model/action_space_manifest.json

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Baseline/checkpoints/050000/pretrained_model/config.json

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Baseline/checkpoints/050000/pretrained_model/policy_postprocessor.json

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Baseline/checkpoints/050000/pretrained_model/prompt_manifest.json

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+    "_step_count": 60001,
+    "_is_initial": false,
+    "_get_lr_called_within_step": false,
+    "_last_lr": [
+        0.0,
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+    "lr_lambdas": [
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+}

Baseline/checkpoints/060000/training_state/training_step.json

@@ -0,0 +1,5 @@
+{
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+    "num_processes": 1,
+    "batch_size": 64
+}

RKD_TimewarpVAE/LAPstyle_linear_10K/wandb/run-20260715_011448-fbqzmp5m/files/output.log

@@ -0,0 +1,267 @@
+INFO 2026-07-15 01:14:49 db_utils.py:121 Logs will be synced with wandb.
+INFO 2026-07-15 01:14:49 db_utils.py:122 Track this run --> https://wandb.ai/minje227_hyu-hanyang-university/lerobot/runs/fbqzmp5m
+INFO 2026-07-15 01:14:49 ot_train.py:298 Creating dataset
+INFO 2026-07-15 01:14:51 ot_train.py:332 Creating policy
+Fetching 27 files: 100%|██████████| 27/27 [00:00<00:00, 1703.16it/s]
+`torch_dtype` is deprecated! Use `dtype` instead!
+Loading weights: 100%|██████████| 1031/1031 [00:00<00:00, 1695.76it/s]
+INFO 2026-07-15 01:15:00 ot_train.py:405 Creating optimizer and scheduler
+INFO 2026-07-15 01:15:00 ot_train.py:439 Output dir: /home/ext_minje/groot_insight/Abs_6D/RKD_TimewarpVAE/LAPstyle_linear_10K
+INFO 2026-07-15 01:15:00 ot_train.py:446 cfg.steps=60000 (60K)
+INFO 2026-07-15 01:15:00 ot_train.py:447 dataset.num_frames=218367 (218K)
+INFO 2026-07-15 01:15:00 ot_train.py:448 dataset.num_episodes=4930
+INFO 2026-07-15 01:15:00 ot_train.py:451 Effective batch size: 64 x 1 = 64
+INFO 2026-07-15 01:15:00 ot_train.py:452 num_learnable_params=1622090881 (2B)
+INFO 2026-07-15 01:15:00 ot_train.py:453 num_total_params=3145709729 (3B)
+Training:   0%|          | 0/60000 [00:00<?, ?step/s]INFO 2026-07-15 01:15:00 ot_train.py:604 Start offline training on a fixed dataset, with effective batch size: 64
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+Training:   1%|          | 600/60000 [14:03<21:54:08,  1.33s/step]INFO 2026-07-15 01:29:04 ot_train.py:649 step:600 smpl:38K ep:867 epch:0.18 loss:0.417 grdn:2.294 lr:8.3e-06 updt_s:0.831 data_s:0.560 smp/s:46 mem_gb:39.70
+Training:   1%|▏         | 800/60000 [18:40<22:33:42,  1.37s/step]INFO 2026-07-15 01:33:41 ot_train.py:649 step:800 smpl:51K ep:1K epch:0.23 loss:0.283 grdn:2.505 lr:1.2e-05 updt_s:0.810 data_s:0.570 smp/s:46 mem_gb:39.70
+Training:   2%|▏         | 1000/60000 [23:17<23:12:05,  1.42s/step]INFO 2026-07-15 01:38:18 ot_train.py:649 step:1K smpl:64K ep:1K epch:0.29 loss:0.238 grdn:2.514 lr:1.5e-05 updt_s:0.808 data_s:0.574 smp/s:46 mem_gb:39.70
+Training:   2%|▏         | 1200/60000 [27:52<22:06:51,  1.35s/step]INFO 2026-07-15 01:42:53 ot_train.py:649 step:1K smpl:77K ep:2K epch:0.35 loss:0.205 grdn:2.267 lr:1.8e-05 updt_s:0.789 data_s:0.583 smp/s:47 mem_gb:39.70
+Training:   2%|▏         | 1400/60000 [32:31<22:44:03,  1.40s/step]INFO 2026-07-15 01:47:32 ot_train.py:649 step:1K smpl:90K ep:2K epch:0.41 loss:0.188 grdn:1.980 lr:2.2e-05 updt_s:0.808 data_s:0.581 smp/s:46 mem_gb:39.70
+Training:   3%|▎         | 1600/60000 [37:09<23:29:17,  1.45s/step]INFO 2026-07-15 01:52:10 ot_train.py:649 step:2K smpl:102K ep:2K epch:0.47 loss:0.174 grdn:1.755 lr:2.5e-05 updt_s:0.805 data_s:0.582 smp/s:46 mem_gb:39.70
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+Training:   4%|▎         | 2200/60000 [52:02<24:11:58,  1.51s/step]INFO 2026-07-15 02:07:02 ot_train.py:649 step:2K smpl:141K ep:3K epch:0.64 loss:0.151 grdn:1.233 lr:3.5e-05 updt_s:0.947 data_s:0.580 smp/s:42 mem_gb:39.70
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+Training:   7%|▋         | 4000/60000 [1:42:22<26:43:32,  1.72s/step]INFO 2026-07-15 02:57:23 ot_train.py:649 step:4K smpl:256K ep:6K epch:1.17 loss:0.112 grdn:0.599 lr:6.5e-05 updt_s:0.919 data_s:0.777 smp/s:38 mem_gb:39.71
+Training:   7%|▋         | 4200/60000 [1:48:05<25:39:27,  1.66s/step]INFO 2026-07-15 03:03:06 ot_train.py:649 step:4K smpl:269K ep:6K epch:1.23 loss:0.112 grdn:0.557 lr:6.8e-05 updt_s:0.921 data_s:0.788 smp/s:37 mem_gb:39.71
+Training:   7%|▋         | 4400/60000 [1:53:43<25:00:58,  1.62s/step]INFO 2026-07-15 03:08:43 ot_train.py:649 step:4K smpl:282K ep:6K epch:1.29 loss:0.112 grdn:0.535 lr:7.2e-05 updt_s:0.908 data_s:0.775 smp/s:38 mem_gb:39.71
+Training:   8%|▊         | 4600/60000 [1:59:17<25:43:05,  1.67s/step]INFO 2026-07-15 03:14:18 ot_train.py:649 step:5K smpl:294K ep:7K epch:1.35 loss:0.108 grdn:0.507 lr:7.5e-05 updt_s:0.884 data_s:0.783 smp/s:38 mem_gb:39.70
+Training:   8%|▊         | 4800/60000 [2:04:50<24:08:01,  1.57s/step]INFO 2026-07-15 03:19:50 ot_train.py:649 step:5K smpl:307K ep:7K epch:1.41 loss:0.108 grdn:0.518 lr:7.8e-05 updt_s:0.892 data_s:0.766 smp/s:39 mem_gb:39.71
+Training:   8%|▊         | 5000/60000 [2:10:27<25:24:36,  1.66s/step]INFO 2026-07-15 03:25:28 ot_train.py:649 step:5K smpl:320K ep:7K epch:1.47 loss:0.107 grdn:0.475 lr:8.2e-05 updt_s:0.903 data_s:0.779 smp/s:38 mem_gb:39.71
+Training:   9%|▊         | 5200/60000 [2:16:05<25:30:32,  1.68s/step]INFO 2026-07-15 03:31:05 ot_train.py:649 step:5K smpl:333K ep:8K epch:1.52 loss:0.105 grdn:0.467 lr:8.5e-05 updt_s:0.894 data_s:0.790 smp/s:38 mem_gb:39.71
+Training:   9%|▉         | 5400/60000 [2:21:40<25:39:48,  1.69s/step]INFO 2026-07-15 03:36:41 ot_train.py:649 step:5K smpl:346K ep:8K epch:1.58 loss:0.103 grdn:0.440 lr:8.8e-05 updt_s:0.899 data_s:0.775 smp/s:38 mem_gb:39.71
+Training:   9%|▉         | 5600/60000 [2:27:17<25:00:27,  1.65s/step]INFO 2026-07-15 03:42:18 ot_train.py:649 step:6K smpl:358K ep:8K epch:1.64 loss:0.103 grdn:0.451 lr:9.2e-05 updt_s:0.891 data_s:0.785 smp/s:38 mem_gb:39.71
+Training:  10%|▉         | 5800/60000 [2:32:52<24:47:20,  1.65s/step]INFO 2026-07-15 03:47:53 ot_train.py:649 step:6K smpl:371K ep:8K epch:1.70 loss:0.100 grdn:0.423 lr:9.5e-05 updt_s:0.880 data_s:0.793 smp/s:38 mem_gb:39.71
+Training:  10%|█         | 6000/60000 [2:38:29<25:15:32,  1.68s/step]INFO 2026-07-15 03:53:29 ot_train.py:649 step:6K smpl:384K ep:9K epch:1.76 loss:0.100 grdn:0.416 lr:9.8e-05 updt_s:0.892 data_s:0.784 smp/s:38 mem_gb:39.71
+Training:  10%|█         | 6200/60000 [2:44:05<24:04:48,  1.61s/step]INFO 2026-07-15 03:59:06 ot_train.py:649 step:6K smpl:397K ep:9K epch:1.82 loss:0.103 grdn:0.426 lr:1.0e-04 updt_s:0.903 data_s:0.774 smp/s:38 mem_gb:39.71
+Training:  11%|█         | 6400/60000 [2:49:41<24:39:44,  1.66s/step]INFO 2026-07-15 04:04:42 ot_train.py:649 step:6K smpl:410K ep:9K epch:1.88 loss:0.100 grdn:0.415 lr:1.0e-04 updt_s:0.886 data_s:0.792 smp/s:38 mem_gb:39.71
+Training:  11%|█         | 6600/60000 [2:55:21<24:35:43,  1.66s/step]INFO 2026-07-15 04:10:22 ot_train.py:649 step:7K smpl:422K ep:10K epch:1.93 loss:0.094 grdn:0.370 lr:1.0e-04 updt_s:0.907 data_s:0.787 smp/s:38 mem_gb:39.71
+Training:  11%|█▏        | 6800/60000 [3:00:56<24:03:01,  1.63s/step]INFO 2026-07-15 04:15:57 ot_train.py:649 step:7K smpl:435K ep:10K epch:1.99 loss:0.094 grdn:0.392 lr:1.0e-04 updt_s:0.879 data_s:0.792 smp/s:38 mem_gb:39.69
+Training:  12%|█▏        | 7000/60000 [3:06:35<23:50:15,  1.62s/step]INFO 2026-07-15 04:21:36 ot_train.py:649 step:7K smpl:448K ep:10K epch:2.05 loss:0.094 grdn:0.361 lr:1.0e-04 updt_s:0.900 data_s:0.789 smp/s:38 mem_gb:39.71
+Training:  12%|█▏        | 7200/60000 [3:12:10<24:41:31,  1.68s/step]INFO 2026-07-15 04:27:11 ot_train.py:649 step:7K smpl:461K ep:10K epch:2.11 loss:0.091 grdn:0.363 lr:1.0e-04 updt_s:0.892 data_s:0.778 smp/s:38 mem_gb:39.71
+Training:  12%|█▏        | 7400/60000 [3:17:47<24:20:25,  1.67s/step]INFO 2026-07-15 04:32:48 ot_train.py:649 step:7K smpl:474K ep:11K epch:2.17 loss:0.089 grdn:0.360 lr:1.0e-04 updt_s:0.888 data_s:0.792 smp/s:38 mem_gb:39.71
+Training:  13%|█▎        | 7600/60000 [3:23:23<25:21:16,  1.74s/step]INFO 2026-07-15 04:38:24 ot_train.py:649 step:8K smpl:486K ep:11K epch:2.23 loss:0.095 grdn:0.381 lr:1.0e-04 updt_s:0.887 data_s:0.788 smp/s:38 mem_gb:39.71
+Training:  13%|█▎        | 7800/60000 [3:28:58<24:14:32,  1.67s/step]INFO 2026-07-15 04:43:59 ot_train.py:649 step:8K smpl:499K ep:11K epch:2.29 loss:0.089 grdn:0.339 lr:1.0e-04 updt_s:0.904 data_s:0.767 smp/s:38 mem_gb:39.71
+Training:  13%|█▎        | 8000/60000 [3:34:34<24:28:28,  1.69s/step]INFO 2026-07-15 04:49:35 ot_train.py:649 step:8K smpl:512K ep:12K epch:2.34 loss:0.091 grdn:0.368 lr:1.0e-04 updt_s:0.891 data_s:0.784 smp/s:38 mem_gb:39.71
+Training:  14%|█▎        | 8200/60000 [3:40:11<24:04:31,  1.67s/step]INFO 2026-07-15 04:55:12 ot_train.py:649 step:8K smpl:525K ep:12K epch:2.40 loss:0.087 grdn:0.354 lr:1.0e-04 updt_s:0.894 data_s:0.785 smp/s:38 mem_gb:39.71
+Training:  14%|█▍        | 8400/60000 [3:45:55<24:06:25,  1.68s/step]INFO 2026-07-15 05:00:56 ot_train.py:649 step:8K smpl:538K ep:12K epch:2.46 loss:0.087 grdn:0.355 lr:1.0e-04 updt_s:0.940 data_s:0.775 smp/s:37 mem_gb:39.71
+Training:  14%|█▍        | 8600/60000 [3:51:35<24:12:43,  1.70s/step]INFO 2026-07-15 05:06:36 ot_train.py:649 step:9K smpl:550K ep:12K epch:2.52 loss:0.088 grdn:0.375 lr:9.9e-05 updt_s:0.902 data_s:0.793 smp/s:38 mem_gb:39.71
+Training:  15%|█▍        | 8800/60000 [3:57:11<22:46:13,  1.60s/step]INFO 2026-07-15 05:12:12 ot_train.py:649 step:9K smpl:563K ep:13K epch:2.58 loss:0.086 grdn:0.340 lr:9.9e-05 updt_s:0.894 data_s:0.782 smp/s:38 mem_gb:39.71
+Training:  15%|█▌        | 9000/60000 [4:02:44<23:21:13,  1.65s/step]INFO 2026-07-15 05:17:45 ot_train.py:649 step:9K smpl:576K ep:13K epch:2.64 loss:0.081 grdn:0.316 lr:9.9e-05 updt_s:0.886 data_s:0.775 smp/s:39 mem_gb:39.71
+Training:  15%|█▌        | 9200/60000 [4:08:21<23:38:04,  1.67s/step]INFO 2026-07-15 05:23:22 ot_train.py:649 step:9K smpl:589K ep:13K epch:2.70 loss:0.084 grdn:0.342 lr:9.9e-05 updt_s:0.896 data_s:0.783 smp/s:38 mem_gb:39.70
+Training:  16%|█▌        | 9400/60000 [4:13:54<23:22:23,  1.66s/step]INFO 2026-07-15 05:28:55 ot_train.py:649 step:9K smpl:602K ep:14K epch:2.75 loss:0.081 grdn:0.330 lr:9.9e-05 updt_s:0.902 data_s:0.760 smp/s:39 mem_gb:39.71
+Training:  16%|█▌        | 9600/60000 [4:19:28<23:36:14,  1.69s/step]INFO 2026-07-15 05:34:29 ot_train.py:649 step:10K smpl:614K ep:14K epch:2.81 loss:0.080 grdn:0.323 lr:9.9e-05 updt_s:0.887 data_s:0.777 smp/s:38 mem_gb:39.71
+Training:  16%|█▋        | 9800/60000 [4:25:08<23:47:20,  1.71s/step]INFO 2026-07-15 05:40:09 ot_train.py:649 step:10K smpl:627K ep:14K epch:2.87 loss:0.080 grdn:0.321 lr:9.9e-05 updt_s:0.908 data_s:0.787 smp/s:38 mem_gb:39.71
+Training:  17%|█▋        | 10000/60000 [4:30:51<25:42:31,  1.85s/step]INFO 2026-07-15 05:45:52 ot_train.py:649 step:10K smpl:640K ep:14K epch:2.93 loss:0.076 grdn:0.321 lr:9.9e-05 updt_s:0.938 data_s:0.772 smp/s:37 mem_gb:39.71
+INFO 2026-07-15 05:45:52 ot_train.py:103 INSIGHT phase transition: index=1 name=fm_only range=[10000,60000) trainable_params=1620515968
+Training:  17%|█▋        | 10200/60000 [4:36:25<23:45:01,  1.72s/step]INFO 2026-07-15 05:51:26 ot_train.py:649 step:10K smpl:653K ep:15K epch:2.99 loss:0.082 grdn:0.352 lr:9.9e-05 updt_s:0.885 data_s:0.779 smp/s:38 mem_gb:39.65
+Training:  17%|█▋        | 10400/60000 [4:42:00<22:01:36,  1.60s/step]INFO 2026-07-15 05:57:01 ot_train.py:649 step:10K smpl:666K ep:15K epch:3.05 loss:0.080 grdn:0.337 lr:9.8e-05 updt_s:0.893 data_s:0.777 smp/s:38 mem_gb:39.65
+Training:  18%|█▊        | 10600/60000 [4:47:35<23:04:43,  1.68s/step]INFO 2026-07-15 06:02:36 ot_train.py:649 step:11K smpl:678K ep:15K epch:3.11 loss:0.076 grdn:0.313 lr:9.8e-05 updt_s:0.889 data_s:0.782 smp/s:38 mem_gb:39.65
+Training:  18%|█▊        | 10800/60000 [4:53:06<22:25:53,  1.64s/step]INFO 2026-07-15 06:08:07 ot_train.py:649 step:11K smpl:691K ep:16K epch:3.17 loss:0.076 grdn:0.318 lr:9.8e-05 updt_s:0.881 data_s:0.770 smp/s:39 mem_gb:39.65
+Training:  18%|█▊        | 11000/60000 [4:58:38<21:29:37,  1.58s/step]INFO 2026-07-15 06:13:39 ot_train.py:649 step:11K smpl:704K ep:16K epch:3.22 loss:0.072 grdn:0.304 lr:9.8e-05 updt_s:0.893 data_s:0.761 smp/s:39 mem_gb:39.65
+Training:  19%|█▊        | 11200/60000 [5:04:19<22:31:02,  1.66s/step]INFO 2026-07-15 06:19:19 ot_train.py:649 step:11K smpl:717K ep:16K epch:3.28 loss:0.075 grdn:0.310 lr:9.8e-05 updt_s:0.920 data_s:0.780 smp/s:38 mem_gb:39.65
+Training:  19%|█▉        | 11400/60000 [5:09:55<22:41:53,  1.68s/step]INFO 2026-07-15 06:24:56 ot_train.py:649 step:11K smpl:730K ep:16K epch:3.34 loss:0.076 grdn:0.313 lr:9.8e-05 updt_s:0.899 data_s:0.779 smp/s:38 mem_gb:39.63
+Training:  19%|█▉        | 11600/60000 [5:15:32<22:43:16,  1.69s/step]INFO 2026-07-15 06:30:33 ot_train.py:649 step:12K smpl:742K ep:17K epch:3.40 loss:0.072 grdn:0.297 lr:9.7e-05 updt_s:0.917 data_s:0.762 smp/s:38 mem_gb:39.65
+Training:  20%|█▉        | 11800/60000 [5:21:22<21:52:02,  1.63s/step]INFO 2026-07-15 06:36:23 ot_train.py:649 step:12K smpl:755K ep:17K epch:3.46 loss:0.075 grdn:0.320 lr:9.7e-05 updt_s:0.946 data_s:0.799 smp/s:37 mem_gb:39.65
+Training:  20%|██        | 12000/60000 [5:27:18<23:45:22,  1.78s/step]INFO 2026-07-15 06:42:19 ot_train.py:649 step:12K smpl:768K ep:17K epch:3.52 loss:0.076 grdn:0.336 lr:9.7e-05 updt_s:0.977 data_s:0.800 smp/s:36 mem_gb:39.65
+Training:  20%|██        | 12200/60000 [5:33:16<24:23:10,  1.84s/step]INFO 2026-07-15 06:48:17 ot_train.py:649 step:12K smpl:781K ep:18K epch:3.58 loss:0.073 grdn:0.316 lr:9.7e-05 updt_s:0.994 data_s:0.789 smp/s:36 mem_gb:39.65
