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+    value: /home/shadeform/Desktop/robot_learning/outputs/train/diffusion_specialized_eval1
+peft:
+    value: null
+persistent_workers:
+    value: true
+policy:
+    value:
+        beta_end: 0.02
+        beta_schedule: squaredcos_cap_v2
+        beta_start: 0.0001
+        clip_sample: true
+        clip_sample_range: 1
+        compile_mode: reduce-overhead
+        compile_model: false
+        crop_is_random: true
+        crop_ratio: 1
+        crop_shape: null
+        device: cuda
+        diffusion_step_embed_dim: 128
+        do_mask_loss_for_padding: false
+        down_dims:
+            - 512
+            - 1024
+            - 2048
+        drop_n_last_frames: 7
+        freeze_vision_encoder: true
+        horizon: 16
+        input_features:
+            observation.images.front:
+                shape:
+                    - 3
+                    - 480
+                    - 640
+                type: VISUAL
+            observation.state:
+                shape:
+                    - 6
+                type: STATE
+        kernel_size: 5
+        license: null
+        n_action_steps: 8
+        n_groups: 8
+        n_obs_steps: 2
+        noise_scheduler_type: DDPM
+        normalization_mapping:
+            ACTION: MIN_MAX
+            STATE: MIN_MAX
+            VISUAL: MEAN_STD
+        num_inference_steps: 100
+        num_train_timesteps: 100
+        optimizer_betas:
+            - 0.95
+            - 0.999
+        optimizer_eps: 1e-08
+        optimizer_lr: 1e-05
+        optimizer_weight_decay: 1e-06
+        output_features:
+            action:
+                shape:
+                    - 6
+                type: ACTION
+        prediction_type: epsilon
+        pretrained_backbone_weights: ResNet18_Weights.IMAGENET1K_V1
+        pretrained_path: /home/shadeform/Desktop/robot_learning/models/checkpoints/diffusion_580_new_dataset_dino/200000/pretrained_model
+        private: null
+        push_to_hub: false
+        repo_id: null
+        resize_shape:
+            - 224
+            - 224
+        scheduler_name: cosine
+        scheduler_warmup_steps: 100
+        spatial_softmax_num_keypoints: 32
+        tags: null
+        type: diffusion
+        use_amp: false
+        use_film_scale_modulation: true
+        use_group_norm: false
+        use_peft: false
+        use_separate_rgb_encoder_per_camera: true
+        vision_backbone: resnet18
+        vision_backbone_type: dinov2
+        vision_encoder_name: facebook/dinov2-small
+        vit_feature_dim: 512
+        vit_pool_type: spatial_softmax
+prefetch_factor:
+    value: 4
+resume:
+    value: false
+reward_model:
+    value: null
+sample_weighting:
+    value: null
+save_checkpoint:
+    value: true
+save_freq:
+    value: 5000
+scheduler:
+    value:
+        name: cosine
+        num_warmup_steps: 100
+        type: diffuser
+seed:
+    value: 1000
+steps:
+    value: 30000
+tolerance_s:
+    value: 0.0001
+use_policy_training_preset:
+    value: true
+wandb:
+    value:
+        add_tags: true
+        disable_artifact: false
+        enable: true
+        entity: null
+        mode: null
+        notes: null
+        project: lerobot
+        run_id: null
diff --git a/checkpoints/diffusion_specialized_new_dataset_580/wandb/run-20260519_011148-d8akc176/files/output.log b/checkpoints/diffusion_specialized_new_dataset_580/wandb/run-20260519_011148-d8akc176/files/output.log
new file mode 100644
index 0000000000000000000000000000000000000000..efd03d03d497b3d2f03fdd3dad9fd6971dc20b83
--- /dev/null
+++ b/checkpoints/diffusion_specialized_new_dataset_580/wandb/run-20260519_011148-d8akc176/files/output.log
@@ -0,0 +1,1526 @@
+INFO 2026-05-19 01:11:49 db_utils.py:121 Logs will be synced with wandb.