+Training:  21%|██        | 12400/60000 [5:39:04<24:21:22,  1.84s/step]INFO 2026-07-15 06:54:05 ot_train.py:649 step:12K smpl:794K ep:18K epch:3.63 loss:0.073 grdn:0.316 lr:9.7e-05 updt_s:0.952 data_s:0.785 smp/s:37 mem_gb:39.65
+Training:  21%|██        | 12600/60000 [5:44:55<25:27:53,  1.93s/step]INFO 2026-07-15 06:59:56 ot_train.py:649 step:13K smpl:806K ep:18K epch:3.69 loss:0.072 grdn:0.303 lr:9.6e-05 updt_s:0.975 data_s:0.777 smp/s:37 mem_gb:39.65
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+Training:  22%|██▏       | 13000/60000 [5:56:39<21:00:35,  1.61s/step]INFO 2026-07-15 07:11:40 ot_train.py:649 step:13K smpl:832K ep:19K epch:3.81 loss:0.070 grdn:0.295 lr:9.6e-05 updt_s:0.984 data_s:0.795 smp/s:36 mem_gb:39.65
+Training:  22%|██▏       | 13200/60000 [6:02:25<21:19:28,  1.64s/step]INFO 2026-07-15 07:17:26 ot_train.py:649 step:13K smpl:845K ep:19K epch:3.87 loss:0.073 grdn:0.306 lr:9.6e-05 updt_s:0.944 data_s:0.780 smp/s:37 mem_gb:39.65
+Training:  22%|██▏       | 13400/60000 [6:08:20<23:43:01,  1.83s/step]INFO 2026-07-15 07:23:21 ot_train.py:649 step:13K smpl:858K ep:19K epch:3.93 loss:0.072 grdn:0.296 lr:9.6e-05 updt_s:0.978 data_s:0.794 smp/s:36 mem_gb:39.65
+Training:  23%|██▎       | 13600/60000 [6:14:08<21:11:34,  1.64s/step]INFO 2026-07-15 07:29:09 ot_train.py:649 step:14K smpl:870K ep:20K epch:3.99 loss:0.068 grdn:0.287 lr:9.5e-05 updt_s:0.935 data_s:0.799 smp/s:37 mem_gb:39.63
+Training:  23%|██▎       | 13800/60000 [6:19:42<21:26:33,  1.67s/step]INFO 2026-07-15 07:34:43 ot_train.py:649 step:14K smpl:883K ep:20K epch:4.04 loss:0.068 grdn:0.280 lr:9.5e-05 updt_s:0.896 data_s:0.771 smp/s:38 mem_gb:39.65
+Training:  23%|██▎       | 14000/60000 [6:25:21<22:42:43,  1.78s/step]INFO 2026-07-15 07:40:21 ot_train.py:649 step:14K smpl:896K ep:20K epch:4.10 loss:0.067 grdn:0.291 lr:9.5e-05 updt_s:0.897 data_s:0.789 smp/s:38 mem_gb:39.65
+Training:  24%|██▎       | 14200/60000 [6:30:55<21:31:39,  1.69s/step]INFO 2026-07-15 07:45:56 ot_train.py:649 step:14K smpl:909K ep:21K epch:4.16 loss:0.069 grdn:0.305 lr:9.5e-05 updt_s:0.880 data_s:0.785 smp/s:38 mem_gb:39.65
+Training:  24%|██▍       | 14400/60000 [6:36:28<21:36:08,  1.71s/step]INFO 2026-07-15 07:51:29 ot_train.py:649 step:14K smpl:922K ep:21K epch:4.22 loss:0.071 grdn:0.295 lr:9.4e-05 updt_s:0.890 data_s:0.771 smp/s:39 mem_gb:39.65
+Training:  24%|██▍       | 14600/60000 [6:42:05<20:58:43,  1.66s/step]INFO 2026-07-15 07:57:06 ot_train.py:649 step:15K smpl:934K ep:21K epch:4.28 loss:0.067 grdn:0.284 lr:9.4e-05 updt_s:0.896 data_s:0.787 smp/s:38 mem_gb:39.65
+Training:  25%|██▍       | 14800/60000 [6:47:41<21:04:35,  1.68s/step]INFO 2026-07-15 08:02:41 ot_train.py:649 step:15K smpl:947K ep:21K epch:4.34 loss:0.070 grdn:0.295 lr:9.4e-05 updt_s:0.875 data_s:0.796 smp/s:38 mem_gb:39.64
+Training:  25%|██▌       | 15000/60000 [6:53:19<20:50:35,  1.67s/step]INFO 2026-07-15 08:08:20 ot_train.py:649 step:15K smpl:960K ep:22K epch:4.40 loss:0.070 grdn:0.317 lr:9.3e-05 updt_s:0.899 data_s:0.789 smp/s:38 mem_gb:39.65
+Training:  25%|██▌       | 15200/60000 [6:58:52<20:45:17,  1.67s/step]INFO 2026-07-15 08:13:53 ot_train.py:649 step:15K smpl:973K ep:22K epch:4.45 loss:0.068 grdn:0.298 lr:9.3e-05 updt_s:0.880 data_s:0.783 smp/s:39 mem_gb:39.65
+Training:  26%|██▌       | 15400/60000 [7:04:28<22:29:15,  1.82s/step]INFO 2026-07-15 08:19:29 ot_train.py:649 step:15K smpl:986K ep:22K epch:4.51 loss:0.073 grdn:0.347 lr:9.3e-05 updt_s:0.883 data_s:0.791 smp/s:38 mem_gb:39.65
+Training:  26%|██▌       | 15600/60000 [7:10:02<20:40:03,  1.68s/step]INFO 2026-07-15 08:25:03 ot_train.py:649 step:16K smpl:998K ep:23K epch:4.57 loss:0.067 grdn:0.295 lr:9.3e-05 updt_s:0.886 data_s:0.781 smp/s:38 mem_gb:39.65
+Training:  26%|██▋       | 15800/60000 [7:15:38<27:01:07,  2.20s/step]INFO 2026-07-15 08:30:39 ot_train.py:649 step:16K smpl:1M ep:23K epch:4.63 loss:0.065 grdn:0.276 lr:9.2e-05 updt_s:0.873 data_s:0.802 smp/s:38 mem_gb:39.63
+Training:  27%|██▋       | 16000/60000 [7:21:13<20:23:19,  1.67s/step]INFO 2026-07-15 08:36:14 ot_train.py:649 step:16K smpl:1M ep:23K epch:4.69 loss:0.064 grdn:0.272 lr:9.2e-05 updt_s:0.892 data_s:0.776 smp/s:38 mem_gb:39.65
+Training:  27%|██▋       | 16200/60000 [7:26:50<22:05:56,  1.82s/step]INFO 2026-07-15 08:41:51 ot_train.py:649 step:16K smpl:1M ep:23K epch:4.75 loss:0.064 grdn:0.278 lr:9.2e-05 updt_s:0.899 data_s:0.782 smp/s:38 mem_gb:39.65
+Training:  27%|██▋       | 16400/60000 [7:32:28<19:56:23,  1.65s/step]INFO 2026-07-15 08:47:29 ot_train.py:649 step:16K smpl:1M ep:24K epch:4.81 loss:0.065 grdn:0.283 lr:9.1e-05 updt_s:0.905 data_s:0.782 smp/s:38 mem_gb:39.65
+Training:  28%|██▊       | 16600/60000 [7:38:03<20:03:35,  1.66s/step]INFO 2026-07-15 08:53:04 ot_train.py:649 step:17K smpl:1M ep:24K epch:4.87 loss:0.064 grdn:0.283 lr:9.1e-05 updt_s:0.889 data_s:0.781 smp/s:38 mem_gb:39.65
+Training:  28%|██▊       | 16800/60000 [7:43:38<21:15:04,  1.77s/step]INFO 2026-07-15 08:58:39 ot_train.py:649 step:17K smpl:1M ep:24K epch:4.92 loss:0.064 grdn:0.290 lr:9.1e-05 updt_s:0.875 data_s:0.793 smp/s:38 mem_gb:39.65
+Training:  28%|██▊       | 17000/60000 [7:49:13<19:59:47,  1.67s/step]INFO 2026-07-15 09:04:13 ot_train.py:649 step:17K smpl:1M ep:25K epch:4.98 loss:0.064 grdn:0.277 lr:9.0e-05 updt_s:0.914 data_s:0.754 smp/s:38 mem_gb:39.65
+Training:  29%|██▊       | 17200/60000 [7:54:45<19:53:59,  1.67s/step]INFO 2026-07-15 09:09:46 ot_train.py:649 step:17K smpl:1M ep:25K epch:5.04 loss:0.065 grdn:0.291 lr:9.0e-05 updt_s:0.889 data_s:0.766 smp/s:39 mem_gb:39.65
+Training:  29%|██▉       | 17400/60000 [8:00:19<18:49:17,  1.59s/step]INFO 2026-07-15 09:15:20 ot_train.py:649 step:17K smpl:1M ep:25K epch:5.10 loss:0.064 grdn:0.277 lr:9.0e-05 updt_s:0.878 data_s:0.791 smp/s:38 mem_gb:39.65
+Training:  29%|██▉       | 17600/60000 [8:05:52<19:38:30,  1.67s/step]INFO 2026-07-15 09:20:52 ot_train.py:649 step:18K smpl:1M ep:25K epch:5.16 loss:0.063 grdn:0.281 lr:8.9e-05 updt_s:0.880 data_s:0.777 smp/s:39 mem_gb:39.65
+Training:  30%|██▉       | 17800/60000 [8:11:29<19:55:36,  1.70s/step]INFO 2026-07-15 09:26:30 ot_train.py:649 step:18K smpl:1M ep:26K epch:5.22 loss:0.065 grdn:0.284 lr:8.9e-05 updt_s:0.874 data_s:0.809 smp/s:38 mem_gb:39.65
+Training:  30%|███       | 18000/60000 [8:17:05<19:44:32,  1.69s/step]INFO 2026-07-15 09:32:06 ot_train.py:649 step:18K smpl:1M ep:26K epch:5.28 loss:0.062 grdn:0.261 lr:8.8e-05 updt_s:0.871 data_s:0.804 smp/s:38 mem_gb:39.65
+Training:  30%|███       | 18200/60000 [8:22:43<19:40:36,  1.69s/step]INFO 2026-07-15 09:37:44 ot_train.py:649 step:18K smpl:1M ep:26K epch:5.33 loss:0.062 grdn:0.270 lr:8.8e-05 updt_s:0.881 data_s:0.804 smp/s:38 mem_gb:39.63
+Training:  31%|███       | 18400/60000 [8:28:19<19:21:06,  1.67s/step]INFO 2026-07-15 09:43:20 ot_train.py:649 step:18K smpl:1M ep:27K epch:5.39 loss:0.061 grdn:0.283 lr:8.8e-05 updt_s:0.869 data_s:0.804 smp/s:38 mem_gb:39.65
+Training:  31%|███       | 18600/60000 [8:33:54<16:08:49,  1.40s/step]INFO 2026-07-15 09:48:55 ot_train.py:649 step:19K smpl:1M ep:27K epch:5.45 loss:0.062 grdn:0.270 lr:8.7e-05 updt_s:0.881 data_s:0.790 smp/s:38 mem_gb:39.65
+Training:  31%|███▏      | 18800/60000 [8:38:38<16:15:26,  1.42s/step]INFO 2026-07-15 09:53:39 ot_train.py:649 step:19K smpl:1M ep:27K epch:5.51 loss:0.061 grdn:0.283 lr:8.7e-05 updt_s:0.807 data_s:0.609 smp/s:45 mem_gb:39.65
+Training:  32%|███▏      | 19000/60000 [8:43:21<15:45:44,  1.38s/step]INFO 2026-07-15 09:58:22 ot_train.py:649 step:19K smpl:1M ep:27K epch:5.57 loss:0.063 grdn:0.275 lr:8.7e-05 updt_s:0.811 data_s:0.599 smp/s:45 mem_gb:39.65
+Training:  32%|███▏      | 19200/60000 [8:47:52<15:17:55,  1.35s/step]INFO 2026-07-15 10:02:53 ot_train.py:649 step:19K smpl:1M ep:28K epch:5.63 loss:0.063 grdn:0.287 lr:8.6e-05 updt_s:0.804 data_s:0.545 smp/s:47 mem_gb:39.65
+Training:  32%|███▏      | 19400/60000 [8:52:20<14:56:52,  1.33s/step]INFO 2026-07-15 10:07:21 ot_train.py:649 step:19K smpl:1M ep:28K epch:5.69 loss:0.063 grdn:0.290 lr:8.6e-05 updt_s:0.805 data_s:0.531 smp/s:48 mem_gb:39.65
+Training:  33%|███▎      | 19600/60000 [8:56:48<14:48:11,  1.32s/step]INFO 2026-07-15 10:11:48 ot_train.py:649 step:20K smpl:1M ep:28K epch:5.74 loss:0.062 grdn:0.277 lr:8.5e-05 updt_s:0.801 data_s:0.533 smp/s:48 mem_gb:39.65
+Training:  33%|███▎      | 19800/60000 [9:01:16<14:59:14,  1.34s/step]INFO 2026-07-15 10:16:17 ot_train.py:649 step:20K smpl:1M ep:29K epch:5.80 loss:0.059 grdn:0.277 lr:8.5e-05 updt_s:0.801 data_s:0.536 smp/s:48 mem_gb:39.65
+Training:  33%|███▎      | 20000/60000 [9:05:46<14:52:36,  1.34s/step]INFO 2026-07-15 10:20:47 ot_train.py:649 step:20K smpl:1M ep:29K epch:5.86 loss:0.062 grdn:0.278 lr:8.5e-05 updt_s:0.813 data_s:0.535 smp/s:48 mem_gb:39.65
+Training:  34%|███▎      | 20200/60000 [9:10:14<14:50:59,  1.34s/step]INFO 2026-07-15 10:25:15 ot_train.py:649 step:20K smpl:1M ep:29K epch:5.92 loss:0.060 grdn:0.276 lr:8.4e-05 updt_s:0.794 data_s:0.544 smp/s:48 mem_gb:39.65
+Training:  34%|███▍      | 20400/60000 [9:14:58<14:46:14,  1.34s/step]INFO 2026-07-15 10:29:59 ot_train.py:649 step:20K smpl:1M ep:29K epch:5.98 loss:0.061 grdn:0.291 lr:8.4e-05 updt_s:0.863 data_s:0.553 smp/s:45 mem_gb:39.63
+Training:  34%|███▍      | 20600/60000 [9:19:29<14:45:25,  1.35s/step]INFO 2026-07-15 10:34:30 ot_train.py:649 step:21K smpl:1M ep:30K epch:6.04 loss:0.058 grdn:0.269 lr:8.3e-05 updt_s:0.810 data_s:0.540 smp/s:47 mem_gb:39.65
+Training:  35%|███▍      | 20800/60000 [9:23:59<14:47:02,  1.36s/step]INFO 2026-07-15 10:39:00 ot_train.py:649 step:21K smpl:1M ep:30K epch:6.10 loss:0.059 grdn:0.273 lr:8.3e-05 updt_s:0.809 data_s:0.535 smp/s:48 mem_gb:39.65
+Training:  35%|███���      | 21000/60000 [9:28:28<14:20:50,  1.32s/step]INFO 2026-07-15 10:43:29 ot_train.py:649 step:21K smpl:1M ep:30K epch:6.15 loss:0.058 grdn:0.277 lr:8.2e-05 updt_s:0.808 data_s:0.537 smp/s:48 mem_gb:39.65
+Training:  35%|███▌      | 21200/60000 [9:32:58<14:35:05,  1.35s/step]INFO 2026-07-15 10:47:59 ot_train.py:649 step:21K smpl:1M ep:31K epch:6.21 loss:0.059 grdn:0.273 lr:8.2e-05 updt_s:0.805 data_s:0.539 smp/s:48 mem_gb:39.65
+Training:  36%|███▌      | 21400/60000 [9:37:28<14:18:09,  1.33s/step]INFO 2026-07-15 10:52:29 ot_train.py:649 step:21K smpl:1M ep:31K epch:6.27 loss:0.057 grdn:0.269 lr:8.1e-05 updt_s:0.806 data_s:0.540 smp/s:48 mem_gb:39.65
+Training:  36%|███▌      | 21600/60000 [9:41:58<13:56:51,  1.31s/step]INFO 2026-07-15 10:56:59 ot_train.py:649 step:22K smpl:1M ep:31K epch:6.33 loss:0.058 grdn:0.263 lr:8.1e-05 updt_s:0.809 data_s:0.537 smp/s:48 mem_gb:39.65
+Training:  36%|███▋      | 21800/60000 [9:46:47<15:02:29,  1.42s/step]INFO 2026-07-15 11:01:48 ot_train.py:649 step:22K smpl:1M ep:31K epch:6.39 loss:0.059 grdn:0.288 lr:8.1e-05 updt_s:0.813 data_s:0.625 smp/s:44 mem_gb:39.65
+Training:  37%|███▋      | 22000/60000 [9:51:22<14:26:30,  1.37s/step]INFO 2026-07-15 11:06:23 ot_train.py:649 step:22K smpl:1M ep:32K epch:6.45 loss:0.058 grdn:0.271 lr:8.0e-05 updt_s:0.805 data_s:0.567 smp/s:47 mem_gb:39.65
+Training:  37%|███▋      | 22200/60000 [9:55:52<14:11:46,  1.35s/step]INFO 2026-07-15 11:10:53 ot_train.py:649 step:22K smpl:1M ep:32K epch:6.51 loss:0.057 grdn:0.268 lr:8.0e-05 updt_s:0.805 data_s:0.543 smp/s:47 mem_gb:39.65
+Training:  37%|███▋      | 22400/60000 [10:00:21<14:02:59,  1.35s/step]INFO 2026-07-15 11:15:22 ot_train.py:649 step:22K smpl:1M ep:32K epch:6.57 loss:0.057 grdn:0.267 lr:7.9e-05 updt_s:0.799 data_s:0.542 smp/s:48 mem_gb:39.65
+Training:  38%|███▊      | 22600/60000 [10:04:52<13:54:35,  1.34s/step]INFO 2026-07-15 11:19:52 ot_train.py:649 step:23K smpl:1M ep:33K epch:6.62 loss:0.055 grdn:0.258 lr:7.9e-05 updt_s:0.803 data_s:0.544 smp/s:48 mem_gb:39.63
+Training:  38%|███▊      | 22800/60000 [10:09:22<14:20:06,  1.39s/step]INFO 2026-07-15 11:24:23 ot_train.py:649 step:23K smpl:1M ep:33K epch:6.68 loss:0.056 grdn:0.277 lr:7.8e-05 updt_s:0.801 data_s:0.547 smp/s:47 mem_gb:39.65
+Training:  38%|███▊      | 23000/60000 [10:13:52<13:51:30,  1.35s/step]INFO 2026-07-15 11:28:53 ot_train.py:649 step:23K smpl:1M ep:33K epch:6.74 loss:0.057 grdn:0.286 lr:7.8e-05 updt_s:0.801 data_s:0.547 smp/s:47 mem_gb:39.65
+Training:  39%|███▊      | 23200/60000 [10:18:22<13:42:00,  1.34s/step]INFO 2026-07-15 11:33:23 ot_train.py:649 step:23K smpl:1M ep:34K epch:6.80 loss:0.056 grdn:0.276 lr:7.7e-05 updt_s:0.801 data_s:0.544 smp/s:48 mem_gb:39.65
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+Training:  44%|████▍     | 26400/60000 [11:31:20<12:25:53,  1.33s/step]INFO 2026-07-15 12:46:20 ot_train.py:649 step:26K smpl:2M ep:38K epch:7.74 loss:0.051 grdn:0.260 lr:6.9e-05 updt_s:0.805 data_s:0.541 smp/s:48 mem_gb:39.64
+Training:  44%|████▍     | 26600/60000 [11:36:02<13:23:44,  1.44s/step]INFO 2026-07-15 12:51:03 ot_train.py:649 step:27K smpl:2M ep:38K epch:7.80 loss:0.052 grdn:0.266 lr:6.8e-05 updt_s:0.838 data_s:0.570 smp/s:45 mem_gb:39.65
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+Training:  45%|████▌     | 27000/60000 [11:45:37<12:17:07,  1.34s/step]INFO 2026-07-15 13:00:37 ot_train.py:649 step:27K smpl:2M ep:39K epch:7.91 loss:0.050 grdn:0.264 lr:6.7e-05 updt_s:0.812 data_s:0.573 smp/s:46 mem_gb:39.65
+Training:  45%|████▌     | 27200/60000 [11:50:09<12:08:53,  1.33s/step]INFO 2026-07-15 13:05:09 ot_train.py:649 step:27K smpl:2M ep:39K epch:7.97 loss:0.051 grdn:0.276 lr:6.7e-05 updt_s:0.809 data_s:0.547 smp/s:47 mem_gb:39.63
+Training:  46%|████▌     | 27400/60000 [11:54:37<12:02:00,  1.33s/step]INFO 2026-07-15 13:09:38 ot_train.py:649 step:27K smpl:2M ep:40K epch:8.03 loss:0.051 grdn:0.275 lr:6.6e-05 updt_s:0.799 data_s:0.540 smp/s:48 mem_gb:39.65
+Training:  46%|████▌     | 27600/60000 [11:59:07<12:10:11,  1.35s/step]INFO 2026-07-15 13:14:08 ot_train.py:649 step:28K smpl:2M ep:40K epch:8.09 loss:0.049 grdn:0.272 lr:6.6e-05 updt_s:0.800 data_s:0.544 smp/s:48 mem_gb:39.65
+Training:  46%|████▋     | 27800/60000 [12:03:36<12:01:44,  1.34s/step]INFO 2026-07-15 13:18:37 ot_train.py:649 step:28K smpl:2M ep:40K epch:8.15 loss:0.049 grdn:0.278 lr:6.5e-05 updt_s:0.798 data_s:0.545 smp/s:48 mem_gb:39.65
+Training:  47%|████▋     | 28000/60000 [12:08:10<12:01:49,  1.35s/step]INFO 2026-07-15 13:23:11 ot_train.py:649 step:28K smpl:2M ep:40K epch:8.21 loss:0.050 grdn:0.301 lr:6.5e-05 updt_s:0.822 data_s:0.544 smp/s:47 mem_gb:39.65
+Training:  47%|████▋     | 28200/60000 [12:12:40<11:59:13,  1.36s/step]INFO 2026-07-15 13:27:41 ot_train.py:649 step:28K smpl:2M ep:41K epch:8.26 loss:0.049 grdn:0.268 lr:6.4e-05 updt_s:0.805 data_s:0.540 smp/s:48 mem_gb:39.65
+Training:  47%|████▋     | 28400/60000 [12:17:19<12:27:06,  1.42s/step]INFO 2026-07-15 13:32:20 ot_train.py:649 step:28K smpl:2M ep:41K epch:8.32 loss:0.049 grdn:0.277 lr:6.4e-05 updt_s:0.837 data_s:0.558 smp/s:46 mem_gb:39.65
+Training:  48%|████▊     | 28600/60000 [12:22:31<14:24:59,  1.65s/step]INFO 2026-07-15 13:37:32 ot_train.py:649 step:29K smpl:2M ep:41K epch:8.38 loss:0.049 grdn:0.256 lr:6.3e-05 updt_s:0.908 data_s:0.646 smp/s:41 mem_gb:39.65
+Training:  48%|████▊     | 28800/60000 [12:28:01<14:25:31,  1.66s/step]INFO 2026-07-15 13:43:02 ot_train.py:649 step:29K smpl:2M ep:42K epch:8.44 loss:0.049 grdn:0.271 lr:6.2e-05 updt_s:0.881 data_s:0.766 smp/s:39 mem_gb:39.65
+Training:  48%|████▊     | 29000/60000 [12:33:25<12:36:05,  1.46s/step]INFO 2026-07-15 13:48:26 ot_train.py:649 step:29K smpl:2M ep:42K epch:8.50 loss:0.049 grdn:0.267 lr:6.2e-05 updt_s:0.868 data_s:0.744 smp/s:40 mem_gb:39.65
+Training:  49%|████▊     | 29200/60000 [12:38:01<11:28:34,  1.34s/step]INFO 2026-07-15 13:53:01 ot_train.py:649 step:29K smpl:2M ep:42K epch:8.56 loss:0.048 grdn:0.263 lr:6.1e-05 updt_s:0.811 data_s:0.564 smp/s:47 mem_gb:39.65
+Training:  49%|████▉     | 29400/60000 [12:42:30<11:27:21,  1.35s/step]INFO 2026-07-15 13:57:31 ot_train.py:649 step:29K smpl:2M ep:42K epch:8.62 loss:0.048 grdn:0.270 lr:6.1e-05 updt_s:0.810 data_s:0.534 smp/s:48 mem_gb:39.63
+Training:  49%|████▉     | 29600/60000 [12:46:57<11:07:00,  1.32s/step]INFO 2026-07-15 14:01:58 ot_train.py:649 step:30K smpl:2M ep:43K epch:8.68 loss:0.047 grdn:0.266 lr:6.0e-05 updt_s:0.799 data_s:0.532 smp/s:48 mem_gb:39.65
+Training:  50%|████▉     | 29800/60000 [12:51:27<11:06:55,  1.33s/step]INFO 2026-07-15 14:06:28 ot_train.py:649 step:30K smpl:2M ep:43K epch:8.73 loss:0.046 grdn:0.268 lr:6.0e-05 updt_s:0.802 data_s:0.544 smp/s:48 mem_gb:39.65
+Training:  50%|█████     | 30000/60000 [12:56:02<11:43:37,  1.41s/step]INFO 2026-07-15 14:11:03 ot_train.py:649 step:30K smpl:2M ep:43K epch:8.79 loss:0.046 grdn:0.276 lr:5.9e-05 updt_s:0.827 data_s:0.546 smp/s:47 mem_gb:39.65
+Training:  50%|█████     | 30200/60000 [13:00:45<11:56:32,  1.44s/step]INFO 2026-07-15 14:15:45 ot_train.py:649 step:30K smpl:2M ep:44K epch:8.85 loss:0.046 grdn:0.262 lr:5.8e-05 updt_s:0.843 data_s:0.563 smp/s:46 mem_gb:39.65
+Training:  51%|█████     | 30400/60000 [13:05:50<14:53:55,  1.81s/step]INFO 2026-07-15 14:20:51 ot_train.py:649 step:30K smpl:2M ep:44K epch:8.91 loss:0.044 grdn:0.252 lr:5.8e-05 updt_s:0.888 data_s:0.636 smp/s:42 mem_gb:39.65
+Training:  51%|█████     | 30600/60000 [13:11:17<13:32:59,  1.66s/step]INFO 2026-07-15 14:26:18 ot_train.py:649 step:31K smpl:2M ep:44K epch:8.97 loss:0.046 grdn:0.268 lr:5.7e-05 updt_s:0.919 data_s:0.714 smp/s:39 mem_gb:39.65
+Training:  51%|█████▏    | 30800/60000 [13:16:45<13:27:19,  1.66s/step]INFO 2026-07-15 14:31:46 ot_train.py:649 step:31K smpl:2M ep:45K epch:9.03 loss:0.046 grdn:0.280 lr:5.7e-05 updt_s:0.898 data_s:0.737 smp/s:39 mem_gb:39.65
+Training:  52%|█████▏    | 31000/60000 [13:22:02<11:23:59,  1.42s/step]INFO 2026-07-15 14:37:03 ot_train.py:649 step:31K smpl:2M ep:45K epch:9.09 loss:0.046 grdn:0.268 lr:5.6e-05 updt_s:0.903 data_s:0.676 smp/s:41 mem_gb:39.65
+Training:  52%|█████▏    | 31200/60000 [13:27:29<13:05:05,  1.64s/step]INFO 2026-07-15 14:42:29 ot_train.py:649 step:31K smpl:2M ep:45K epch:9.14 loss:0.043 grdn:0.256 lr:5.6e-05 updt_s:0.918 data_s:0.711 smp/s:39 mem_gb:39.65
+Training:  52%|█████▏    | 31400/60000 [13:32:57<13:05:32,  1.65s/step]INFO 2026-07-15 14:47:58 ot_train.py:649 step:31K smpl:2M ep:45K epch:9.20 loss:0.045 grdn:0.270 lr:5.5e-05 updt_s:0.923 data_s:0.715 smp/s:39 mem_gb:39.65
+Training:  53%|█████▎    | 31600/60000 [13:38:29<15:03:45,  1.91s/step]INFO 2026-07-15 14:53:30 ot_train.py:649 step:32K smpl:2M ep:46K epch:9.26 loss:0.045 grdn:0.277 lr:5.4e-05 updt_s:0.898 data_s:0.756 smp/s:39 mem_gb:39.63
+Training:  53%|█████▎    | 31800/60000 [13:44:04<13:42:29,  1.75s/step]INFO 2026-07-15 14:59:05 ot_train.py:649 step:32K smpl:2M ep:46K epch:9.32 loss:0.044 grdn:0.273 lr:5.4e-05 updt_s:0.895 data_s:0.775 smp/s:38 mem_gb:39.65
+Training:  53%|█████▎    | 32000/60000 [13:49:37<12:24:43,  1.60s/step]INFO 2026-07-15 15:04:37 ot_train.py:649 step:32K smpl:2M ep:46K epch:9.38 loss:0.043 grdn:0.260 lr:5.3e-05 updt_s:0.900 data_s:0.760 smp/s:39 mem_gb:39.65
+Training:  54%|█████▎    | 32200/60000 [13:55:07<13:05:20,  1.69s/step]INFO 2026-07-15 15:10:08 ot_train.py:649 step:32K smpl:2M ep:47K epch:9.44 loss:0.043 grdn:0.268 lr:5.3e-05 updt_s:0.906 data_s:0.744 smp/s:39 mem_gb:39.65
+Training:  54%|█████▍    | 32400/60000 [14:00:41<12:41:28,  1.66s/step]INFO 2026-07-15 15:15:41 ot_train.py:649 step:32K smpl:2M ep:47K epch:9.50 loss:0.044 grdn:0.283 lr:5.2e-05 updt_s:0.892 data_s:0.769 smp/s:39 mem_gb:39.65
+Training:  54%|█████▍    | 32600/60000 [14:06:09<12:52:35,  1.69s/step]INFO 2026-07-15 15:21:10 ot_train.py:649 step:33K smpl:2M ep:47K epch:9.55 loss:0.044 grdn:0.285 lr:5.1e-05 updt_s:0.904 data_s:0.733 smp/s:39 mem_gb:39.65
+Training:  55%|█████▍    | 32800/60000 [14:11:40<12:28:25,  1.65s/step]INFO 2026-07-15 15:26:40 ot_train.py:649 step:33K smpl:2M ep:47K epch:9.61 loss:0.043 grdn:0.272 lr:5.1e-05 updt_s:0.913 data_s:0.734 smp/s:39 mem_gb:39.65
+Training:  55%|█████▌    | 33000/60000 [14:17:08<11:58:18,  1.60s/step]INFO 2026-07-15 15:32:09 ot_train.py:649 step:33K smpl:2M ep:48K epch:9.67 loss:0.043 grdn:0.276 lr:5.0e-05 updt_s:0.901 data_s:0.737 smp/s:39 mem_gb:39.65
+Training:  55%|█████▌    | 33200/60000 [14:22:37<12:14:42,  1.64s/step]INFO 2026-07-15 15:37:37 ot_train.py:649 step:33K smpl:2M ep:48K epch:9.73 loss:0.043 grdn:0.265 lr:5.0e-05 updt_s:0.889 data_s:0.748 smp/s:39 mem_gb:39.65
+Training:  56%|█████▌    | 33400/60000 [14:28:10<13:17:00,  1.80s/step]INFO 2026-07-15 15:43:10 ot_train.py:649 step:33K smpl:2M ep:48K epch:9.79 loss:0.042 grdn:0.262 lr:4.9e-05 updt_s:0.907 data_s:0.753 smp/s:39 mem_gb:39.65
+Training:  56%|█████▌    | 33600/60000 [14:33:41<11:26:34,  1.56s/step]INFO 2026-07-15 15:48:42 ot_train.py:649 step:34K smpl:2M ep:49K epch:9.85 loss:0.042 grdn:0.269 lr:4.9e-05 updt_s:0.894 data_s:0.757 smp/s:39 mem_gb:39.65
+Training:  56%|█████▋    | 33800/60000 [14:39:12<12:05:25,  1.66s/step]INFO 2026-07-15 15:54:13 ot_train.py:649 step:34K smpl:2M ep:49K epch:9.91 loss:0.042 grdn:0.269 lr:4.8e-05 updt_s:0.902 data_s:0.750 smp/s:39 mem_gb:39.65
+Training:  57%|█████▋    | 34000/60000 [14:44:44<12:39:14,  1.75s/step]INFO 2026-07-15 15:59:45 ot_train.py:649 step:34K smpl:2M ep:49K epch:9.96 loss:0.041 grdn:0.269 lr:4.7e-05 updt_s:0.908 data_s:0.745 smp/s:39 mem_gb:39.63
+Training:  57%|█████▋    | 34200/60000 [14:50:15<11:34:10,  1.61s/step]INFO 2026-07-15 16:05:16 ot_train.py:649 step:34K smpl:2M ep:49K epch:10.02 loss:0.041 grdn:0.265 lr:4.7e-05 updt_s:0.885 data_s:0.765 smp/s:39 mem_gb:39.65
+Training:  57%|█████▋    | 34400/60000 [14:55:44<11:12:52,  1.58s/step]INFO 2026-07-15 16:10:45 ot_train.py:649 step:34K smpl:2M ep:50K epch:10.08 loss:0.040 grdn:0.275 lr:4.6e-05 updt_s:0.890 data_s:0.751 smp/s:39 mem_gb:39.65
+Training:  58%|█████▊    | 34600/60000 [15:01:17<13:52:57,  1.97s/step]INFO 2026-07-15 16:16:18 ot_train.py:649 step:35K smpl:2M ep:50K epch:10.14 loss:0.040 grdn:0.274 lr:4.6e-05 updt_s:0.897 data_s:0.762 smp/s:39 mem_gb:39.65
+Training:  58%|█████▊    | 34800/60000 [15:06:47<11:30:06,  1.64s/step]INFO 2026-07-15 16:21:48 ot_train.py:649 step:35K smpl:2M ep:50K epch:10.20 loss:0.041 grdn:0.276 lr:4.5e-05 updt_s:0.905 data_s:0.741 smp/s:39 mem_gb:39.65
+Training:  58%|█████▊    | 35000/60000 [15:12:24<13:44:55,  1.98s/step]INFO 2026-07-15 16:27:24 ot_train.py:649 step:35K smpl:2M ep:51K epch:10.26 loss:0.041 grdn:0.279 lr:4.4e-05 updt_s:0.915 data_s:0.762 smp/s:38 mem_gb:39.65
+Training:  59%|█████▊    | 35200/60000 [15:17:56<11:19:10,  1.64s/step]INFO 2026-07-15 16:32:57 ot_train.py:649 step:35K smpl:2M ep:51K epch:10.32 loss:0.040 grdn:0.271 lr:4.4e-05 updt_s:0.901 data_s:0.754 smp/s:39 mem_gb:39.65
+Training:  59%|█████▉    | 35400/60000 [15:23:25<11:22:13,  1.66s/step]INFO 2026-07-15 16:38:26 ot_train.py:649 step:35K smpl:2M ep:51K epch:10.38 loss:0.039 grdn:0.279 lr:4.3e-05 updt_s:0.937 data_s:0.704 smp/s:39 mem_gb:39.65
+Training:  59%|█████▉    | 35600/60000 [15:28:55<11:08:09,  1.64s/step]INFO 2026-07-15 16:43:56 ot_train.py:649 step:36K smpl:2M ep:51K epch:10.43 loss:0.039 grdn:0.263 lr:4.3e-05 updt_s:0.928 data_s:0.718 smp/s:39 mem_gb:39.65
+Training:  60%|█████▉    | 35800/60000 [15:34:28<10:59:18,  1.63s/step]INFO 2026-07-15 16:49:29 ot_train.py:649 step:36K smpl:2M ep:52K epch:10.49 loss:0.039 grdn:0.284 lr:4.2e-05 updt_s:0.922 data_s:0.736 smp/s:39 mem_gb:39.65
+Training:  60%|██████    | 36000/60000 [15:39:59<13:25:48,  2.01s/step]INFO 2026-07-15 16:55:00 ot_train.py:649 step:36K smpl:2M ep:52K epch:10.55 loss:0.039 grdn:0.273 lr:4.2e-05 updt_s:0.892 data_s:0.758 smp/s:39 mem_gb:39.65
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+Training:  65%|██████▌   | 39000/60000 [17:02:57<9:25:02,  1.61s/step]INFO 2026-07-15 18:17:58 ot_train.py:649 step:39K smpl:2M ep:56K epch:11.43 loss:0.034 grdn:0.277 lr:3.3e-05 updt_s:0.908 data_s:0.727 smp/s:39 mem_gb:39.65
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+Training:  66%|██████▌   | 39400/60000 [17:14:03<9:23:47,  1.64s/step]INFO 2026-07-15 18:29:04 ot_train.py:649 step:39K smpl:3M ep:57K epch:11.55 loss:0.034 grdn:0.275 lr:3.2e-05 updt_s:0.890 data_s:0.761 smp/s:39 mem_gb:39.65
+Training:  66%|██████▌   | 39600/60000 [17:19:37<9:24:05,  1.66s/step]INFO 2026-07-15 18:34:38 ot_train.py:649 step:40K smpl:3M ep:57K epch:11.61 loss:0.034 grdn:0.274 lr:3.2e-05 updt_s:0.899 data_s:0.765 smp/s:38 mem_gb:39.65
+Training:  66%|██████▋   | 39800/60000 [17:25:13<9:10:36,  1.64s/step]INFO 2026-07-15 18:40:14 ot_train.py:649 step:40K smpl:3M ep:58K epch:11.66 loss:0.033 grdn:0.274 lr:3.1e-05 updt_s:0.904 data_s:0.773 smp/s:38 mem_gb:39.65
+Training:  67%|██████▋   | 40000/60000 [17:30:46<9:06:25,  1.64s/step]INFO 2026-07-15 18:45:47 ot_train.py:649 step:40K smpl:3M ep:58K epch:11.72 loss:0.033 grdn:0.273 lr:3.0e-05 updt_s:0.910 data_s:0.748 smp/s:39 mem_gb:39.65
+Training:  67%|██████▋   | 40200/60000 [17:36:15<8:59:06,  1.63s/step]INFO 2026-07-15 18:51:16 ot_train.py:649 step:40K smpl:3M ep:58K epch:11.78 loss:0.034 grdn:0.294 lr:3.0e-05 updt_s:0.886 data_s:0.756 smp/s:39 mem_gb:39.65
+Training:  67%|██████▋   | 40400/60000 [17:41:45<8:59:47,  1.65s/step]INFO 2026-07-15 18:56:45 ot_train.py:649 step:40K smpl:3M ep:58K epch:11.84 loss:0.033 grdn:0.278 lr:2.9e-05 updt_s:0.893 data_s:0.747 smp/s:39 mem_gb:39.65
+Training:  68%|██████▊   | 40600/60000 [17:47:17<9:01:37,  1.68s/step]INFO 2026-07-15 19:02:18 ot_train.py:649 step:41K smpl:3M ep:59K epch:11.90 loss:0.034 grdn:0.276 lr:2.9e-05 updt_s:0.914 data_s:0.744 smp/s:39 mem_gb:39.65
+Training:  68%|██████▊   | 40800/60000 [17:52:47<8:38:50,  1.62s/step]INFO 2026-07-15 19:07:48 ot_train.py:649 step:41K smpl:3M ep:59K epch:11.96 loss:0.031 grdn:0.275 lr:2.8e-05 updt_s:0.915 data_s:0.728 smp/s:39 mem_gb:39.63
+Training:  68%|██████▊   | 41000/60000 [17:58:20<8:44:08,  1.66s/step]INFO 2026-07-15 19:13:21 ot_train.py:649 step:41K smpl:3M ep:59K epch:12.02 loss:0.031 grdn:0.267 lr:2.8e-05 updt_s:0.905 data_s:0.752 smp/s:39 mem_gb:39.65
+Training:  69%|██████▊   | 41200/60000 [18:03:54<8:27:18,  1.62s/step]INFO 2026-07-15 19:18:55 ot_train.py:649 step:41K smpl:3M ep:60K epch:12.08 loss:0.031 grdn:0.270 lr:2.7e-05 updt_s:0.919 data_s:0.749 smp/s:38 mem_gb:39.65
+Training:  69%|██████▉   | 41400/60000 [18:09:29<8:36:59,  1.67s/step]INFO 2026-07-15 19:24:30 ot_train.py:649 step:41K smpl:3M ep:60K epch:12.13 loss:0.031 grdn:0.271 lr:2.7e-05 updt_s:0.885 data_s:0.785 smp/s:38 mem_gb:39.65
+Training:  69%|██████▉   | 41600/60000 [18:15:02<8:33:00,  1.67s/step]INFO 2026-07-15 19:30:03 ot_train.py:649 step:42K smpl:3M ep:60K epch:12.19 loss:0.031 grdn:0.266 lr:2.6e-05 updt_s:0.885 data_s:0.774 smp/s:39 mem_gb:39.65
+Training:  70%|██████▉   | 41800/60000 [18:20:41<8:03:56,  1.60s/step]INFO 2026-07-15 19:35:42 ot_train.py:649 step:42K smpl:3M ep:60K epch:12.25 loss:0.031 grdn:0.264 lr:2.6e-05 updt_s:0.901 data_s:0.786 smp/s:38 mem_gb:39.65
+Training:  70%|███████   | 42000/60000 [18:26:15<8:51:45,  1.77s/step]INFO 2026-07-15 19:41:16 ot_train.py:649 step:42K smpl:3M ep:61K epch:12.31 loss:0.030 grdn:0.271 lr:2.5e-05 updt_s:0.903 data_s:0.763 smp/s:38 mem_gb:39.65
+Training:  70%|███████   | 42200/60000 [18:31:50<8:07:39,  1.64s/step]INFO 2026-07-15 19:46:51 ot_train.py:649 step:42K smpl:3M ep:61K epch:12.37 loss:0.031 grdn:0.288 lr:2.5e-05 updt_s:0.889 data_s:0.780 smp/s:38 mem_gb:39.65
+Training:  71%|███████   | 42400/60000 [18:37:21<7:42:54,  1.58s/step]INFO 2026-07-15 19:52:22 ot_train.py:649 step:42K smpl:3M ep:61K epch:12.43 loss:0.031 grdn:0.269 lr:2.4e-05 updt_s:0.880 data_s:0.770 smp/s:39 mem_gb:39.65
+Training:  71%|███████   | 42600/60000 [18:42:54<8:28:19,  1.75s/step]INFO 2026-07-15 19:57:54 ot_train.py:649 step:43K smpl:3M ep:62K epch:12.49 loss:0.030 grdn:0.283 lr:2.4e-05 updt_s:0.888 data_s:0.770 smp/s:39 mem_gb:39.65