+INFO 2026-05-19 01:11:49 db_utils.py:122 Track this run --> https://wandb.ai/romainguntz-eth-z-rich/lerobot/runs/d8akc176
+INFO 2026-05-19 01:11:49 ot_train.py:236 Creating dataset
+INFO 2026-05-19 01:11:49 eo_utils.py:110 Using video codec: libsvtav1
+INFO 2026-05-19 01:11:49 ot_train.py:270 Creating policy
+config.json: 100%|███████████████████████████████████████████████████████| 547/547 [00:00<00:00, 2.16MB/s]
+model.safetensors: 100%|██████████████████████████████████████████████| 88.2M/88.2M [00:00<00:00, 108MB/s]
+Loading weights: 100%|████████████████████████████████████████████████| 223/223 [00:00<00:00, 4996.77it/s]
+Loading weights from local directory
+INFO 2026-05-19 01:11:52 ot_train.py:347 Creating optimizer and scheduler
+INFO 2026-05-19 01:11:52 ot_train.py:374 Output dir: /home/shadeform/Desktop/robot_learning/outputs/train/diffusion_specialized_eval1
+INFO 2026-05-19 01:11:52 ot_train.py:381 cfg.steps=30000 (30K)
+INFO 2026-05-19 01:11:52 ot_train.py:382 dataset.num_frames=80087 (80K)
+INFO 2026-05-19 01:11:52 ot_train.py:383 dataset.num_episodes=134
+INFO 2026-05-19 01:11:52 ot_train.py:386 Effective batch size: 32 x 1 = 32
+INFO 2026-05-19 01:11:52 ot_train.py:387 num_learnable_params=251772134 (252M)
+INFO 2026-05-19 01:11:52 ot_train.py:388 num_total_params=273828710 (274M)
+Training:   0%|                                                               | 0/30000 [00:00<?, ?step/s]INFO 2026-05-19 01:11:52 ot_train.py:453 Start offline training on a fixed dataset, with effective batch size: 32
+Training:   0%|                                                      | 19/30000 [00:04<45:40, 10.94step/s]INFO 2026-05-19 01:11:57 ot_train.py:488 step:20 smpl:640 ep:1 epch:0.01 loss:0.046 grdn:0.723 lr:1.0e-06 updt_s:0.160 data_s:0.079
+Training:   0%|                                                      | 39/30000 [00:06<54:42,  9.13step/s]INFO 2026-05-19 01:11:59 ot_train.py:488 step:40 smpl:1K ep:2 epch:0.02 loss:0.043 grdn:0.672 lr:3.1e-06 updt_s:0.059 data_s:0.025
+Training:   0%|                                                    | 59/30000 [00:08<1:00:30,  8.25step/s]INFO 2026-05-19 01:12:01 ot_train.py:488 step:60 smpl:2K ep:3 epch:0.02 loss:0.051 grdn:0.679 lr:5.0e-06 updt_s:0.059 data_s:0.056
+Training:   0%|▏                                                     | 80/30000 [00:11<53:36,  9.30step/s]INFO 2026-05-19 01:12:03 ot_train.py:488 step:80 smpl:3K ep:4 epch:0.03 loss:0.028 grdn:0.414 lr:7.1e-06 updt_s:0.058 data_s:0.057
+Training:   0%|▏                                                     | 99/30000 [00:13<57:18,  8.70step/s]INFO 2026-05-19 01:12:06 ot_train.py:488 step:100 smpl:3K ep:5 epch:0.04 loss:0.031 grdn:0.439 lr:9.0e-06 updt_s:0.059 data_s:0.054
+Training:   0%|▏                                                    | 119/30000 [00:15<59:36,  8.35step/s]INFO 2026-05-19 01:12:08 ot_train.py:488 step:120 smpl:4K ep:6 epch:0.05 loss:0.033 grdn:0.444 lr:1.0e-05 updt_s:0.058 data_s:0.058
+Training:   0%|▏                                                  | 139/30000 [00:18<1:01:42,  8.06step/s]INFO 2026-05-19 01:12:10 ot_train.py:488 step:140 smpl:4K ep:7 epch:0.06 loss:0.022 grdn:0.300 lr:1.0e-05 updt_s:0.060 data_s:0.059
+Training:   1%|▎                                                  | 159/30000 [00:20<1:01:14,  8.12step/s]INFO 2026-05-19 01:12:13 ot_train.py:488 step:160 smpl:5K ep:9 epch:0.06 loss:0.020 grdn:0.270 lr:1.0e-05 updt_s:0.059 data_s:0.055