+Training:  71%|███████▏  | 42800/60000 [18:48:29<8:12:23,  1.72s/step]INFO 2026-07-15 20:03:30 ot_train.py:649 step:43K smpl:3M ep:62K epch:12.54 loss:0.031 grdn:0.287 lr:2.3e-05 updt_s:0.899 data_s:0.776 smp/s:38 mem_gb:39.65
+Training:  72%|███████▏  | 43000/60000 [18:54:02<7:50:20,  1.66s/step]INFO 2026-07-15 20:09:02 ot_train.py:649 step:43K smpl:3M ep:62K epch:12.60 loss:0.030 grdn:0.266 lr:2.3e-05 updt_s:0.883 data_s:0.773 smp/s:39 mem_gb:39.63
+Training:  72%|███████▏  | 43200/60000 [18:59:32<7:37:26,  1.63s/step]INFO 2026-07-15 20:14:33 ot_train.py:649 step:43K smpl:3M ep:62K epch:12.66 loss:0.028 grdn:0.265 lr:2.2e-05 updt_s:0.881 data_s:0.768 smp/s:39 mem_gb:39.65
+Training:  72%|███████▏  | 43400/60000 [19:05:04<7:02:56,  1.53s/step]INFO 2026-07-15 20:20:05 ot_train.py:649 step:43K smpl:3M ep:63K epch:12.72 loss:0.029 grdn:0.258 lr:2.2e-05 updt_s:0.880 data_s:0.772 smp/s:39 mem_gb:39.65
+Training:  73%|███████▎  | 43600/60000 [19:10:37<7:33:46,  1.66s/step]INFO 2026-07-15 20:25:37 ot_train.py:649 step:44K smpl:3M ep:63K epch:12.78 loss:0.028 grdn:0.267 lr:2.1e-05 updt_s:0.901 data_s:0.759 smp/s:39 mem_gb:39.65
+Training:  73%|███████▎  | 43800/60000 [19:16:09<7:24:12,  1.65s/step]INFO 2026-07-15 20:31:10 ot_train.py:649 step:44K smpl:3M ep:63K epch:12.84 loss:0.028 grdn:0.264 lr:2.1e-05 updt_s:0.876 data_s:0.782 smp/s:39 mem_gb:39.65
+Training:  73%|███████▎  | 44000/60000 [19:21:43<7:17:16,  1.64s/step]INFO 2026-07-15 20:36:43 ot_train.py:649 step:44K smpl:3M ep:64K epch:12.90 loss:0.028 grdn:0.270 lr:2.0e-05 updt_s:0.876 data_s:0.787 smp/s:38 mem_gb:39.65
+Training:  74%|███████▎  | 44200/60000 [19:27:16<7:18:01,  1.66s/step]INFO 2026-07-15 20:42:17 ot_train.py:649 step:44K smpl:3M ep:64K epch:12.95 loss:0.028 grdn:0.278 lr:2.0e-05 updt_s:0.895 data_s:0.766 smp/s:39 mem_gb:39.65
+Training:  74%|███████▍  | 44400/60000 [19:32:48<7:06:06,  1.64s/step]INFO 2026-07-15 20:47:49 ot_train.py:649 step:44K smpl:3M ep:64K epch:13.01 loss:0.028 grdn:0.275 lr:1.9e-05 updt_s:0.886 data_s:0.772 smp/s:39 mem_gb:39.65
+Training:  74%|███████▍  | 44600/60000 [19:38:19<7:01:17,  1.64s/step]INFO 2026-07-15 20:53:20 ot_train.py:649 step:45K smpl:3M ep:64K epch:13.07 loss:0.027 grdn:0.245 lr:1.9e-05 updt_s:0.867 data_s:0.784 smp/s:39 mem_gb:39.65
+Training:  75%|███████▍  | 44800/60000 [19:43:53<6:56:48,  1.65s/step]INFO 2026-07-15 20:58:53 ot_train.py:649 step:45K smpl:3M ep:65K epch:13.13 loss:0.028 grdn:0.281 lr:1.9e-05 updt_s:0.891 data_s:0.770 smp/s:39 mem_gb:39.65
+Training:  75%|███████▌  | 45000/60000 [19:49:22<6:56:13,  1.66s/step]INFO 2026-07-15 21:04:23 ot_train.py:649 step:45K smpl:3M ep:65K epch:13.19 loss:0.028 grdn:0.274 lr:1.8e-05 updt_s:0.871 data_s:0.774 smp/s:39 mem_gb:39.65
+Training:  75%|███████▌  | 45200/60000 [19:54:55<6:43:19,  1.64s/step]INFO 2026-07-15 21:09:56 ot_train.py:649 step:45K smpl:3M ep:65K epch:13.25 loss:0.027 grdn:0.263 lr:1.8e-05 updt_s:0.869 data_s:0.790 smp/s:39 mem_gb:39.63
+Training:  76%|███████▌  | 45400/60000 [20:00:29<6:47:38,  1.68s/step]INFO 2026-07-15 21:15:30 ot_train.py:649 step:45K smpl:3M ep:66K epch:13.31 loss:0.026 grdn:0.257 lr:1.7e-05 updt_s:0.881 data_s:0.786 smp/s:38 mem_gb:39.65
+Training:  76%|███████▌  | 45600/60000 [20:06:02<6:52:35,  1.72s/step]INFO 2026-07-15 21:21:03 ot_train.py:649 step:46K smpl:3M ep:66K epch:13.36 loss:0.026 grdn:0.272 lr:1.7e-05 updt_s:0.883 data_s:0.775 smp/s:39 mem_gb:39.65
+Training:  76%|███████▋  | 45800/60000 [20:11:31<6:33:56,  1.66s/step]INFO 2026-07-15 21:26:32 ot_train.py:649 step:46K smpl:3M ep:66K epch:13.42 loss:0.026 grdn:0.258 lr:1.6e-05 updt_s:0.864 data_s:0.776 smp/s:39 mem_gb:39.65
+Training:  77%|███████▋  | 46000/60000 [20:17:03<6:29:20,  1.67s/step]INFO 2026-07-15 21:32:04 ot_train.py:649 step:46K smpl:3M ep:66K epch:13.48 loss:0.026 grdn:0.264 lr:1.6e-05 updt_s:0.874 data_s:0.780 smp/s:39 mem_gb:39.65
+Training:  77%|███████▋  | 46200/60000 [20:22:37<6:26:11,  1.68s/step]INFO 2026-07-15 21:37:37 ot_train.py:649 step:46K smpl:3M ep:67K epch:13.54 loss:0.026 grdn:0.264 lr:1.5e-05 updt_s:0.881 data_s:0.784 smp/s:38 mem_gb:39.65
+Training:  77%|███████▋  | 46400/60000 [20:28:13<6:47:41,  1.80s/step]INFO 2026-07-15 21:43:14 ot_train.py:649 step:46K smpl:3M ep:67K epch:13.60 loss:0.026 grdn:0.259 lr:1.5e-05 updt_s:0.891 data_s:0.785 smp/s:38 mem_gb:39.65
+Training:  78%|███████▊  | 46600/60000 [20:33:47<6:16:55,  1.69s/step]INFO 2026-07-15 21:48:48 ot_train.py:649 step:47K smpl:3M ep:67K epch:13.66 loss:0.026 grdn:0.254 lr:1.5e-05 updt_s:0.890 data_s:0.777 smp/s:38 mem_gb:39.65
+Training:  78%|███████▊  | 46800/60000 [20:39:23<5:54:43,  1.61s/step]INFO 2026-07-15 21:54:24 ot_train.py:649 step:47K smpl:3M ep:68K epch:13.72 loss:0.026 grdn:0.265 lr:1.4e-05 updt_s:0.904 data_s:0.771 smp/s:38 mem_gb:39.65
+Training:  78%|███████▊  | 47000/60000 [20:45:10<6:07:36,  1.70s/step]INFO 2026-07-15 22:00:11 ot_train.py:649 step:47K smpl:3M ep:68K epch:13.77 loss:0.025 grdn:0.281 lr:1.4e-05 updt_s:0.945 data_s:0.785 smp/s:37 mem_gb:39.65
+Training:  79%|███████▊  | 47200/60000 [20:50:49<5:27:49,  1.54s/step]INFO 2026-07-15 22:05:50 ot_train.py:649 step:47K smpl:3M ep:68K epch:13.83 loss:0.026 grdn:0.281 lr:1.3e-05 updt_s:0.930 data_s:0.762 smp/s:38 mem_gb:39.65
+Training:  79%|███████▉  | 47400/60000 [20:56:38<7:06:35,  2.03s/step]INFO 2026-07-15 22:11:39 ot_train.py:649 step:47K smpl:3M ep:68K epch:13.89 loss:0.024 grdn:0.257 lr:1.3e-05 updt_s:0.960 data_s:0.781 smp/s:37 mem_gb:39.63
+Training:  79%|███████▉  | 47600/60000 [21:02:33<5:47:49,  1.68s/step]INFO 2026-07-15 22:17:34 ot_train.py:649 step:48K smpl:3M ep:69K epch:13.95 loss:0.024 grdn:0.257 lr:1.3e-05 updt_s:0.973 data_s:0.795 smp/s:36 mem_gb:39.65
+Training:  80%|███████▉  | 47800/60000 [21:08:23<5:40:00,  1.67s/step]INFO 2026-07-15 22:23:24 ot_train.py:649 step:48K smpl:3M ep:69K epch:14.01 loss:0.024 grdn:0.247 lr:1.2e-05 updt_s:0.952 data_s:0.794 smp/s:37 mem_gb:39.65
+Training:  80%|████████  | 48000/60000 [21:14:09<5:54:37,  1.77s/step]INFO 2026-07-15 22:29:10 ot_train.py:649 step:48K smpl:3M ep:69K epch:14.07 loss:0.024 grdn:0.266 lr:1.2e-05 updt_s:0.937 data_s:0.788 smp/s:37 mem_gb:39.65
+Training:  80%|████████  | 48200/60000 [21:19:44<5:24:42,  1.65s/step]INFO 2026-07-15 22:34:45 ot_train.py:649 step:48K smpl:3M ep:70K epch:14.13 loss:0.024 grdn:0.254 lr:1.2e-05 updt_s:0.877 data_s:0.795 smp/s:38 mem_gb:39.65
+Training:  81%|████████  | 48400/60000 [21:25:17<5:15:43,  1.63s/step]INFO 2026-07-15 22:40:17 ot_train.py:649 step:48K smpl:3M ep:70K epch:14.19 loss:0.024 grdn:0.242 lr:1.1e-05 updt_s:0.865 data_s:0.792 smp/s:39 mem_gb:39.65
+Training:  81%|████████  | 48600/60000 [21:30:51<5:16:24,  1.67s/step]INFO 2026-07-15 22:45:52 ot_train.py:649 step:49K smpl:3M ep:70K epch:14.24 loss:0.024 grdn:0.249 lr:1.1e-05 updt_s:0.867 data_s:0.799 smp/s:38 mem_gb:39.65
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+Training:  83%|████████▎ | 49600/60000 [21:58:29<4:42:03,  1.63s/step]INFO 2026-07-15 23:13:30 ot_train.py:649 step:50K smpl:3M ep:72K epch:14.54 loss:0.023 grdn:0.256 lr:9.0e-06 updt_s:0.851 data_s:0.788 smp/s:39 mem_gb:39.65
+Training:  83%|████████▎ | 49800/60000 [22:03:59<4:24:07,  1.55s/step]INFO 2026-07-15 23:19:00 ot_train.py:649 step:50K smpl:3M ep:72K epch:14.60 loss:0.023 grdn:0.247 lr:8.7e-06 updt_s:0.876 data_s:0.770 smp/s:39 mem_gb:39.63
+Training:  83%|████████▎ | 49868/60000 [22:05:53<4:39:24,  1.65s/step]

RKD_TimewarpVAE/LAPstyle_linear_10K/wandb/run-20260715_011448-fbqzmp5m/run-fbqzmp5m.wandb

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RKD_TimewarpVAE/LAPstyle_linear_6K/wandb/run-20260715_011448-pebh9ode/files/output.log

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+INFO 2026-07-15 01:14:49 db_utils.py:121 Logs will be synced with wandb.
+INFO 2026-07-15 01:14:49 db_utils.py:122 Track this run --> https://wandb.ai/minje227_hyu-hanyang-university/lerobot/runs/pebh9ode
+INFO 2026-07-15 01:14:49 ot_train.py:298 Creating dataset
+INFO 2026-07-15 01:14:51 ot_train.py:332 Creating policy
+Fetching 27 files: 100%|██████████| 27/27 [00:00<00:00, 1325.37it/s]
+`torch_dtype` is deprecated! Use `dtype` instead!
+Loading weights: 100%|██████████| 1031/1031 [00:00<00:00, 2096.45it/s]
+INFO 2026-07-15 01:15:00 ot_train.py:405 Creating optimizer and scheduler
+INFO 2026-07-15 01:15:00 ot_train.py:439 Output dir: /home/ext_minje/groot_insight/Abs_6D/RKD_TimewarpVAE/LAPstyle_linear_6K
+INFO 2026-07-15 01:15:00 ot_train.py:446 cfg.steps=60000 (60K)
+INFO 2026-07-15 01:15:00 ot_train.py:447 dataset.num_frames=218367 (218K)
+INFO 2026-07-15 01:15:00 ot_train.py:448 dataset.num_episodes=4930
+INFO 2026-07-15 01:15:00 ot_train.py:451 Effective batch size: 64 x 1 = 64
+INFO 2026-07-15 01:15:00 ot_train.py:452 num_learnable_params=1622090881 (2B)
+INFO 2026-07-15 01:15:00 ot_train.py:453 num_total_params=3145709729 (3B)
+Training:   0%|          | 0/60000 [00:00<?, ?step/s]INFO 2026-07-15 01:15:00 ot_train.py:604 Start offline training on a fixed dataset, with effective batch size: 64
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+Training:   1%|          | 400/60000 [09:24<22:24:07,  1.35s/step]INFO 2026-07-15 01:24:24 ot_train.py:649 step:400 smpl:26K ep:578 epch:0.12 loss:1.021 grdn:1.398 lr:5.0e-06 updt_s:0.813 data_s:0.574 smp/s:46 mem_gb:39.70
+Training:   1%|          | 600/60000 [14:03<21:52:56,  1.33s/step]INFO 2026-07-15 01:29:03 ot_train.py:649 step:600 smpl:38K ep:867 epch:0.18 loss:0.416 grdn:2.294 lr:8.3e-06 updt_s:0.823 data_s:0.567 smp/s:46 mem_gb:39.70
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+Training:   2%|▏         | 1000/60000 [23:17<23:07:02,  1.41s/step]INFO 2026-07-15 01:38:17 ot_train.py:649 step:1K smpl:64K ep:1K epch:0.29 loss:0.236 grdn:2.537 lr:1.5e-05 updt_s:0.819 data_s:0.562 smp/s:46 mem_gb:39.70
+Training:   2%|▏         | 1200/60000 [27:52<21:59:48,  1.35s/step]INFO 2026-07-15 01:42:53 ot_train.py:649 step:1K smpl:77K ep:2K epch:0.35 loss:0.203 grdn:2.252 lr:1.8e-05 updt_s:0.819 data_s:0.553 smp/s:47 mem_gb:39.70
+Training:   2%|▏         | 1400/60000 [32:31<23:02:02,  1.42s/step]INFO 2026-07-15 01:47:31 ot_train.py:649 step:1K smpl:90K ep:2K epch:0.41 loss:0.186 grdn:1.998 lr:2.2e-05 updt_s:0.830 data_s:0.559 smp/s:46 mem_gb:39.70
+Training:   3%|▎         | 1600/60000 [37:09<23:48:13,  1.47s/step]INFO 2026-07-15 01:52:09 ot_train.py:649 step:2K smpl:102K ep:2K epch:0.47 loss:0.173 grdn:1.758 lr:2.5e-05 updt_s:0.827 data_s:0.560 smp/s:46 mem_gb:39.70
+Training:   3%|▎         | 1800/60000 [41:54<22:41:14,  1.40s/step]INFO 2026-07-15 01:56:54 ot_train.py:649 step:2K smpl:115K ep:3K epch:0.53 loss:0.162 grdn:1.516 lr:2.8e-05 updt_s:0.845 data_s:0.576 smp/s:45 mem_gb:39.70
+Training:   3%|▎         | 2000/60000 [47:10<24:14:16,  1.50s/step]INFO 2026-07-15 02:02:10 ot_train.py:649 step:2K smpl:128K ep:3K epch:0.59 loss:0.153 grdn:1.335 lr:3.2e-05 updt_s:0.888 data_s:0.687 smp/s:41 mem_gb:39.70
+Training:   4%|▎         | 2200/60000 [52:28<25:51:23,  1.61s/step]INFO 2026-07-15 02:07:29 ot_train.py:649 step:2K smpl:141K ep:3K epch:0.64 loss:0.147 grdn:1.223 lr:3.5e-05 updt_s:0.876 data_s:0.712 smp/s:40 mem_gb:39.70
+Training:   4%|▍         | 2400/60000 [58:13<26:13:20,  1.64s/step]INFO 2026-07-15 02:13:13 ot_train.py:649 step:2K smpl:154K ep:3K epch:0.70 loss:0.142 grdn:1.103 lr:3.8e-05 updt_s:0.979 data_s:0.739 smp/s:37 mem_gb:39.69
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+Training:   5%|▌         | 3200/60000 [1:20:37<26:17:44,  1.67s/step]INFO 2026-07-15 02:35:37 ot_train.py:649 step:3K smpl:205K ep:5K epch:0.94 loss:0.123 grdn:0.761 lr:5.2e-05 updt_s:0.905 data_s:0.774 smp/s:38 mem_gb:39.71
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+Training:   6%|▌         | 3600/60000 [1:31:55<26:20:28,  1.68s/step]INFO 2026-07-15 02:46:55 ot_train.py:649 step:4K smpl:230K ep:5K epch:1.06 loss:0.113 grdn:0.646 lr:5.8e-05 updt_s:0.946 data_s:0.766 smp/s:37 mem_gb:39.71
+Training:   6%|▋         | 3800/60000 [1:37:31<25:29:57,  1.63s/step]INFO 2026-07-15 02:52:31 ot_train.py:649 step:4K smpl:243K ep:5K epch:1.11 loss:0.110 grdn:0.636 lr:6.2e-05 updt_s:0.903 data_s:0.773 smp/s:38 mem_gb:39.71
+Training:   7%|▋         | 4000/60000 [1:43:11<25:54:37,  1.67s/step]INFO 2026-07-15 02:58:11 ot_train.py:649 step:4K smpl:256K ep:6K epch:1.17 loss:0.109 grdn:0.590 lr:6.5e-05 updt_s:0.918 data_s:0.777 smp/s:38 mem_gb:39.71
+Training:   7%|▋         | 4200/60000 [1:48:54<26:11:55,  1.69s/step]INFO 2026-07-15 03:03:54 ot_train.py:649 step:4K smpl:269K ep:6K epch:1.23 loss:0.107 grdn:0.556 lr:6.8e-05 updt_s:0.924 data_s:0.785 smp/s:37 mem_gb:39.71
+Training:   7%|▋         | 4400/60000 [1:54:30<26:10:52,  1.70s/step]INFO 2026-07-15 03:09:31 ot_train.py:649 step:4K smpl:282K ep:6K epch:1.29 loss:0.105 grdn:0.524 lr:7.2e-05 updt_s:0.892 data_s:0.786 smp/s:38 mem_gb:39.71
+Training:   8%|▊         | 4600/60000 [2:00:05<25:36:21,  1.66s/step]INFO 2026-07-15 03:15:06 ot_train.py:649 step:5K smpl:294K ep:7K epch:1.35 loss:0.102 grdn:0.504 lr:7.5e-05 updt_s:0.878 data_s:0.791 smp/s:38 mem_gb:39.70
+Training:   8%|▊         | 4800/60000 [2:05:40<24:32:38,  1.60s/step]INFO 2026-07-15 03:20:41 ot_train.py:649 step:5K smpl:307K ep:7K epch:1.41 loss:0.102 grdn:0.516 lr:7.8e-05 updt_s:0.906 data_s:0.765 smp/s:38 mem_gb:39.71
+Training:   8%|▊         | 5000/60000 [2:11:18<26:55:04,  1.76s/step]INFO 2026-07-15 03:26:18 ot_train.py:649 step:5K smpl:320K ep:7K epch:1.47 loss:0.101 grdn:0.469 lr:8.2e-05 updt_s:0.899 data_s:0.783 smp/s:38 mem_gb:39.71
+Training:   9%|▊         | 5200/60000 [2:16:54<24:51:25,  1.63s/step]INFO 2026-07-15 03:31:55 ot_train.py:649 step:5K smpl:333K ep:8K epch:1.52 loss:0.103 grdn:0.472 lr:8.5e-05 updt_s:0.886 data_s:0.793 smp/s:38 mem_gb:39.71
+Training:   9%|▉         | 5400/60000 [2:22:32<25:31:34,  1.68s/step]INFO 2026-07-15 03:37:33 ot_train.py:649 step:5K smpl:346K ep:8K epch:1.58 loss:0.098 grdn:0.447 lr:8.8e-05 updt_s:0.899 data_s:0.786 smp/s:38 mem_gb:39.71
+Training:   9%|▉         | 5600/60000 [2:28:08<25:11:56,  1.67s/step]INFO 2026-07-15 03:43:08 ot_train.py:649 step:6K smpl:358K ep:8K epch:1.64 loss:0.096 grdn:0.432 lr:9.2e-05 updt_s:0.886 data_s:0.789 smp/s:38 mem_gb:39.71
+Training:  10%|▉         | 5800/60000 [2:33:44<25:01:13,  1.66s/step]INFO 2026-07-15 03:48:44 ot_train.py:649 step:6K smpl:371K ep:8K epch:1.70 loss:0.094 grdn:0.417 lr:9.5e-05 updt_s:0.888 data_s:0.786 smp/s:38 mem_gb:39.71
+Training:  10%|█         | 6000/60000 [2:39:21<27:26:41,  1.83s/step]INFO 2026-07-15 03:54:21 ot_train.py:649 step:6K smpl:384K ep:9K epch:1.76 loss:0.095 grdn:0.425 lr:9.8e-05 updt_s:0.889 data_s:0.789 smp/s:38 mem_gb:39.71
+INFO 2026-07-15 03:54:21 ot_train.py:103 INSIGHT phase transition: index=1 name=fm_only range=[6000,60000) trainable_params=1620515968
+Training:  10%|█         | 6200/60000 [2:44:57<25:12:11,  1.69s/step]INFO 2026-07-15 03:59:57 ot_train.py:649 step:6K smpl:397K ep:9K epch:1.82 loss:0.095 grdn:0.411 lr:1.0e-04 updt_s:0.898 data_s:0.778 smp/s:38 mem_gb:39.65
+Training:  11%|█         | 6400/60000 [2:50:33<24:49:56,  1.67s/step]INFO 2026-07-15 04:05:33 ot_train.py:649 step:6K smpl:410K ep:9K epch:1.88 loss:0.092 grdn:0.406 lr:1.0e-04 updt_s:0.888 data_s:0.787 smp/s:38 mem_gb:39.65
+Training:  11%|█         | 6600/60000 [2:56:11<24:40:30,  1.66s/step]INFO 2026-07-15 04:11:11 ot_train.py:649 step:7K smpl:422K ep:10K epch:1.93 loss:0.089 grdn:0.369 lr:1.0e-04 updt_s:0.899 data_s:0.785 smp/s:38 mem_gb:39.65
+Training:  11%|█▏        | 6800/60000 [3:01:48<25:13:44,  1.71s/step]INFO 2026-07-15 04:16:49 ot_train.py:649 step:7K smpl:435K ep:10K epch:1.99 loss:0.092 grdn:0.408 lr:1.0e-04 updt_s:0.889 data_s:0.794 smp/s:38 mem_gb:39.63
+Training:  12%|█▏        | 7000/60000 [3:07:23<24:04:04,  1.63s/step]INFO 2026-07-15 04:22:23 ot_train.py:649 step:7K smpl:448K ep:10K epch:2.05 loss:0.091 grdn:0.374 lr:1.0e-04 updt_s:0.889 data_s:0.779 smp/s:38 mem_gb:39.65
+Training:  12%|█▏        | 7200/60000 [3:12:59<24:30:07,  1.67s/step]INFO 2026-07-15 04:27:59 ot_train.py:649 step:7K smpl:461K ep:10K epch:2.11 loss:0.087 grdn:0.365 lr:1.0e-04 updt_s:0.881 data_s:0.793 smp/s:38 mem_gb:39.65
+Training:  12%|█▏        | 7400/60000 [3:18:36<24:22:07,  1.67s/step]INFO 2026-07-15 04:33:36 ot_train.py:649 step:7K smpl:474K ep:11K epch:2.17 loss:0.089 grdn:0.379 lr:1.0e-04 updt_s:0.888 data_s:0.791 smp/s:38 mem_gb:39.65
+Training:  13%|█▎        | 7600/60000 [3:24:13<24:14:16,  1.67s/step]INFO 2026-07-15 04:39:13 ot_train.py:649 step:8K smpl:486K ep:11K epch:2.23 loss:0.088 grdn:0.358 lr:1.0e-04 updt_s:0.891 data_s:0.791 smp/s:38 mem_gb:39.65
+Training:  13%|█▎        | 7800/60000 [3:29:51<27:32:12,  1.90s/step]INFO 2026-07-15 04:44:51 ot_train.py:649 step:8K smpl:499K ep:11K epch:2.29 loss:0.087 grdn:0.362 lr:1.0e-04 updt_s:0.901 data_s:0.782 smp/s:38 mem_gb:39.65
+Training:  13%|█▎        | 8000/60000 [3:35:26<24:11:18,  1.67s/step]INFO 2026-07-15 04:50:27 ot_train.py:649 step:8K smpl:512K ep:12K epch:2.34 loss:0.089 grdn:0.385 lr:1.0e-04 updt_s:0.870 data_s:0.803 smp/s:38 mem_gb:39.65
+Training:  14%|█▎        | 8200/60000 [3:41:06<23:27:19,  1.63s/step]INFO 2026-07-15 04:56:07 ot_train.py:649 step:8K smpl:525K ep:12K epch:2.40 loss:0.086 grdn:0.358 lr:1.0e-04 updt_s:0.909 data_s:0.787 smp/s:38 mem_gb:39.65
+Training:  14%|█▍        | 8400/60000 [3:46:45<24:59:29,  1.74s/step]INFO 2026-07-15 05:01:45 ot_train.py:649 step:8K smpl:538K ep:12K epch:2.46 loss:0.085 grdn:0.354 lr:1.0e-04 updt_s:0.921 data_s:0.767 smp/s:38 mem_gb:39.65
+Training:  14%|█▍        | 8600/60000 [3:52:23<24:03:08,  1.68s/step]INFO 2026-07-15 05:07:24 ot_train.py:649 step:9K smpl:550K ep:12K epch:2.52 loss:0.083 grdn:0.343 lr:9.9e-05 updt_s:0.906 data_s:0.781 smp/s:38 mem_gb:39.65
+Training:  15%|█▍        | 8800/60000 [3:57:58<23:17:35,  1.64s/step]INFO 2026-07-15 05:12:59 ot_train.py:649 step:9K smpl:563K ep:13K epch:2.58 loss:0.084 grdn:0.336 lr:9.9e-05 updt_s:0.905 data_s:0.766 smp/s:38 mem_gb:39.64
+Training:  15%|█▌        | 9000/60000 [4:03:31<21:07:31,  1.49s/step]INFO 2026-07-15 05:18:32 ot_train.py:649 step:9K smpl:576K ep:13K epch:2.64 loss:0.081 grdn:0.331 lr:9.9e-05 updt_s:0.917 data_s:0.743 smp/s:39 mem_gb:39.65
+Training:  15%|█▌        | 9200/60000 [4:09:07<25:20:25,  1.80s/step]INFO 2026-07-15 05:24:07 ot_train.py:649 step:9K smpl:589K ep:13K epch:2.70 loss:0.084 grdn:0.379 lr:9.9e-05 updt_s:0.894 data_s:0.780 smp/s:38 mem_gb:39.63
+Training:  16%|█▌        | 9400/60000 [4:14:39<23:37:47,  1.68s/step]INFO 2026-07-15 05:29:39 ot_train.py:649 step:9K smpl:602K ep:14K epch:2.75 loss:0.086 grdn:0.377 lr:9.9e-05 updt_s:0.887 data_s:0.767 smp/s:39 mem_gb:39.65
+Training:  16%|█▌        | 9600/60000 [4:20:14<23:28:17,  1.68s/step]INFO 2026-07-15 05:35:15 ot_train.py:649 step:10K smpl:614K ep:14K epch:2.81 loss:0.082 grdn:0.334 lr:9.9e-05 updt_s:0.901 data_s:0.773 smp/s:38 mem_gb:39.65
+Training:  16%|█▋        | 9800/60000 [4:25:54<22:54:59,  1.64s/step]INFO 2026-07-15 05:40:55 ot_train.py:649 step:10K smpl:627K ep:14K epch:2.87 loss:0.080 grdn:0.326 lr:9.9e-05 updt_s:0.914 data_s:0.780 smp/s:38 mem_gb:39.65
+Training:  17%|█▋        | 10000/60000 [4:31:31<23:00:39,  1.66s/step]INFO 2026-07-15 05:46:31 ot_train.py:649 step:10K smpl:640K ep:14K epch:2.93 loss:0.077 grdn:0.322 lr:9.9e-05 updt_s:0.909 data_s:0.770 smp/s:38 mem_gb:39.65
+Training:  17%|█▋        | 10200/60000 [4:37:08<24:14:57,  1.75s/step]INFO 2026-07-15 05:52:08 ot_train.py:649 step:10K smpl:653K ep:15K epch:2.99 loss:0.078 grdn:0.321 lr:9.9e-05 updt_s:0.916 data_s:0.763 smp/s:38 mem_gb:39.65
+Training:  17%|█▋        | 10400/60000 [4:42:38<22:46:01,  1.65s/step]INFO 2026-07-15 05:57:38 ot_train.py:649 step:10K smpl:666K ep:15K epch:3.05 loss:0.078 grdn:0.321 lr:9.8e-05 updt_s:0.944 data_s:0.701 smp/s:39 mem_gb:39.65
+Training:  18%|█▊        | 10600/60000 [4:48:10<23:12:41,  1.69s/step]INFO 2026-07-15 06:03:10 ot_train.py:649 step:11K smpl:678K ep:15K epch:3.11 loss:0.078 grdn:0.328 lr:9.8e-05 updt_s:0.915 data_s:0.739 smp/s:39 mem_gb:39.65
+Training:  18%|█▊        | 10800/60000 [4:53:42<22:32:03,  1.65s/step]INFO 2026-07-15 06:08:43 ot_train.py:649 step:11K smpl:691K ep:16K epch:3.17 loss:0.080 grdn:0.335 lr:9.8e-05 updt_s:0.888 data_s:0.771 smp/s:39 mem_gb:39.65
+Training:  18%|█▊        | 11000/60000 [4:59:14<22:51:50,  1.68s/step]INFO 2026-07-15 06:14:15 ot_train.py:649 step:11K smpl:704K ep:16K epch:3.22 loss:0.076 grdn:0.337 lr:9.8e-05 updt_s:0.892 data_s:0.764 smp/s:39 mem_gb:39.65
+Training:  19%|█▊        | 11200/60000 [5:04:53<22:29:22,  1.66s/step]INFO 2026-07-15 06:19:53 ot_train.py:649 step:11K smpl:717K ep:16K epch:3.28 loss:0.076 grdn:0.318 lr:9.8e-05 updt_s:0.913 data_s:0.775 smp/s:38 mem_gb:39.65
+Training:  19%|█▉        | 11400/60000 [5:10:28<22:43:38,  1.68s/step]INFO 2026-07-15 06:25:29 ot_train.py:649 step:11K smpl:730K ep:16K epch:3.34 loss:0.078 grdn:0.324 lr:9.8e-05 updt_s:0.888 data_s:0.782 smp/s:38 mem_gb:39.63
+Training:  19%|█▉        | 11600/60000 [5:15:58<21:21:23,  1.59s/step]INFO 2026-07-15 06:30:59 ot_train.py:649 step:12K smpl:742K ep:17K epch:3.40 loss:0.075 grdn:0.322 lr:9.7e-05 updt_s:0.947 data_s:0.700 smp/s:39 mem_gb:39.65
+Training:  20%|█▉        | 11800/60000 [5:21:14<21:16:14,  1.59s/step]INFO 2026-07-15 06:36:15 ot_train.py:649 step:12K smpl:755K ep:17K epch:3.46 loss:0.077 grdn:0.333 lr:9.7e-05 updt_s:0.986 data_s:0.588 smp/s:41 mem_gb:39.65
+Training:  20%|██        | 12000/60000 [5:26:27<21:17:46,  1.60s/step]INFO 2026-07-15 06:41:28 ot_train.py:649 step:12K smpl:768K ep:17K epch:3.52 loss:0.074 grdn:0.329 lr:9.7e-05 updt_s:0.982 data_s:0.580 smp/s:41 mem_gb:39.65
+Training:  20%|██        | 12200/60000 [5:31:39<19:50:46,  1.49s/step]INFO 2026-07-15 06:46:39 ot_train.py:649 step:12K smpl:781K ep:18K epch:3.58 loss:0.075 grdn:0.330 lr:9.7e-05 updt_s:0.974 data_s:0.578 smp/s:41 mem_gb:39.65
+Training:  21%|██        | 12400/60000 [5:36:48<19:14:09,  1.45s/step]INFO 2026-07-15 06:51:49 ot_train.py:649 step:12K smpl:794K ep:18K epch:3.63 loss:0.074 grdn:0.313 lr:9.7e-05 updt_s:0.963 data_s:0.581 smp/s:41 mem_gb:39.65
+Training:  21%|██        | 12600/60000 [5:41:57<18:45:17,  1.42s/step]INFO 2026-07-15 06:56:58 ot_train.py:649 step:13K smpl:806K ep:18K epch:3.69 loss:0.074 grdn:0.307 lr:9.6e-05 updt_s:0.954 data_s:0.585 smp/s:42 mem_gb:39.65
+Training:  21%|██▏       | 12800/60000 [5:47:06<20:03:30,  1.53s/step]INFO 2026-07-15 07:02:07 ot_train.py:649 step:13K smpl:819K ep:18K epch:3.75 loss:0.074 grdn:0.307 lr:9.6e-05 updt_s:0.958 data_s:0.582 smp/s:42 mem_gb:39.65
+Training:  22%|██▏       | 13000/60000 [5:52:17<21:29:51,  1.65s/step]INFO 2026-07-15 07:07:18 ot_train.py:649 step:13K smpl:832K ep:19K epch:3.81 loss:0.072 grdn:0.296 lr:9.6e-05 updt_s:0.972 data_s:0.578 smp/s:41 mem_gb:39.65
+Training:  22%|██▏       | 13200/60000 [5:57:32<19:53:07,  1.53s/step]INFO 2026-07-15 07:12:33 ot_train.py:649 step:13K smpl:845K ep:19K epch:3.87 loss:0.071 grdn:0.295 lr:9.6e-05 updt_s:0.986 data_s:0.584 smp/s:41 mem_gb:39.65
+Training:  22%|██▏       | 13400/60000 [6:02:45<19:34:18,  1.51s/step]INFO 2026-07-15 07:17:45 ot_train.py:649 step:13K smpl:858K ep:19K epch:3.93 loss:0.073 grdn:0.300 lr:9.6e-05 updt_s:0.976 data_s:0.583 smp/s:41 mem_gb:39.65
+Training:  23%|██▎       | 13600/60000 [6:07:57<20:34:15,  1.60s/step]INFO 2026-07-15 07:22:57 ot_train.py:649 step:14K smpl:870K ep:20K epch:3.99 loss:0.072 grdn:0.314 lr:9.5e-05 updt_s:0.968 data_s:0.586 smp/s:41 mem_gb:39.63
+Training:  23%|██▎       | 13800/60000 [6:13:16<21:20:50,  1.66s/step]INFO 2026-07-15 07:28:16 ot_train.py:649 step:14K smpl:883K ep:20K epch:4.04 loss:0.071 grdn:0.299 lr:9.5e-05 updt_s:0.977 data_s:0.616 smp/s:40 mem_gb:39.65
+Training:  23%|██▎       | 14000/60000 [6:18:50<21:26:38,  1.68s/step]INFO 2026-07-15 07:33:50 ot_train.py:649 step:14K smpl:896K ep:20K epch:4.10 loss:0.070 grdn:0.306 lr:9.5e-05 updt_s:0.889 data_s:0.775 smp/s:38 mem_gb:39.65
+Training:  24%|██▎       | 14200/60000 [6:24:29<21:37:37,  1.70s/step]INFO 2026-07-15 07:39:30 ot_train.py:649 step:14K smpl:909K ep:21K epch:4.16 loss:0.070 grdn:0.299 lr:9.5e-05 updt_s:0.906 data_s:0.787 smp/s:38 mem_gb:39.65
+Training:  24%|██▍       | 14400/60000 [6:30:02<21:12:34,  1.67s/step]INFO 2026-07-15 07:45:03 ot_train.py:649 step:14K smpl:922K ep:21K epch:4.22 loss:0.070 grdn:0.282 lr:9.4e-05 updt_s:0.891 data_s:0.769 smp/s:39 mem_gb:39.65
+Training:  24%|██▍       | 14600/60000 [6:35:38<21:03:54,  1.67s/step]INFO 2026-07-15 07:50:39 ot_train.py:649 step:15K smpl:934K ep:21K epch:4.28 loss:0.070 grdn:0.308 lr:9.4e-05 updt_s:0.881 data_s:0.793 smp/s:38 mem_gb:39.65
+Training:  25%|██▍       | 14800/60000 [6:41:15<20:02:20,  1.60s/step]INFO 2026-07-15 07:56:15 ot_train.py:649 step:15K smpl:947K ep:21K epch:4.34 loss:0.071 grdn:0.299 lr:9.4e-05 updt_s:0.888 data_s:0.788 smp/s:38 mem_gb:39.64
+Training:  25%|██▌       | 15000/60000 [6:46:50<21:03:54,  1.69s/step]INFO 2026-07-15 08:01:50 ot_train.py:649 step:15K smpl:960K ep:22K epch:4.40 loss:0.071 grdn:0.322 lr:9.3e-05 updt_s:0.892 data_s:0.780 smp/s:38 mem_gb:39.65
+Training:  25%|██▌       | 15200/60000 [6:52:28<21:02:19,  1.69s/step]INFO 2026-07-15 08:07:29 ot_train.py:649 step:15K smpl:973K ep:22K epch:4.45 loss:0.071 grdn:0.316 lr:9.3e-05 updt_s:0.902 data_s:0.785 smp/s:38 mem_gb:39.65
+Training:  26%|██▌       | 15400/60000 [6:58:02<20:36:48,  1.66s/step]INFO 2026-07-15 08:13:02 ot_train.py:649 step:15K smpl:986K ep:22K epch:4.51 loss:0.069 grdn:0.309 lr:9.3e-05 updt_s:0.879 data_s:0.783 smp/s:38 mem_gb:39.65
+Training:  26%|██▌       | 15600/60000 [7:03:37<20:47:22,  1.69s/step]INFO 2026-07-15 08:18:37 ot_train.py:649 step:16K smpl:998K ep:23K epch:4.57 loss:0.072 grdn:0.335 lr:9.3e-05 updt_s:0.881 data_s:0.791 smp/s:38 mem_gb:39.65
+Training:  26%|██▋       | 15800/60000 [7:09:12<26:23:47,  2.15s/step]INFO 2026-07-15 08:24:12 ot_train.py:649 step:16K smpl:1M ep:23K epch:4.63 loss:0.068 grdn:0.300 lr:9.2e-05 updt_s:0.871 data_s:0.798 smp/s:38 mem_gb:39.63
+Training:  27%|██▋       | 16000/60000 [7:14:46<20:01:54,  1.64s/step]INFO 2026-07-15 08:29:47 ot_train.py:649 step:16K smpl:1M ep:23K epch:4.69 loss:0.068 grdn:0.293 lr:9.2e-05 updt_s:0.873 data_s:0.794 smp/s:38 mem_gb:39.65
+Training:  27%|██▋       | 16200/60000 [7:20:24<20:17:45,  1.67s/step]INFO 2026-07-15 08:35:24 ot_train.py:649 step:16K smpl:1M ep:23K epch:4.75 loss:0.069 grdn:0.312 lr:9.2e-05 updt_s:0.883 data_s:0.800 smp/s:38 mem_gb:39.65
+Training:  27%|██▋       | 16400/60000 [7:26:03<20:11:57,  1.67s/step]INFO 2026-07-15 08:41:03 ot_train.py:649 step:16K smpl:1M ep:24K epch:4.81 loss:0.067 grdn:0.288 lr:9.1e-05 updt_s:0.899 data_s:0.790 smp/s:38 mem_gb:39.65
+Training:  28%|██▊       | 16600/60000 [7:31:39<20:03:40,  1.66s/step]INFO 2026-07-15 08:46:40 ot_train.py:649 step:17K smpl:1M ep:24K epch:4.87 loss:0.065 grdn:0.283 lr:9.1e-05 updt_s:0.888 data_s:0.791 smp/s:38 mem_gb:39.65
+Training:  28%|██▊       | 16800/60000 [7:37:16<19:40:27,  1.64s/step]INFO 2026-07-15 08:52:16 ot_train.py:649 step:17K smpl:1M ep:24K epch:4.92 loss:0.067 grdn:0.299 lr:9.1e-05 updt_s:0.888 data_s:0.789 smp/s:38 mem_gb:39.65
+Training:  28%|██▊       | 17000/60000 [7:42:50<20:17:43,  1.70s/step]INFO 2026-07-15 08:57:51 ot_train.py:649 step:17K smpl:1M ep:25K epch:4.98 loss:0.065 grdn:0.289 lr:9.0e-05 updt_s:0.877 data_s:0.791 smp/s:38 mem_gb:39.65
+Training:  29%|██▊       | 17200/60000 [7:48:25<20:13:04,  1.70s/step]INFO 2026-07-15 09:03:26 ot_train.py:649 step:17K smpl:1M ep:25K epch:5.04 loss:0.065 grdn:0.287 lr:9.0e-05 updt_s:0.886 data_s:0.784 smp/s:38 mem_gb:39.65
+Training:  29%|██▉       | 17400/60000 [7:53:57<18:59:10,  1.60s/step]INFO 2026-07-15 09:08:58 ot_train.py:649 step:17K smpl:1M ep:25K epch:5.10 loss:0.065 grdn:0.283 lr:9.0e-05 updt_s:0.878 data_s:0.778 smp/s:39 mem_gb:39.65