+Training:   1%|▎                                                  | 179/30000 [00:22<1:00:54,  8.16step/s]INFO 2026-05-19 01:12:15 ot_train.py:488 step:180 smpl:6K ep:10 epch:0.07 loss:0.018 grdn:0.250 lr:1.0e-05 updt_s:0.060 data_s:0.053
+Training:   1%|▎                                                  | 199/30000 [00:25<1:02:26,  7.96step/s]INFO 2026-05-19 01:12:17 ot_train.py:488 step:200 smpl:6K ep:11 epch:0.08 loss:0.018 grdn:0.263 lr:1.0e-05 updt_s:0.060 data_s:0.055
+Training:   1%|▎                                                  | 219/30000 [00:27<1:03:24,  7.83step/s]INFO 2026-05-19 01:12:20 ot_train.py:488 step:220 smpl:7K ep:12 epch:0.09 loss:0.018 grdn:0.247 lr:1.0e-05 updt_s:0.059 data_s:0.058
+Training:   1%|▍                                                  | 239/30000 [00:29<1:02:43,  7.91step/s]INFO 2026-05-19 01:12:22 ot_train.py:488 step:240 smpl:8K ep:13 epch:0.10 loss:0.018 grdn:0.239 lr:1.0e-05 updt_s:0.059 data_s:0.058
+Training:   1%|▍                                                  | 259/30000 [00:32<1:02:36,  7.92step/s]INFO 2026-05-19 01:12:24 ot_train.py:488 step:260 smpl:8K ep:14 epch:0.10 loss:0.016 grdn:0.223 lr:1.0e-05 updt_s:0.059 data_s:0.057
+Training:   1%|▍                                                  | 279/30000 [00:34<1:03:53,  7.75step/s]INFO 2026-05-19 01:12:27 ot_train.py:488 step:280 smpl:9K ep:15 epch:0.11 loss:0.016 grdn:0.210 lr:1.0e-05 updt_s:0.059 data_s:0.062
+Training:   1%|▌                                                  | 299/30000 [00:36<1:03:02,  7.85step/s]INFO 2026-05-19 01:12:29 ot_train.py:488 step:300 smpl:10K ep:16 epch:0.12 loss:0.017 grdn:0.226 lr:1.0e-05 updt_s:0.060 data_s:0.058
+Training:   1%|▌                                                  | 319/30000 [00:39<1:02:51,  7.87step/s]INFO 2026-05-19 01:12:31 ot_train.py:488 step:320 smpl:10K ep:17 epch:0.13 loss:0.017 grdn:0.207 lr:1.0e-05 updt_s:0.060 data_s:0.057
+Training:   1%|▌                                                  | 339/30000 [00:41<1:03:50,  7.74step/s]INFO 2026-05-19 01:12:34 ot_train.py:488 step:340 smpl:11K ep:18 epch:0.14 loss:0.015 grdn:0.206 lr:1.0e-05 updt_s:0.059 data_s:0.059
+Training:   1%|▌                                                  | 359/30000 [00:43<1:01:30,  8.03step/s]INFO 2026-05-19 01:12:36 ot_train.py:488 step:360 smpl:12K ep:19 epch:0.14 loss:0.016 grdn:0.227 lr:1.0e-05 updt_s:0.060 data_s:0.055
+Training:   1%|▋                                                  | 379/30000 [00:46<1:04:09,  7.70step/s]INFO 2026-05-19 01:12:39 ot_train.py:488 step:380 smpl:12K ep:20 epch:0.15 loss:0.012 grdn:0.187 lr:1.0e-05 updt_s:0.060 data_s:0.061
+Training:   1%|▋                                                  | 399/30000 [00:48<1:04:19,  7.67step/s]INFO 2026-05-19 01:12:41 ot_train.py:488 step:400 smpl:13K ep:21 epch:0.16 loss:0.013 grdn:0.205 lr:1.0e-05 updt_s:0.061 data_s:0.058
+Training:   1%|▋                                                  | 419/30000 [00:51<1:03:47,  7.73step/s]INFO 2026-05-19 01:12:43 ot_train.py:488 step:420 smpl:13K ep:22 epch:0.17 loss:0.015 grdn:0.219 lr:1.0e-05 updt_s:0.058 data_s:0.060
+Training:   1%|▋                                                  | 439/30000 [00:53<1:01:09,  8.06step/s]INFO 2026-05-19 01:12:46 ot_train.py:488 step:440 smpl:14K ep:24 epch:0.18 loss:0.016 grdn:0.230 lr:1.0e-05 updt_s:0.059 data_s:0.058