+Training:  29%|██▉       | 17600/60000 [7:59:33<20:48:12,  1.77s/step]INFO 2026-07-15 09:14:33 ot_train.py:649 step:18K smpl:1M ep:25K epch:5.16 loss:0.064 grdn:0.280 lr:8.9e-05 updt_s:0.893 data_s:0.778 smp/s:38 mem_gb:39.65
+Training:  30%|██▉       | 17800/60000 [8:05:05<19:23:52,  1.65s/step]INFO 2026-07-15 09:20:05 ot_train.py:649 step:18K smpl:1M ep:26K epch:5.22 loss:0.066 grdn:0.293 lr:8.9e-05 updt_s:0.863 data_s:0.793 smp/s:39 mem_gb:39.65
+Training:  30%|███       | 18000/60000 [8:10:42<19:33:02,  1.68s/step]INFO 2026-07-15 09:25:42 ot_train.py:649 step:18K smpl:1M ep:26K epch:5.28 loss:0.064 grdn:0.276 lr:8.8e-05 updt_s:0.881 data_s:0.798 smp/s:38 mem_gb:39.65
+Training:  30%|███       | 18200/60000 [8:16:17<19:24:20,  1.67s/step]INFO 2026-07-15 09:31:18 ot_train.py:649 step:18K smpl:1M ep:26K epch:5.33 loss:0.064 grdn:0.275 lr:8.8e-05 updt_s:0.874 data_s:0.799 smp/s:38 mem_gb:39.63
+Training:  31%|███       | 18400/60000 [8:21:54<18:19:20,  1.59s/step]INFO 2026-07-15 09:36:54 ot_train.py:649 step:18K smpl:1M ep:27K epch:5.39 loss:0.062 grdn:0.273 lr:8.8e-05 updt_s:0.881 data_s:0.796 smp/s:38 mem_gb:39.65
+Training:  31%|███       | 18600/60000 [8:27:30<19:21:28,  1.68s/step]INFO 2026-07-15 09:42:30 ot_train.py:649 step:19K smpl:1M ep:27K epch:5.45 loss:0.063 grdn:0.279 lr:8.7e-05 updt_s:0.879 data_s:0.795 smp/s:38 mem_gb:39.65
+Training:  31%|███▏      | 18800/60000 [8:33:09<19:58:31,  1.75s/step]INFO 2026-07-15 09:48:09 ot_train.py:649 step:19K smpl:1M ep:27K epch:5.51 loss:0.065 grdn:0.321 lr:8.7e-05 updt_s:0.893 data_s:0.797 smp/s:38 mem_gb:39.65
+Training:  32%|███▏      | 19000/60000 [8:37:57<16:03:07,  1.41s/step]INFO 2026-07-15 09:52:57 ot_train.py:649 step:19K smpl:1M ep:27K epch:5.57 loss:0.064 grdn:0.279 lr:8.7e-05 updt_s:0.814 data_s:0.621 smp/s:45 mem_gb:39.65
+Training:  32%|███▏      | 19200/60000 [8:42:42<15:50:58,  1.40s/step]INFO 2026-07-15 09:57:42 ot_train.py:649 step:19K smpl:1M ep:28K epch:5.63 loss:0.064 grdn:0.282 lr:8.6e-05 updt_s:0.810 data_s:0.612 smp/s:45 mem_gb:39.65
+Training:  32%|███▏      | 19400/60000 [8:47:13<15:03:45,  1.34s/step]INFO 2026-07-15 10:02:13 ot_train.py:649 step:19K smpl:1M ep:28K epch:5.69 loss:0.061 grdn:0.269 lr:8.6e-05 updt_s:0.801 data_s:0.549 smp/s:47 mem_gb:39.65
+Training:  33%|███▎      | 19600/60000 [8:51:41<14:50:00,  1.32s/step]INFO 2026-07-15 10:06:41 ot_train.py:649 step:20K smpl:1M ep:28K epch:5.74 loss:0.062 grdn:0.273 lr:8.5e-05 updt_s:0.803 data_s:0.534 smp/s:48 mem_gb:39.65
+Training:  33%|███▎      | 19800/60000 [8:56:08<14:47:32,  1.32s/step]INFO 2026-07-15 10:11:09 ot_train.py:649 step:20K smpl:1M ep:29K epch:5.80 loss:0.061 grdn:0.279 lr:8.5e-05 updt_s:0.799 data_s:0.535 smp/s:48 mem_gb:39.65
+Training:  33%|███▎      | 20000/60000 [9:00:36<14:51:45,  1.34s/step]INFO 2026-07-15 10:15:37 ot_train.py:649 step:20K smpl:1M ep:29K epch:5.86 loss:0.063 grdn:0.285 lr:8.5e-05 updt_s:0.802 data_s:0.535 smp/s:48 mem_gb:39.65
+Training:  34%|███▎      | 20200/60000 [9:05:07<14:42:18,  1.33s/step]INFO 2026-07-15 10:20:07 ot_train.py:649 step:20K smpl:1M ep:29K epch:5.92 loss:0.061 grdn:0.271 lr:8.4e-05 updt_s:0.802 data_s:0.545 smp/s:48 mem_gb:39.65
+Training:  34%|███▍      | 20400/60000 [9:09:39<14:47:23,  1.34s/step]INFO 2026-07-15 10:24:39 ot_train.py:649 step:20K smpl:1M ep:29K epch:5.98 loss:0.059 grdn:0.260 lr:8.4e-05 updt_s:0.813 data_s:0.545 smp/s:47 mem_gb:39.63
+Training:  34%|███▍      | 20600/60000 [9:14:20<15:22:48,  1.41s/step]INFO 2026-07-15 10:29:21 ot_train.py:649 step:21K smpl:1M ep:30K epch:6.04 loss:0.060 grdn:0.267 lr:8.3e-05 updt_s:0.852 data_s:0.550 smp/s:46 mem_gb:39.65
+Training:  35%|███▍      | 20800/60000 [9:18:51<14:42:18,  1.35s/step]INFO 2026-07-15 10:33:51 ot_train.py:649 step:21K smpl:1M ep:30K epch:6.10 loss:0.062 grdn:0.290 lr:8.3e-05 updt_s:0.809 data_s:0.539 smp/s:47 mem_gb:39.65
+Training:  35%|███▌      | 21000/60000 [9:23:20<14:31:23,  1.34s/step]INFO 2026-07-15 10:38:21 ot_train.py:649 step:21K smpl:1M ep:30K epch:6.15 loss:0.060 grdn:0.282 lr:8.2e-05 updt_s:0.807 data_s:0.537 smp/s:48 mem_gb:39.65
+Training:  35%|███▌      | 21200/60000 [9:27:50<14:25:41,  1.34s/step]INFO 2026-07-15 10:42:51 ot_train.py:649 step:21K smpl:1M ep:31K epch:6.21 loss:0.061 grdn:0.290 lr:8.2e-05 updt_s:0.810 data_s:0.537 smp/s:48 mem_gb:39.65
+Training:  36%|███▌      | 21400/60000 [9:32:20<14:22:03,  1.34s/step]INFO 2026-07-15 10:47:20 ot_train.py:649 step:21K smpl:1M ep:31K epch:6.27 loss:0.059 grdn:0.264 lr:8.1e-05 updt_s:0.801 data_s:0.543 smp/s:48 mem_gb:39.65
+Training:  36%|███▌      | 21600/60000 [9:36:50<14:07:18,  1.32s/step]INFO 2026-07-15 10:51:51 ot_train.py:649 step:22K smpl:1M ep:31K epch:6.33 loss:0.060 grdn:0.271 lr:8.1e-05 updt_s:0.801 data_s:0.547 smp/s:47 mem_gb:39.65
+Training:  36%|███▋      | 21800/60000 [9:41:20<13:51:06,  1.31s/step]INFO 2026-07-15 10:56:21 ot_train.py:649 step:22K smpl:1M ep:31K epch:6.39 loss:0.059 grdn:0.271 lr:8.1e-05 updt_s:0.807 data_s:0.539 smp/s:48 mem_gb:39.65
+Training:  37%|███▋      | 22000/60000 [9:46:06<15:11:06,  1.44s/step]INFO 2026-07-15 11:01:07 ot_train.py:649 step:22K smpl:1M ep:32K epch:6.45 loss:0.060 grdn:0.282 lr:8.0e-05 updt_s:0.816 data_s:0.608 smp/s:45 mem_gb:39.65
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+Training:  41%|████      | 24600/60000 [10:45:18<13:12:07,  1.34s/step]INFO 2026-07-15 12:00:18 ot_train.py:649 step:25K smpl:2M ep:36K epch:7.21 loss:0.056 grdn:0.262 lr:7.4e-05 updt_s:0.799 data_s:0.546 smp/s:48 mem_gb:39.65
+Training:  41%|████▏     | 24800/60000 [10:49:49<13:39:05,  1.40s/step]INFO 2026-07-15 12:04:50 ot_train.py:649 step:25K smpl:2M ep:36K epch:7.27 loss:0.055 grdn:0.258 lr:7.3e-05 updt_s:0.805 data_s:0.548 smp/s:47 mem_gb:39.65
+Training:  42%|████▏     | 25000/60000 [10:54:20<13:03:59,  1.34s/step]INFO 2026-07-15 12:09:20 ot_train.py:649 step:25K smpl:2M ep:36K epch:7.33 loss:0.055 grdn:0.270 lr:7.3e-05 updt_s:0.778 data_s:0.570 smp/s:47 mem_gb:39.63
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+Training:  42%|████▏     | 25400/60000 [11:03:21<12:50:26,  1.34s/step]INFO 2026-07-15 12:18:22 ot_train.py:649 step:25K smpl:2M ep:37K epch:7.44 loss:0.052 grdn:0.253 lr:7.2e-05 updt_s:0.804 data_s:0.537 smp/s:48 mem_gb:39.65
+Training:  43%|████▎     | 25600/60000 [11:07:57<13:50:54,  1.45s/step]INFO 2026-07-15 12:22:57 ot_train.py:649 step:26K smpl:2M ep:37K epch:7.50 loss:0.054 grdn:0.276 lr:7.1e-05 updt_s:0.810 data_s:0.561 smp/s:47 mem_gb:39.65
+Training:  43%|████▎     | 25800/60000 [11:12:43<12:37:23,  1.33s/step]INFO 2026-07-15 12:27:44 ot_train.py:649 step:26K smpl:2M ep:37K epch:7.56 loss:0.055 grdn:0.287 lr:7.1e-05 updt_s:0.815 data_s:0.614 smp/s:45 mem_gb:39.65
+Training:  43%|████▎     | 26000/60000 [11:17:15<12:38:36,  1.34s/step]INFO 2026-07-15 12:32:15 ot_train.py:649 step:26K smpl:2M ep:38K epch:7.62 loss:0.053 grdn:0.266 lr:7.0e-05 updt_s:0.800 data_s:0.552 smp/s:47 mem_gb:39.65
+Training:  44%|████▎     | 26200/60000 [11:21:44<12:28:46,  1.33s/step]INFO 2026-07-15 12:36:45 ot_train.py:649 step:26K smpl:2M ep:38K epch:7.68 loss:0.054 grdn:0.269 lr:7.0e-05 updt_s:0.798 data_s:0.546 smp/s:48 mem_gb:39.65
+Training:  44%|████▍     | 26400/60000 [11:26:15<12:55:37,  1.39s/step]INFO 2026-07-15 12:41:16 ot_train.py:649 step:26K smpl:2M ep:38K epch:7.74 loss:0.053 grdn:0.270 lr:6.9e-05 updt_s:0.810 data_s:0.541 smp/s:47 mem_gb:39.64
+Training:  44%|████▍     | 26600/60000 [11:30:46<12:17:32,  1.32s/step]INFO 2026-07-15 12:45:46 ot_train.py:649 step:27K smpl:2M ep:38K epch:7.80 loss:0.053 grdn:0.270 lr:6.8e-05 updt_s:0.804 data_s:0.544 smp/s:47 mem_gb:39.65
+Training:  45%|████▍     | 26800/60000 [11:35:23<14:29:56,  1.57s/step]INFO 2026-07-15 12:50:23 ot_train.py:649 step:27K smpl:2M ep:39K epch:7.85 loss:0.054 grdn:0.284 lr:6.8e-05 updt_s:0.829 data_s:0.553 smp/s:46 mem_gb:39.65
+Training:  45%|████▌     | 27000/60000 [11:40:17<13:10:36,  1.44s/step]INFO 2026-07-15 12:55:17 ot_train.py:649 step:27K smpl:2M ep:39K epch:7.91 loss:0.052 grdn:0.266 lr:6.7e-05 updt_s:0.853 data_s:0.613 smp/s:44 mem_gb:39.65
+Training:  45%|████▌     | 27200/60000 [11:44:57<12:19:03,  1.35s/step]INFO 2026-07-15 12:59:57 ot_train.py:649 step:27K smpl:2M ep:39K epch:7.97 loss:0.053 grdn:0.293 lr:6.7e-05 updt_s:0.828 data_s:0.570 smp/s:46 mem_gb:39.63
+Training:  46%|████▌     | 27400/60000 [11:49:28<12:03:57,  1.33s/step]INFO 2026-07-15 13:04:28 ot_train.py:649 step:27K smpl:2M ep:40K epch:8.03 loss:0.052 grdn:0.274 lr:6.6e-05 updt_s:0.802 data_s:0.548 smp/s:47 mem_gb:39.65
+Training:  46%|████▌     | 27600/60000 [11:53:57<11:56:14,  1.33s/step]INFO 2026-07-15 13:08:57 ot_train.py:649 step:28K smpl:2M ep:40K epch:8.09 loss:0.050 grdn:0.261 lr:6.6e-05 updt_s:0.800 data_s:0.540 smp/s:48 mem_gb:39.65
+Training:  46%|████▋     | 27800/60000 [11:58:26<11:54:24,  1.33s/step]INFO 2026-07-15 13:13:26 ot_train.py:649 step:28K smpl:2M ep:40K epch:8.15 loss:0.051 grdn:0.284 lr:6.5e-05 updt_s:0.801 data_s:0.542 smp/s:48 mem_gb:39.65
+Training:  47%|████▋     | 28000/60000 [12:02:55<12:06:08,  1.36s/step]INFO 2026-07-15 13:17:56 ot_train.py:649 step:28K smpl:2M ep:40K epch:8.21 loss:0.051 grdn:0.277 lr:6.5e-05 updt_s:0.799 data_s:0.545 smp/s:48 mem_gb:39.65
+Training:  47%|████▋     | 28200/60000 [12:07:29<12:42:52,  1.44s/step]INFO 2026-07-15 13:22:30 ot_train.py:649 step:28K smpl:2M ep:41K epch:8.26 loss:0.051 grdn:0.272 lr:6.4e-05 updt_s:0.818 data_s:0.547 smp/s:47 mem_gb:39.65
+Training:  47%|████▋     | 28400/60000 [12:11:59<11:39:53,  1.33s/step]INFO 2026-07-15 13:26:59 ot_train.py:649 step:28K smpl:2M ep:41K epch:8.32 loss:0.050 grdn:0.263 lr:6.4e-05 updt_s:0.806 data_s:0.539 smp/s:48 mem_gb:39.65
+Training:  48%|████▊     | 28600/60000 [12:16:37<12:36:49,  1.45s/step]INFO 2026-07-15 13:31:37 ot_train.py:649 step:29K smpl:2M ep:41K epch:8.38 loss:0.051 grdn:0.258 lr:6.3e-05 updt_s:0.826 data_s:0.560 smp/s:46 mem_gb:39.65
+Training:  48%|████▊     | 28800/60000 [12:21:42<13:28:51,  1.56s/step]INFO 2026-07-15 13:36:42 ot_train.py:649 step:29K smpl:2M ep:42K epch:8.44 loss:0.050 grdn:0.272 lr:6.2e-05 updt_s:0.866 data_s:0.655 smp/s:42 mem_gb:39.65
+Training:  48%|████▊     | 29000/60000 [12:27:13<13:55:32,  1.62s/step]INFO 2026-07-15 13:42:13 ot_train.py:649 step:29K smpl:2M ep:42K epch:8.50 loss:0.050 grdn:0.275 lr:6.2e-05 updt_s:0.916 data_s:0.731 smp/s:39 mem_gb:39.65
+Training:  49%|████▊     | 29200/60000 [12:32:43<12:22:14,  1.45s/step]INFO 2026-07-15 13:47:43 ot_train.py:649 step:29K smpl:2M ep:42K epch:8.56 loss:0.050 grdn:0.274 lr:6.1e-05 updt_s:0.876 data_s:0.768 smp/s:39 mem_gb:39.65
+Training:  49%|████▉     | 29400/60000 [12:37:22<11:17:56,  1.33s/step]INFO 2026-07-15 13:52:23 ot_train.py:649 step:29K smpl:2M ep:42K epch:8.62 loss:0.050 grdn:0.279 lr:6.1e-05 updt_s:0.806 data_s:0.588 smp/s:46 mem_gb:39.63
+Training:  49%|████▉     | 29600/60000 [12:41:52<11:13:16,  1.33s/step]INFO 2026-07-15 13:56:53 ot_train.py:649 step:30K smpl:2M ep:43K epch:8.68 loss:0.050 grdn:0.275 lr:6.0e-05 updt_s:0.809 data_s:0.537 smp/s:48 mem_gb:39.65
+Training:  50%|████▉     | 29800/60000 [12:46:20<11:19:38,  1.35s/step]INFO 2026-07-15 14:01:20 ot_train.py:649 step:30K smpl:2M ep:43K epch:8.73 loss:0.048 grdn:0.273 lr:6.0e-05 updt_s:0.801 data_s:0.533 smp/s:48 mem_gb:39.65
+Training:  50%|█████     | 30000/60000 [12:50:49<12:15:53,  1.47s/step]INFO 2026-07-15 14:05:49 ot_train.py:649 step:30K smpl:2M ep:43K epch:8.79 loss:0.048 grdn:0.270 lr:5.9e-05 updt_s:0.806 data_s:0.535 smp/s:48 mem_gb:39.65
+Training:  50%|█████     | 30200/60000 [12:55:22<11:56:13,  1.44s/step]INFO 2026-07-15 14:10:22 ot_train.py:649 step:30K smpl:2M ep:44K epch:8.85 loss:0.048 grdn:0.273 lr:5.8e-05 updt_s:0.811 data_s:0.551 smp/s:47 mem_gb:39.65
+Training:  51%|█████     | 30400/60000 [13:00:01<12:18:10,  1.50s/step]INFO 2026-07-15 14:15:02 ot_train.py:649 step:30K smpl:2M ep:44K epch:8.91 loss:0.046 grdn:0.256 lr:5.8e-05 updt_s:0.840 data_s:0.554 smp/s:46 mem_gb:39.65
+Training:  51%|█████     | 30600/60000 [13:05:08<11:42:33,  1.43s/step]INFO 2026-07-15 14:20:08 ot_train.py:649 step:31K smpl:2M ep:44K epch:8.97 loss:0.047 grdn:0.260 lr:5.7e-05 updt_s:0.897 data_s:0.630 smp/s:42 mem_gb:39.65
+Training:  51%|█████▏    | 30800/60000 [13:10:31<12:57:37,  1.60s/step]INFO 2026-07-15 14:25:31 ot_train.py:649 step:31K smpl:2M ep:45K epch:9.03 loss:0.047 grdn:0.273 lr:5.7e-05 updt_s:0.909 data_s:0.702 smp/s:40 mem_gb:39.65
+Training:  52%|█████▏    | 31000/60000 [13:15:59<13:18:56,  1.65s/step]INFO 2026-07-15 14:30:59 ot_train.py:649 step:31K smpl:2M ep:45K epch:9.09 loss:0.047 grdn:0.264 lr:5.6e-05 updt_s:0.909 data_s:0.726 smp/s:39 mem_gb:39.65
+Training:  52%|█████▏    | 31200/60000 [13:21:20<11:55:33,  1.49s/step]INFO 2026-07-15 14:36:21 ot_train.py:649 step:31K smpl:2M ep:45K epch:9.14 loss:0.046 grdn:0.265 lr:5.6e-05 updt_s:0.901 data_s:0.704 smp/s:40 mem_gb:39.65
+Training:  52%|█████▏    | 31400/60000 [13:26:38<12:25:35,  1.56s/step]INFO 2026-07-15 14:41:38 ot_train.py:649 step:31K smpl:2M ep:45K epch:9.20 loss:0.049 grdn:0.291 lr:5.5e-05 updt_s:0.927 data_s:0.656 smp/s:40 mem_gb:39.65
+Training:  53%|█████▎    | 31600/60000 [13:32:05<15:29:36,  1.96s/step]INFO 2026-07-15 14:47:06 ot_train.py:649 step:32K smpl:2M ep:46K epch:9.26 loss:0.048 grdn:0.272 lr:5.4e-05 updt_s:0.893 data_s:0.739 smp/s:39 mem_gb:39.63
+Training:  53%|█████▎    | 31800/60000 [13:37:36<12:42:22,  1.62s/step]INFO 2026-07-15 14:52:36 ot_train.py:649 step:32K smpl:2M ep:46K epch:9.32 loss:0.045 grdn:0.267 lr:5.4e-05 updt_s:0.886 data_s:0.761 smp/s:39 mem_gb:39.65
+Training:  53%|█████▎    | 32000/60000 [13:43:08<12:59:36,  1.67s/step]INFO 2026-07-15 14:58:09 ot_train.py:649 step:32K smpl:2M ep:46K epch:9.38 loss:0.045 grdn:0.264 lr:5.3e-05 updt_s:0.891 data_s:0.769 smp/s:39 mem_gb:39.65
+Training:  54%|█████▎    | 32200/60000 [13:48:42<12:49:20,  1.66s/step]INFO 2026-07-15 15:03:42 ot_train.py:649 step:32K smpl:2M ep:47K epch:9.44 loss:0.045 grdn:0.266 lr:5.3e-05 updt_s:0.894 data_s:0.767 smp/s:39 mem_gb:39.65
+Training:  54%|█████▍    | 32400/60000 [13:54:14<13:26:32,  1.75s/step]INFO 2026-07-15 15:09:15 ot_train.py:649 step:32K smpl:2M ep:47K epch:9.50 loss:0.045 grdn:0.270 lr:5.2e-05 updt_s:0.886 data_s:0.771 smp/s:39 mem_gb:39.65
+Training:  54%|█████▍    | 32600/60000 [13:59:44<12:05:42,  1.59s/step]INFO 2026-07-15 15:14:44 ot_train.py:649 step:33K smpl:2M ep:47K epch:9.55 loss:0.045 grdn:0.264 lr:5.1e-05 updt_s:0.891 data_s:0.753 smp/s:39 mem_gb:39.65
+Training:  55%|█████▍    | 32800/60000 [14:05:16<13:15:31,  1.75s/step]INFO 2026-07-15 15:20:16 ot_train.py:649 step:33K smpl:2M ep:47K epch:9.61 loss:0.046 grdn:0.276 lr:5.1e-05 updt_s:0.907 data_s:0.747 smp/s:39 mem_gb:39.65
+Training:  55%|█████▌    | 33000/60000 [14:10:46<12:09:18,  1.62s/step]INFO 2026-07-15 15:25:47 ot_train.py:649 step:33K smpl:2M ep:48K epch:9.67 loss:0.045 grdn:0.270 lr:5.0e-05 updt_s:0.884 data_s:0.765 smp/s:39 mem_gb:39.65
+Training:  55%|█████▌    | 33200/60000 [14:16:19<12:15:16,  1.65s/step]INFO 2026-07-15 15:31:19 ot_train.py:649 step:33K smpl:2M ep:48K epch:9.73 loss:0.044 grdn:0.268 lr:5.0e-05 updt_s:0.901 data_s:0.755 smp/s:39 mem_gb:39.65
+Training:  56%|█████▌    | 33400/60000 [14:21:47<12:15:57,  1.66s/step]INFO 2026-07-15 15:36:48 ot_train.py:649 step:33K smpl:2M ep:48K epch:9.79 loss:0.044 grdn:0.268 lr:4.9e-05 updt_s:0.895 data_s:0.744 smp/s:39 mem_gb:39.65
+Training:  56%|█████▌    | 33600/60000 [14:27:19<12:05:29,  1.65s/step]INFO 2026-07-15 15:42:19 ot_train.py:649 step:34K smpl:2M ep:49K epch:9.85 loss:0.045 grdn:0.280 lr:4.9e-05 updt_s:0.897 data_s:0.756 smp/s:39 mem_gb:39.65
+Training:  56%|█████▋    | 33800/60000 [14:32:50<11:58:31,  1.65s/step]INFO 2026-07-15 15:47:51 ot_train.py:649 step:34K smpl:2M ep:49K epch:9.91 loss:0.045 grdn:0.267 lr:4.8e-05 updt_s:0.900 data_s:0.751 smp/s:39 mem_gb:39.65
+Training:  57%|█████▋    | 34000/60000 [14:38:20<11:58:31,  1.66s/step]INFO 2026-07-15 15:53:21 ot_train.py:649 step:34K smpl:2M ep:49K epch:9.96 loss:0.043 grdn:0.264 lr:4.7e-05 updt_s:0.891 data_s:0.754 smp/s:39 mem_gb:39.63
+Training:  57%|█████▋    | 34200/60000 [14:43:50<11:59:06,  1.67s/step]INFO 2026-07-15 15:58:50 ot_train.py:649 step:34K smpl:2M ep:49K epch:10.02 loss:0.042 grdn:0.266 lr:4.7e-05 updt_s:0.885 data_s:0.757 smp/s:39 mem_gb:39.65
+Training:  57%|█████▋    | 34400/60000 [14:49:21<12:00:19,  1.69s/step]INFO 2026-07-15 16:04:21 ot_train.py:649 step:34K smpl:2M ep:50K epch:10.08 loss:0.042 grdn:0.275 lr:4.6e-05 updt_s:0.918 data_s:0.733 smp/s:39 mem_gb:39.65
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+Training:  63%|██████▎   | 37800/60000 [16:23:30<10:07:20,  1.64s/step]INFO 2026-07-15 17:38:31 ot_train.py:649 step:38K smpl:2M ep:55K epch:11.08 loss:0.039 grdn:0.283 lr:3.6e-05 updt_s:0.902 data_s:0.768 smp/s:38 mem_gb:39.65
+Training:  63%|██████▎   | 38000/60000 [16:29:02<10:04:25,  1.65s/step]INFO 2026-07-15 17:44:02 ot_train.py:649 step:38K smpl:2M ep:55K epch:11.14 loss:0.038 grdn:0.279 lr:3.6e-05 updt_s:0.919 data_s:0.733 smp/s:39 mem_gb:39.65
+Training:  64%|██████▎   | 38200/60000 [16:34:34<10:26:43,  1.72s/step]INFO 2026-07-15 17:49:34 ot_train.py:649 step:38K smpl:2M ep:55K epch:11.20 loss:0.039 grdn:0.282 lr:3.5e-05 updt_s:0.896 data_s:0.757 smp/s:39 mem_gb:39.65
+Training:  64%|██████▍   | 38400/60000 [16:40:07<9:43:40,  1.62s/step]INFO 2026-07-15 17:55:07 ot_train.py:649 step:38K smpl:2M ep:55K epch:11.25 loss:0.039 grdn:0.282 lr:3.5e-05 updt_s:0.898 data_s:0.764 smp/s:39 mem_gb:39.63
+Training:  64%|██████▍   | 38600/60000 [16:45:41<9:59:21,  1.68s/step] INFO 2026-07-15 18:00:42 ot_train.py:649 step:39K smpl:2M ep:56K epch:11.31 loss:0.036 grdn:0.271 lr:3.4e-05 updt_s:0.898 data_s:0.769 smp/s:38 mem_gb:39.65
+Training:  65%|██████▍   | 38800/60000 [16:51:14<9:42:48,  1.65s/step]INFO 2026-07-15 18:06:15 ot_train.py:649 step:39K smpl:2M ep:56K epch:11.37 loss:0.036 grdn:0.274 lr:3.4e-05 updt_s:0.887 data_s:0.773 smp/s:39 mem_gb:39.65
+Training:  65%|██████▌   | 39000/60000 [16:56:47<8:56:40,  1.53s/step]INFO 2026-07-15 18:11:47 ot_train.py:649 step:39K smpl:2M ep:56K epch:11.43 loss:0.036 grdn:0.277 lr:3.3e-05 updt_s:0.893 data_s:0.763 smp/s:39 mem_gb:39.65
+Training:  65%|██████▌   | 39200/60000 [17:02:13<9:34:09,  1.66s/step]INFO 2026-07-15 18:17:14 ot_train.py:649 step:39K smpl:3M ep:57K epch:11.49 loss:0.036 grdn:0.265 lr:3.3e-05 updt_s:0.903 data_s:0.726 smp/s:39 mem_gb:39.65
+Training:  66%|██████▌   | 39400/60000 [17:07:46<9:30:10,  1.66s/step]INFO 2026-07-15 18:22:46 ot_train.py:649 step:39K smpl:3M ep:57K epch:11.55 loss:0.035 grdn:0.284 lr:3.2e-05 updt_s:0.911 data_s:0.745 smp/s:39 mem_gb:39.65
+Training:  66%|██████▌   | 39600/60000 [17:13:17<9:28:58,  1.67s/step]INFO 2026-07-15 18:28:18 ot_train.py:649 step:40K smpl:3M ep:57K epch:11.61 loss:0.036 grdn:0.280 lr:3.2e-05 updt_s:0.902 data_s:0.751 smp/s:39 mem_gb:39.65
+Training:  66%|██████▋   | 39800/60000 [17:18:48<9:09:37,  1.63s/step]INFO 2026-07-15 18:33:49 ot_train.py:649 step:40K smpl:3M ep:58K epch:11.66 loss:0.035 grdn:0.272 lr:3.1e-05 updt_s:0.879 data_s:0.772 smp/s:39 mem_gb:39.65
+Training:  67%|██████▋   | 40000/60000 [17:24:21<9:41:26,  1.74s/step]INFO 2026-07-15 18:39:21 ot_train.py:649 step:40K smpl:3M ep:58K epch:11.72 loss:0.036 grdn:0.301 lr:3.0e-05 updt_s:0.908 data_s:0.748 smp/s:39 mem_gb:39.65
+Training:  67%|██████▋   | 40200/60000 [17:29:52<9:08:19,  1.66s/step]INFO 2026-07-15 18:44:52 ot_train.py:649 step:40K smpl:3M ep:58K epch:11.78 loss:0.035 grdn:0.285 lr:3.0e-05 updt_s:0.882 data_s:0.769 smp/s:39 mem_gb:39.65
+Training:  67%|██████▋   | 40400/60000 [17:35:22<8:29:03,  1.56s/step]INFO 2026-07-15 18:50:22 ot_train.py:649 step:40K smpl:3M ep:58K epch:11.84 loss:0.035 grdn:0.289 lr:2.9e-05 updt_s:0.883 data_s:0.762 smp/s:39 mem_gb:39.65
+Training:  68%|██████▊   | 40600/60000 [17:40:51<8:55:50,  1.66s/step]INFO 2026-07-15 18:55:52 ot_train.py:649 step:41K smpl:3M ep:59K epch:11.90 loss:0.036 grdn:0.274 lr:2.9e-05 updt_s:0.880 data_s:0.762 smp/s:39 mem_gb:39.65
+Training:  68%|██████▊   | 40800/60000 [17:46:23<8:22:22,  1.57s/step]INFO 2026-07-15 19:01:23 ot_train.py:649 step:41K smpl:3M ep:59K epch:11.96 loss:0.033 grdn:0.284 lr:2.8e-05 updt_s:0.875 data_s:0.779 smp/s:39 mem_gb:39.63
+Training:  68%|██████▊   | 41000/60000 [17:51:55<8:46:14,  1.66s/step]INFO 2026-07-15 19:06:56 ot_train.py:649 step:41K smpl:3M ep:59K epch:12.02 loss:0.033 grdn:0.279 lr:2.8e-05 updt_s:0.884 data_s:0.773 smp/s:39 mem_gb:39.65
+Training:  69%|██████▊   | 41200/60000 [17:57:29<8:53:16,  1.70s/step]INFO 2026-07-15 19:12:30 ot_train.py:649 step:41K smpl:3M ep:60K epch:12.08 loss:0.033 grdn:0.283 lr:2.7e-05 updt_s:0.890 data_s:0.774 smp/s:38 mem_gb:39.65
+Training:  69%|██████▉   | 41400/60000 [18:03:04<8:42:38,  1.69s/step]INFO 2026-07-15 19:18:04 ot_train.py:649 step:41K smpl:3M ep:60K epch:12.13 loss:0.033 grdn:0.283 lr:2.7e-05 updt_s:0.889 data_s:0.779 smp/s:38 mem_gb:39.65
+Training:  69%|██████▉   | 41600/60000 [18:08:39<9:24:26,  1.84s/step]INFO 2026-07-15 19:23:40 ot_train.py:649 step:42K smpl:3M ep:60K epch:12.19 loss:0.033 grdn:0.278 lr:2.6e-05 updt_s:0.894 data_s:0.780 smp/s:38 mem_gb:39.65
+Training:  70%|██████▉   | 41800/60000 [18:14:12<8:32:23,  1.69s/step]INFO 2026-07-15 19:29:12 ot_train.py:649 step:42K smpl:3M ep:60K epch:12.25 loss:0.033 grdn:0.281 lr:2.6e-05 updt_s:0.868 data_s:0.790 smp/s:39 mem_gb:39.65
+Training:  70%|███████   | 42000/60000 [18:19:51<8:13:53,  1.65s/step]INFO 2026-07-15 19:34:51 ot_train.py:649 step:42K smpl:3M ep:61K epch:12.31 loss:0.033 grdn:0.281 lr:2.5e-05 updt_s:0.895 data_s:0.795 smp/s:38 mem_gb:39.65
+Training:  70%|███████   | 42200/60000 [18:25:24<9:48:29,  1.98s/step]INFO 2026-07-15 19:40:24 ot_train.py:649 step:42K smpl:3M ep:61K epch:12.37 loss:0.033 grdn:0.273 lr:2.5e-05 updt_s:0.910 data_s:0.748 smp/s:39 mem_gb:39.65
+Training:  71%|███████   | 42400/60000 [18:31:00<8:14:29,  1.69s/step]INFO 2026-07-15 19:46:00 ot_train.py:649 step:42K smpl:3M ep:61K epch:12.43 loss:0.033 grdn:0.287 lr:2.4e-05 updt_s:0.908 data_s:0.768 smp/s:38 mem_gb:39.65
+Training:  71%|███████   | 42600/60000 [18:36:31<8:07:33,  1.68s/step]INFO 2026-07-15 19:51:32 ot_train.py:649 step:43K smpl:3M ep:62K epch:12.49 loss:0.033 grdn:0.275 lr:2.4e-05 updt_s:0.865 data_s:0.787 smp/s:39 mem_gb:39.65
+Training:  71%|███████▏  | 42800/60000 [18:42:03<8:02:53,  1.68s/step]INFO 2026-07-15 19:57:03 ot_train.py:649 step:43K smpl:3M ep:62K epch:12.54 loss:0.032 grdn:0.283 lr:2.3e-05 updt_s:0.873 data_s:0.779 smp/s:39 mem_gb:39.65
+Training:  72%|███████▏  | 43000/60000 [18:47:39<8:03:43,  1.71s/step]INFO 2026-07-15 20:02:39 ot_train.py:649 step:43K smpl:3M ep:62K epch:12.60 loss:0.031 grdn:0.270 lr:2.3e-05 updt_s:0.908 data_s:0.768 smp/s:38 mem_gb:39.63
+Training:  72%|███████▏  | 43200/60000 [18:53:13<7:30:45,  1.61s/step]INFO 2026-07-15 20:08:13 ot_train.py:649 step:43K smpl:3M ep:62K epch:12.66 loss:0.030 grdn:0.276 lr:2.2e-05 updt_s:0.894 data_s:0.770 smp/s:38 mem_gb:39.65
+Training:  72%|███████▏  | 43400/60000 [18:58:43<7:25:35,  1.61s/step]INFO 2026-07-15 20:13:43 ot_train.py:649 step:43K smpl:3M ep:63K epch:12.72 loss:0.031 grdn:0.273 lr:2.2e-05 updt_s:0.897 data_s:0.748 smp/s:39 mem_gb:39.65
+Training:  73%|███████▎  | 43600/60000 [19:04:15<7:41:36,  1.69s/step]INFO 2026-07-15 20:19:15 ot_train.py:649 step:44K smpl:3M ep:63K epch:12.78 loss:0.030 grdn:0.278 lr:2.1e-05 updt_s:0.888 data_s:0.769 smp/s:39 mem_gb:39.65
+Training:  73%|███████▎  | 43800/60000 [19:09:46<7:19:43,  1.63s/step]INFO 2026-07-15 20:24:46 ot_train.py:649 step:44K smpl:3M ep:63K epch:12.84 loss:0.030 grdn:0.286 lr:2.1e-05 updt_s:0.880 data_s:0.769 smp/s:39 mem_gb:39.65
+Training:  73%|███████▎  | 44000/60000 [19:15:19<7:19:15,  1.65s/step]INFO 2026-07-15 20:30:20 ot_train.py:649 step:44K smpl:3M ep:64K epch:12.90 loss:0.030 grdn:0.279 lr:2.0e-05 updt_s:0.872 data_s:0.791 smp/s:38 mem_gb:39.65
+Training:  74%|███████▎  | 44200/60000 [19:20:53<7:13:49,  1.65s/step]INFO 2026-07-15 20:35:53 ot_train.py:649 step:44K smpl:3M ep:64K epch:12.95 loss:0.030 grdn:0.272 lr:2.0e-05 updt_s:0.888 data_s:0.777 smp/s:38 mem_gb:39.65
+Training:  74%|███████▍  | 44400/60000 [19:26:25<7:15:33,  1.68s/step]INFO 2026-07-15 20:41:26 ot_train.py:649 step:44K smpl:3M ep:64K epch:13.01 loss:0.030 grdn:0.284 lr:1.9e-05 updt_s:0.887 data_s:0.770 smp/s:39 mem_gb:39.65
+Training:  74%|███████▍  | 44600/60000 [19:31:57<7:00:20,  1.64s/step]INFO 2026-07-15 20:46:57 ot_train.py:649 step:45K smpl:3M ep:64K epch:13.07 loss:0.029 grdn:0.275 lr:1.9e-05 updt_s:0.872 data_s:0.780 smp/s:39 mem_gb:39.65
+Training:  75%|███████▍  | 44800/60000 [19:37:28<6:54:07,  1.63s/step]INFO 2026-07-15 20:52:28 ot_train.py:649 step:45K smpl:3M ep:65K epch:13.13 loss:0.029 grdn:0.265 lr:1.9e-05 updt_s:0.864 data_s:0.786 smp/s:39 mem_gb:39.65
+Training:  75%|███████▌  | 45000/60000 [19:43:01<6:48:42,  1.63s/step]INFO 2026-07-15 20:58:01 ot_train.py:649 step:45K smpl:3M ep:65K epch:13.19 loss:0.030 grdn:0.279 lr:1.8e-05 updt_s:0.884 data_s:0.777 smp/s:39 mem_gb:39.65
+Training:  75%|███████▌  | 45200/60000 [19:48:32<6:52:14,  1.67s/step]INFO 2026-07-15 21:03:33 ot_train.py:649 step:45K smpl:3M ep:65K epch:13.25 loss:0.029 grdn:0.269 lr:1.8e-05 updt_s:0.857 data_s:0.793 smp/s:39 mem_gb:39.63
+Training:  76%|███████▌  | 45400/60000 [19:54:04<6:52:36,  1.70s/step]INFO 2026-07-15 21:09:04 ot_train.py:649 step:45K smpl:3M ep:66K epch:13.31 loss:0.029 grdn:0.282 lr:1.7e-05 updt_s:0.874 data_s:0.779 smp/s:39 mem_gb:39.65
+Training:  76%|███████▌  | 45600/60000 [19:59:37<6:44:32,  1.69s/step]INFO 2026-07-15 21:14:37 ot_train.py:649 step:46K smpl:3M ep:66K epch:13.36 loss:0.028 grdn:0.273 lr:1.7e-05 updt_s:0.885 data_s:0.775 smp/s:39 mem_gb:39.65
+Training:  76%|███████▋  | 45800/60000 [20:05:10<6:27:43,  1.64s/step]INFO 2026-07-15 21:20:10 ot_train.py:649 step:46K smpl:3M ep:66K epch:13.42 loss:0.028 grdn:0.280 lr:1.6e-05 updt_s:0.872 data_s:0.790 smp/s:39 mem_gb:39.65
+Training:  77%|███████▋  | 46000/60000 [20:10:40<6:30:17,  1.67s/step]INFO 2026-07-15 21:25:41 ot_train.py:649 step:46K smpl:3M ep:66K epch:13.48 loss:0.027 grdn:0.278 lr:1.6e-05 updt_s:0.871 data_s:0.775 smp/s:39 mem_gb:39.65
+Training:  77%|███████▋  | 46200/60000 [20:16:12<6:14:10,  1.63s/step]INFO 2026-07-15 21:31:13 ot_train.py:649 step:46K smpl:3M ep:67K epch:13.54 loss:0.028 grdn:0.270 lr:1.5e-05 updt_s:0.882 data_s:0.774 smp/s:39 mem_gb:39.65
+Training:  77%|███████▋  | 46400/60000 [20:21:45<6:42:41,  1.78s/step]INFO 2026-07-15 21:36:45 ot_train.py:649 step:46K smpl:3M ep:67K epch:13.60 loss:0.028 grdn:0.284 lr:1.5e-05 updt_s:0.889 data_s:0.769 smp/s:39 mem_gb:39.65
+Training:  78%|███████▊  | 46600/60000 [20:27:17<5:58:05,  1.60s/step]INFO 2026-07-15 21:42:18 ot_train.py:649 step:47K smpl:3M ep:67K epch:13.66 loss:0.027 grdn:0.278 lr:1.5e-05 updt_s:0.895 data_s:0.762 smp/s:39 mem_gb:39.65
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RKD_TimewarpVAE/LAPstyle_linear_6K/wandb/run-20260715_011448-pebh9ode/run-pebh9ode.wandb

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RKD_TimewarpVAE/RSCLstyle_cosine_60K/wandb/run-20260715_102129-typqzcxc/files/output.log

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+INFO 2026-07-15 10:21:30 db_utils.py:121 Logs will be synced with wandb.
+INFO 2026-07-15 10:21:30 db_utils.py:122 Track this run --> https://wandb.ai/minje227_hyu-hanyang-university/lerobot/runs/typqzcxc
+INFO 2026-07-15 10:21:30 ot_train.py:298 Creating dataset
+INFO 2026-07-15 10:21:32 ot_train.py:332 Creating policy
+Fetching 27 files: 100%|██████████| 27/27 [00:00<00:00, 6173.14it/s]
+`torch_dtype` is deprecated! Use `dtype` instead!
+Loading weights: 100%|██████████| 1031/1031 [00:00<00:00, 2134.12it/s]
+INFO 2026-07-15 10:21:37 ot_train.py:405 Creating optimizer and scheduler
+INFO 2026-07-15 10:21:37 ot_train.py:439 Output dir: /home/ext_minje/groot_insight/Abs_6D/RKD_TimewarpVAE/RSCLstyle_cosine_60K
+INFO 2026-07-15 10:21:37 ot_train.py:446 cfg.steps=60000 (60K)
+INFO 2026-07-15 10:21:37 ot_train.py:447 dataset.num_frames=218367 (218K)
+INFO 2026-07-15 10:21:37 ot_train.py:448 dataset.num_episodes=4930
+INFO 2026-07-15 10:21:37 ot_train.py:451 Effective batch size: 64 x 1 = 64
+INFO 2026-07-15 10:21:37 ot_train.py:452 num_learnable_params=1622090881 (2B)
+INFO 2026-07-15 10:21:37 ot_train.py:453 num_total_params=3145709729 (3B)
+Training:   0%|          | 0/60000 [00:00<?, ?step/s]INFO 2026-07-15 10:21:37 ot_train.py:604 Start offline training on a fixed dataset, with effective batch size: 64
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+Training:   8%|▊         | 4800/60000 [1:39:34<18:23:19,  1.20s/step]INFO 2026-07-15 12:01:12 ot_train.py:649 step:5K smpl:307K ep:7K epch:1.41 loss:0.110 grdn:0.410 lr:1.0e-04 updt_s:0.663 data_s:0.546 smp/s:53 mem_gb:39.71
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+Training:  10%|█         | 6000/60000 [2:04:15<20:41:51,  1.38s/step]INFO 2026-07-15 12:25:53 ot_train.py:649 step:6K smpl:384K ep:9K epch:1.76 loss:0.103 grdn:0.372 lr:9.9e-05 updt_s:0.716 data_s:0.629 smp/s:48 mem_gb:39.71
+Training:  10%|█         | 6200/60000 [2:08:28<18:10:27,  1.22s/step]INFO 2026-07-15 12:30:06 ot_train.py:649 step:6K smpl:397K ep:9K epch:1.82 loss:0.101 grdn:0.347 lr:9.9e-05 updt_s:0.682 data_s:0.577 smp/s:51 mem_gb:39.71
+Training:  11%|█         | 6400/60000 [2:12:31<18:38:19,  1.25s/step]INFO 2026-07-15 12:34:09 ot_train.py:649 step:6K smpl:410K ep:9K epch:1.88 loss:0.099 grdn:0.356 lr:9.9e-05 updt_s:0.662 data_s:0.549 smp/s:53 mem_gb:39.71
+Training:  11%|█         | 6600/60000 [2:16:33<18:04:27,  1.22s/step]INFO 2026-07-15 12:38:11 ot_train.py:649 step:7K smpl:422K ep:10K epch:1.93 loss:0.096 grdn:0.339 lr:9.9e-05 updt_s:0.663 data_s:0.544 smp/s:53 mem_gb:39.71
+Training:  11%|█▏        | 6800/60000 [2:20:35<17:37:19,  1.19s/step]INFO 2026-07-15 12:42:13 ot_train.py:649 step:7K smpl:435K ep:10K epch:1.99 loss:0.099 grdn:0.371 lr:9.9e-05 updt_s:0.661 data_s:0.546 smp/s:53 mem_gb:39.69
+Training:  12%|█▏        | 7000/60000 [2:24:37<17:53:25,  1.22s/step]INFO 2026-07-15 12:46:15 ot_train.py:649 step:7K smpl:448K ep:10K epch:2.05 loss:0.099 grdn:0.341 lr:9.9e-05 updt_s:0.664 data_s:0.542 smp/s:53 mem_gb:39.71
+Training:  12%|█▏        | 7200/60000 [2:28:41<19:31:18,  1.33s/step]INFO 2026-07-15 12:50:18 ot_train.py:649 step:7K smpl:461K ep:10K epch:2.11 loss:0.096 grdn:0.358 lr:9.9e-05 updt_s:0.666 data_s:0.547 smp/s:53 mem_gb:39.71
+Training:  12%|█▏        | 7400/60000 [2:33:03<18:43:59,  1.28s/step]INFO 2026-07-15 12:54:41 ot_train.py:649 step:7K smpl:474K ep:11K epch:2.17 loss:0.096 grdn:0.345 lr:9.9e-05 updt_s:0.702 data_s:0.608 smp/s:49 mem_gb:39.71
+Training:  13%|█▎        | 7600/60000 [2:37:16<17:27:26,  1.20s/step]INFO 2026-07-15 12:58:54 ot_train.py:649 step:8K smpl:486K ep:11K epch:2.23 loss:0.096 grdn:0.345 lr:9.8e-05 updt_s:0.682 data_s:0.578 smp/s:51 mem_gb:39.71
+Training:  13%|█▎        | 7800/60000 [2:41:19<17:52:33,  1.23s/step]INFO 2026-07-15 13:02:57 ot_train.py:649 step:8K smpl:499K ep:11K epch:2.29 loss:0.093 grdn:0.324 lr:9.8e-05 updt_s:0.660 data_s:0.548 smp/s:53 mem_gb:39.71
+Training:  13%|█▎        | 8000/60000 [2:45:22<18:16:06,  1.26s/step]INFO 2026-07-15 13:06:59 ot_train.py:649 step:8K smpl:512K ep:12K epch:2.34 loss:0.094 grdn:0.341 lr:9.8e-05 updt_s:0.663 data_s:0.547 smp/s:53 mem_gb:39.71
+Training:  14%|█▎        | 8200/60000 [2:49:29<17:34:12,  1.22s/step]INFO 2026-07-15 13:11:07 ot_train.py:649 step:8K smpl:525K ep:12K epch:2.40 loss:0.092 grdn:0.327 lr:9.8e-05 updt_s:0.667 data_s:0.567 smp/s:52 mem_gb:39.71
+Training:  14%|█▍        | 8400/60000 [2:53:32<17:40:18,  1.23s/step]INFO 2026-07-15 13:15:10 ot_train.py:649 step:8K smpl:538K ep:12K epch:2.46 loss:0.094 grdn:0.356 lr:9.8e-05 updt_s:0.659 data_s:0.550 smp/s:53 mem_gb:39.71
+Training:  14%|█▍        | 8600/60000 [2:57:33<17:15:01,  1.21s/step]INFO 2026-07-15 13:19:11 ot_train.py:649 step:9K smpl:550K ep:12K epch:2.52 loss:0.093 grdn:0.339 lr:9.8e-05 updt_s:0.657 data_s:0.546 smp/s:53 mem_gb:39.71
+Training:  15%|█▍        | 8800/60000 [3:01:32<16:35:24,  1.17s/step]INFO 2026-07-15 13:23:10 ot_train.py:649 step:9K smpl:563K ep:13K epch:2.58 loss:0.091 grdn:0.319 lr:9.8e-05 updt_s:0.665 data_s:0.526 smp/s:54 mem_gb:39.70
+Training:  15%|█▌        | 9000/60000 [3:05:26<16:23:11,  1.16s/step]INFO 2026-07-15 13:27:03 ot_train.py:649 step:9K smpl:576K ep:13K epch:2.64 loss:0.087 grdn:0.308 lr:9.7e-05 updt_s:0.658 data_s:0.505 smp/s:55 mem_gb:39.71
+Training:  15%|█▌        | 9200/60000 [3:09:27<16:43:23,  1.19s/step]INFO 2026-07-15 13:31:05 ot_train.py:649 step:9K smpl:589K ep:13K epch:2.70 loss:0.087 grdn:0.329 lr:9.7e-05 updt_s:0.655 data_s:0.547 smp/s:53 mem_gb:39.69
+Training:  16%|█▌        | 9400/60000 [3:13:44<18:26:23,  1.31s/step]INFO 2026-07-15 13:35:22 ot_train.py:649 step:9K smpl:602K ep:14K epch:2.75 loss:0.088 grdn:0.340 lr:9.7e-05 updt_s:0.685 data_s:0.598 smp/s:50 mem_gb:39.71
+Training:  16%|█▌        | 9600/60000 [3:18:44<22:31:02,  1.61s/step]INFO 2026-07-15 13:40:22 ot_train.py:649 step:10K smpl:614K ep:14K epch:2.81 loss:0.089 grdn:0.331 lr:9.7e-05 updt_s:0.764 data_s:0.732 smp/s:43 mem_gb:39.71
+Training:  16%|█▋        | 9800/60000 [3:23:56<20:49:11,  1.49s/step]INFO 2026-07-15 13:45:33 ot_train.py:649 step:10K smpl:627K ep:14K epch:2.87 loss:0.087 grdn:0.318 lr:9.7e-05 updt_s:0.786 data_s:0.766 smp/s:41 mem_gb:39.71
+Training:  17%|█▋        | 10000/60000 [3:28:38<16:45:35,  1.21s/step]INFO 2026-07-15 13:50:15 ot_train.py:649 step:10K smpl:640K ep:14K epch:2.93 loss:0.084 grdn:0.322 lr:9.6e-05 updt_s:0.738 data_s:0.668 smp/s:46 mem_gb:39.71
+Training:  17%|█▋        | 10200/60000 [3:32:37<16:17:41,  1.18s/step]INFO 2026-07-15 13:54:15 ot_train.py:649 step:10K smpl:653K ep:15K epch:2.99 loss:0.091 grdn:0.358 lr:9.6e-05 updt_s:0.657 data_s:0.537 smp/s:54 mem_gb:39.71
+Training:  17%|█▋        | 10400/60000 [3:36:35<15:56:07,  1.16s/step]INFO 2026-07-15 13:58:13 ot_train.py:649 step:10K smpl:666K ep:15K epch:3.05 loss:0.090 grdn:0.345 lr:9.6e-05 updt_s:0.652 data_s:0.533 smp/s:54 mem_gb:39.71
+Training:  18%|█▊        | 10600/60000 [3:40:32<15:51:45,  1.16s/step]INFO 2026-07-15 14:02:10 ot_train.py:649 step:11K smpl:678K ep:15K epch:3.11 loss:0.083 grdn:0.307 lr:9.6e-05 updt_s:0.648 data_s:0.533 smp/s:54 mem_gb:39.71
+Training:  18%|█▊        | 10800/60000 [3:44:29<15:49:04,  1.16s/step]INFO 2026-07-15 14:06:07 ot_train.py:649 step:11K smpl:691K ep:16K epch:3.17 loss:0.083 grdn:0.314 lr:9.6e-05 updt_s:0.647 data_s:0.537 smp/s:54 mem_gb:39.71
+Training:  18%|█▊        | 11000/60000 [3:48:29<17:19:11,  1.27s/step]INFO 2026-07-15 14:10:07 ot_train.py:649 step:11K smpl:704K ep:16K epch:3.22 loss:0.080 grdn:0.306 lr:9.5e-05 updt_s:0.651 data_s:0.542 smp/s:54 mem_gb:39.71
+Training:  19%|█▊        | 11200/60000 [3:52:38<16:10:26,  1.19s/step]INFO 2026-07-15 14:14:15 ot_train.py:649 step:11K smpl:717K ep:16K epch:3.28 loss:0.081 grdn:0.305 lr:9.5e-05 updt_s:0.667 data_s:0.574 smp/s:52 mem_gb:39.71
+Training:  19%|█▉        | 11400/60000 [3:57:01<20:44:05,  1.54s/step]INFO 2026-07-15 14:18:39 ot_train.py:649 step:11K smpl:730K ep:16K epch:3.34 loss:0.083 grdn:0.324 lr:9.5e-05 updt_s:0.695 data_s:0.617 smp/s:49 mem_gb:39.69
+Training:  19%|█▉        | 11600/60000 [4:01:41<17:43:03,  1.32s/step]INFO 2026-07-15 14:23:19 ot_train.py:649 step:12K smpl:742K ep:17K epch:3.40 loss:0.080 grdn:0.324 lr:9.5e-05 updt_s:0.725 data_s:0.673 smp/s:46 mem_gb:39.71
+Training:  20%|█▉        | 11800/60000 [4:06:33<18:39:15,  1.39s/step]INFO 2026-07-15 14:28:11 ot_train.py:649 step:12K smpl:755K ep:17K epch:3.46 loss:0.081 grdn:0.319 lr:9.4e-05 updt_s:0.746 data_s:0.710 smp/s:44 mem_gb:39.71
+Training:  20%|██        | 12000/60000 [4:11:35<21:32:14,  1.62s/step]INFO 2026-07-15 14:33:13 ot_train.py:649 step:12K smpl:768K ep:17K epch:3.52 loss:0.079 grdn:0.307 lr:9.4e-05 updt_s:0.762 data_s:0.743 smp/s:43 mem_gb:39.71
+Training:  20%|██        | 12200/60000 [4:16:46<23:11:03,  1.75s/step]INFO 2026-07-15 14:38:24 ot_train.py:649 step:12K smpl:781K ep:18K epch:3.58 loss:0.077 grdn:0.305 lr:9.4e-05 updt_s:0.913 data_s:0.636 smp/s:41 mem_gb:39.71
+Training:  21%|██        | 12400/60000 [4:22:11<21:40:26,  1.64s/step]INFO 2026-07-15 14:43:49 ot_train.py:649 step:12K smpl:794K ep:18K epch:3.63 loss:0.079 grdn:0.320 lr:9.4e-05 updt_s:0.939 data_s:0.683 smp/s:39 mem_gb:39.71
+Training:  21%|██        | 12600/60000 [4:27:44<22:09:25,  1.68s/step]INFO 2026-07-15 14:49:22 ot_train.py:649 step:13K smpl:806K ep:18K epch:3.69 loss:0.082 grdn:0.329 lr:9.3e-05 updt_s:0.928 data_s:0.731 smp/s:39 mem_gb:39.71
+Training:  21%|██▏       | 12800/60000 [4:33:17<21:43:50,  1.66s/step]INFO 2026-07-15 14:54:55 ot_train.py:649 step:13K smpl:819K ep:18K epch:3.75 loss:0.079 grdn:0.314 lr:9.3e-05 updt_s:0.913 data_s:0.745 smp/s:39 mem_gb:39.71
+Training:  22%|██▏       | 13000/60000 [4:38:51<22:25:54,  1.72s/step]INFO 2026-07-15 15:00:29 ot_train.py:649 step:13K smpl:832K ep:19K epch:3.81 loss:0.077 grdn:0.311 lr:9.3e-05 updt_s:0.883 data_s:0.785 smp/s:38 mem_gb:39.71
+Training:  22%|██▏       | 13200/60000 [4:44:21<21:33:37,  1.66s/step]INFO 2026-07-15 15:05:59 ot_train.py:649 step:13K smpl:845K ep:19K epch:3.87 loss:0.076 grdn:0.293 lr:9.2e-05 updt_s:0.884 data_s:0.760 smp/s:39 mem_gb:39.71
+Training:  22%|██▏       | 13400/60000 [4:49:55<21:41:52,  1.68s/step]INFO 2026-07-15 15:11:33 ot_train.py:649 step:13K smpl:858K ep:19K epch:3.93 loss:0.081 grdn:0.329 lr:9.2e-05 updt_s:0.917 data_s:0.746 smp/s:38 mem_gb:39.71
+Training:  23%|██▎       | 13600/60000 [4:55:28<22:38:37,  1.76s/step]INFO 2026-07-15 15:17:06 ot_train.py:649 step:14K smpl:870K ep:20K epch:3.99 loss:0.079 grdn:0.328 lr:9.2e-05 updt_s:0.899 data_s:0.762 smp/s:39 mem_gb:39.69
+Training:  23%|██▎       | 13800/60000 [5:01:00<21:21:21,  1.66s/step]INFO 2026-07-15 15:22:38 ot_train.py:649 step:14K smpl:883K ep:20K epch:4.04 loss:0.074 grdn:0.306 lr:9.2e-05 updt_s:0.897 data_s:0.757 smp/s:39 mem_gb:39.71
+Training:  23%|██▎       | 14000/60000 [5:06:29<20:16:35,  1.59s/step]INFO 2026-07-15 15:28:07 ot_train.py:649 step:14K smpl:896K ep:20K epch:4.10 loss:0.072 grdn:0.302 lr:9.1e-05 updt_s:0.887 data_s:0.754 smp/s:39 mem_gb:39.71
+Training:  24%|██▎       | 14200/60000 [5:11:58<21:04:41,  1.66s/step]INFO 2026-07-15 15:33:36 ot_train.py:649 step:14K smpl:909K ep:21K epch:4.16 loss:0.076 grdn:0.332 lr:9.1e-05 updt_s:0.892 data_s:0.746 smp/s:39 mem_gb:39.71
+Training:  24%|██▍       | 14400/60000 [5:17:28<20:11:25,  1.59s/step]INFO 2026-07-15 15:39:05 ot_train.py:649 step:14K smpl:922K ep:21K epch:4.22 loss:0.074 grdn:0.297 lr:9.1e-05 updt_s:0.884 data_s:0.761 smp/s:39 mem_gb:39.71
+Training:  24%|██▍       | 14600/60000 [5:22:59<20:36:07,  1.63s/step]INFO 2026-07-15 15:44:36 ot_train.py:649 step:15K smpl:934K ep:21K epch:4.28 loss:0.073 grdn:0.298 lr:9.0e-05 updt_s:0.905 data_s:0.745 smp/s:39 mem_gb:39.71
+Training:  25%|██▍       | 14800/60000 [5:28:31<20:47:44,  1.66s/step]INFO 2026-07-15 15:50:09 ot_train.py:649 step:15K smpl:947K ep:21K epch:4.34 loss:0.074 grdn:0.307 lr:9.0e-05 updt_s:0.891 data_s:0.766 smp/s:39 mem_gb:39.70
+Training:  25%|██▌       | 15000/60000 [5:34:01<23:39:33,  1.89s/step]INFO 2026-07-15 15:55:39 ot_train.py:649 step:15K smpl:960K ep:22K epch:4.40 loss:0.073 grdn:0.311 lr:9.0e-05 updt_s:0.885 data_s:0.763 smp/s:39 mem_gb:39.71
+Training:  25%|██▌       | 15200/60000 [5:39:29<20:16:59,  1.63s/step]INFO 2026-07-15 16:01:06 ot_train.py:649 step:15K smpl:973K ep:22K epch:4.45 loss:0.075 grdn:0.322 lr:8.9e-05 updt_s:0.874 data_s:0.757 smp/s:39 mem_gb:39.71
+Training:  26%|██▌       | 15400/60000 [5:44:55<20:17:01,  1.64s/step]INFO 2026-07-15 16:06:33 ot_train.py:649 step:15K smpl:986K ep:22K epch:4.51 loss:0.071 grdn:0.301 lr:8.9e-05 updt_s:0.868 data_s:0.761 smp/s:39 mem_gb:39.71
+Training:  26%|██▌       | 15600/60000 [5:50:30<20:53:06,  1.69s/step]INFO 2026-07-15 16:12:08 ot_train.py:649 step:16K smpl:998K ep:23K epch:4.57 loss:0.072 grdn:0.309 lr:8.9e-05 updt_s:0.881 data_s:0.787 smp/s:38 mem_gb:39.71
+Training:  26%|██▋       | 15800/60000 [5:55:56<24:36:41,  2.00s/step]INFO 2026-07-15 16:17:34 ot_train.py:649 step:16K smpl:1M ep:23K epch:4.63 loss:0.072 grdn:0.306 lr:8.8e-05 updt_s:0.873 data_s:0.750 smp/s:39 mem_gb:39.69
+Training:  27%|██▋       | 16000/60000 [6:01:29<18:49:26,  1.54s/step]INFO 2026-07-15 16:23:07 ot_train.py:649 step:16K smpl:1M ep:23K epch:4.69 loss:0.071 grdn:0.310 lr:8.8e-05 updt_s:0.903 data_s:0.761 smp/s:38 mem_gb:39.71
+Training:  27%|██▋       | 16200/60000 [6:07:00<20:31:47,  1.69s/step]INFO 2026-07-15 16:28:38 ot_train.py:649 step:16K smpl:1M ep:23K epch:4.75 loss:0.070 grdn:0.310 lr:8.8e-05 updt_s:0.886 data_s:0.763 smp/s:39 mem_gb:39.71
+Training:  27%|██▋       | 16400/60000 [6:12:36<21:07:07,  1.74s/step]INFO 2026-07-15 16:34:14 ot_train.py:649 step:16K smpl:1M ep:24K epch:4.81 loss:0.070 grdn:0.309 lr:8.7e-05 updt_s:0.890 data_s:0.782 smp/s:38 mem_gb:39.71
+Training:  28%|██▊       | 16600/60000 [6:18:07<19:51:46,  1.65s/step]INFO 2026-07-15 16:39:45 ot_train.py:649 step:17K smpl:1M ep:24K epch:4.87 loss:0.068 grdn:0.308 lr:8.7e-05 updt_s:0.901 data_s:0.750 smp/s:39 mem_gb:39.71
+Training:  28%|██▊       | 16800/60000 [6:23:39<20:02:08,  1.67s/step]INFO 2026-07-15 16:45:17 ot_train.py:649 step:17K smpl:1M ep:24K epch:4.92 loss:0.068 grdn:0.314 lr:8.6e-05 updt_s:0.918 data_s:0.737 smp/s:39 mem_gb:39.71
+Training:  28%|██▊       | 17000/60000 [6:29:15<19:50:07,  1.66s/step]INFO 2026-07-15 16:50:53 ot_train.py:649 step:17K smpl:1M ep:25K epch:4.98 loss:0.069 grdn:0.314 lr:8.6e-05 updt_s:0.923 data_s:0.753 smp/s:38 mem_gb:39.71
+Training:  29%|██▊       | 17200/60000 [6:34:51<19:55:04,  1.68s/step]INFO 2026-07-15 16:56:29 ot_train.py:649 step:17K smpl:1M ep:25K epch:5.04 loss:0.067 grdn:0.300 lr:8.6e-05 updt_s:0.907 data_s:0.764 smp/s:38 mem_gb:39.71
+Training:  29%|██▉       | 17400/60000 [6:40:24<19:13:49,  1.63s/step]INFO 2026-07-15 17:02:02 ot_train.py:649 step:17K smpl:1M ep:25K epch:5.10 loss:0.067 grdn:0.295 lr:8.5e-05 updt_s:0.904 data_s:0.758 smp/s:39 mem_gb:39.71
+Training:  29%|██▉       | 17600/60000 [6:46:00<19:37:37,  1.67s/step]INFO 2026-07-15 17:07:38 ot_train.py:649 step:18K smpl:1M ep:25K epch:5.16 loss:0.066 grdn:0.305 lr:8.5e-05 updt_s:0.888 data_s:0.785 smp/s:38 mem_gb:39.71
+Training:  30%|██▉       | 17800/60000 [6:51:36<20:52:07,  1.78s/step]INFO 2026-07-15 17:13:14 ot_train.py:649 step:18K smpl:1M ep:26K epch:5.22 loss:0.068 grdn:0.314 lr:8.4e-05 updt_s:0.892 data_s:0.784 smp/s:38 mem_gb:39.71
+Training:  30%|███       | 18000/60000 [6:57:19<20:07:14,  1.72s/step]INFO 2026-07-15 17:18:57 ot_train.py:649 step:18K smpl:1M ep:26K epch:5.28 loss:0.067 grdn:0.303 lr:8.4e-05 updt_s:0.936 data_s:0.773 smp/s:37 mem_gb:39.71
+Training:  30%|███       | 18200/60000 [7:03:02<19:57:21,  1.72s/step]INFO 2026-07-15 17:24:40 ot_train.py:649 step:18K smpl:1M ep:26K epch:5.33 loss:0.064 grdn:0.296 lr:8.4e-05 updt_s:0.936 data_s:0.773 smp/s:37 mem_gb:39.69
+Training:  31%|███       | 18400/60000 [7:08:44<21:07:29,  1.83s/step]INFO 2026-07-15 17:30:22 ot_train.py:649 step:18K smpl:1M ep:27K epch:5.39 loss:0.063 grdn:0.297 lr:8.3e-05 updt_s:0.934 data_s:0.773 smp/s:37 mem_gb:39.71
+Training:  31%|███       | 18600/60000 [7:14:20<18:44:51,  1.63s/step]INFO 2026-07-15 17:35:58 ot_train.py:649 step:19K smpl:1M ep:27K epch:5.45 loss:0.063 grdn:0.293 lr:8.3e-05 updt_s:0.915 data_s:0.757 smp/s:38 mem_gb:39.71
+Training:  31%|███▏      | 18800/60000 [7:19:58<19:21:31,  1.69s/step]INFO 2026-07-15 17:41:36 ot_train.py:649 step:19K smpl:1M ep:27K epch:5.51 loss:0.062 grdn:0.299 lr:8.2e-05 updt_s:0.911 data_s:0.776 smp/s:38 mem_gb:39.71
+Training:  32%|███▏      | 19000/60000 [7:25:32<18:50:13,  1.65s/step]INFO 2026-07-15 17:47:10 ot_train.py:649 step:19K smpl:1M ep:27K epch:5.57 loss:0.065 grdn:0.307 lr:8.2e-05 updt_s:0.898 data_s:0.765 smp/s:39 mem_gb:39.71
+Training:  32%|███▏      | 19200/60000 [7:31:07<18:58:39,  1.67s/step]INFO 2026-07-15 17:52:45 ot_train.py:649 step:19K smpl:1M ep:28K epch:5.63 loss:0.067 grdn:0.334 lr:8.2e-05 updt_s:0.899 data_s:0.771 smp/s:38 mem_gb:39.71
+Training:  32%|███▏      | 19400/60000 [7:36:39<17:13:15,  1.53s/step]INFO 2026-07-15 17:58:17 ot_train.py:649 step:19K smpl:1M ep:28K epch:5.69 loss:0.063 grdn:0.300 lr:8.1e-05 updt_s:0.884 data_s:0.771 smp/s:39 mem_gb:39.71
+Training:  33%|███▎      | 19600/60000 [7:42:13<19:09:58,  1.71s/step]INFO 2026-07-15 18:03:51 ot_train.py:649 step:20K smpl:1M ep:28K epch:5.74 loss:0.063 grdn:0.298 lr:8.1e-05 updt_s:0.893 data_s:0.771 smp/s:38 mem_gb:39.71
+Training:  33%|███▎      | 19800/60000 [7:47:51<19:50:56,  1.78s/step]INFO 2026-07-15 18:09:29 ot_train.py:649 step:20K smpl:1M ep:29K epch:5.80 loss:0.062 grdn:0.301 lr:8.0e-05 updt_s:0.911 data_s:0.775 smp/s:38 mem_gb:39.71
+Training:  33%|███▎      | 20000/60000 [7:53:28<18:42:44,  1.68s/step]INFO 2026-07-15 18:15:06 ot_train.py:649 step:20K smpl:1M ep:29K epch:5.86 loss:0.064 grdn:0.313 lr:8.0e-05 updt_s:0.893 data_s:0.785 smp/s:38 mem_gb:39.71
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RKD_TimewarpVAE/RSCLstyle_cosine_60K/wandb/run-20260715_102129-typqzcxc/run-typqzcxc.wandb

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