+Training:   2%|▊                                                  | 459/30000 [00:55<1:00:20,  8.16step/s]INFO 2026-05-19 01:12:48 ot_train.py:488 step:460 smpl:15K ep:25 epch:0.18 loss:0.014 grdn:0.222 lr:1.0e-05 updt_s:0.064 data_s:0.056
+Training:   2%|▊                                                  | 479/30000 [00:58<1:01:17,  8.03step/s]INFO 2026-05-19 01:12:51 ot_train.py:488 step:480 smpl:15K ep:26 epch:0.19 loss:0.013 grdn:0.203 lr:1.0e-05 updt_s:0.059 data_s:0.057
+Training:   2%|▊                                                  | 499/30000 [01:00<1:00:02,  8.19step/s]INFO 2026-05-19 01:12:53 ot_train.py:488 step:500 smpl:16K ep:27 epch:0.20 loss:0.015 grdn:0.226 lr:1.0e-05 updt_s:0.059 data_s:0.059
+Training:   2%|▉                                                    | 519/30000 [01:03<59:15,  8.29step/s]INFO 2026-05-19 01:12:55 ot_train.py:488 step:520 smpl:17K ep:28 epch:0.21 loss:0.015 grdn:0.218 lr:1.0e-05 updt_s:0.059 data_s:0.059
+Training:   2%|▉                                                    | 539/30000 [01:05<57:34,  8.53step/s]INFO 2026-05-19 01:12:58 ot_train.py:488 step:540 smpl:17K ep:29 epch:0.22 loss:0.012 grdn:0.182 lr:1.0e-05 updt_s:0.059 data_s:0.061
+Training:   2%|▉                                                    | 559/30000 [01:07<58:18,  8.42step/s]INFO 2026-05-19 01:13:00 ot_train.py:488 step:560 smpl:18K ep:30 epch:0.22 loss:0.014 grdn:0.210 lr:1.0e-05 updt_s:0.059 data_s:0.059
+Training:   2%|█                                                    | 579/30000 [01:10<59:03,  8.30step/s]INFO 2026-05-19 01:13:02 ot_train.py:488 step:580 smpl:19K ep:31 epch:0.23 loss:0.016 grdn:0.227 lr:1.0e-05 updt_s:0.059 data_s:0.058
+Training:   2%|█                                                  | 599/30000 [01:12<1:01:32,  7.96step/s]INFO 2026-05-19 01:13:05 ot_train.py:488 step:600 smpl:19K ep:32 epch:0.24 loss:0.012 grdn:0.200 lr:1.0e-05 updt_s:0.058 data_s:0.061
+Training:   2%|█                                                  | 619/30000 [01:15<1:02:22,  7.85step/s]INFO 2026-05-19 01:13:07 ot_train.py:488 step:620 smpl:20K ep:33 epch:0.25 loss:0.013 grdn:0.202 lr:1.0e-05 updt_s:0.059 data_s:0.058
+Training:   2%|█                                                  | 639/30000 [01:17<1:02:48,  7.79step/s]INFO 2026-05-19 01:13:10 ot_train.py:488 step:640 smpl:20K ep:34 epch:0.26 loss:0.014 grdn:0.192 lr:1.0e-05 updt_s:0.060 data_s:0.059
+Training:   2%|█                                                  | 659/30000 [01:19<1:02:22,  7.84step/s]INFO 2026-05-19 01:13:12 ot_train.py:488 step:660 smpl:21K ep:35 epch:0.26 loss:0.014 grdn:0.212 lr:1.0e-05 updt_s:0.059 data_s:0.058
+Training:   2%|█▏                                                 | 679/30000 [01:22<1:00:17,  8.10step/s]INFO 2026-05-19 01:13:14 ot_train.py:488 step:680 smpl:22K ep:36 epch:0.27 loss:0.013 grdn:0.206 lr:1.0e-05 updt_s:0.060 data_s:0.059
+Training:   2%|█▏                                                   | 699/30000 [01:24<58:10,  8.39step/s]INFO 2026-05-19 01:13:17 ot_train.py:488 step:700 smpl:22K ep:37 epch:0.28 loss:0.012 grdn:0.195 lr:1.0e-05 updt_s:0.059 data_s:0.059
+Training:   2%|█▎                                                   | 719/30000 [01:26<59:44,  8.17step/s]INFO 2026-05-19 01:13:19 ot_train.py:488 step:720 smpl:23K ep:39 epch:0.29 loss:0.013 grdn:0.193 lr:1.0e-05 updt_s:0.059 data_s:0.061
+Training:   2%|█▎                                                   | 739/30000 [01:29<58:01,  8.41step/s]INFO 2026-05-19 01:13:22 ot_train.py:488 step:740 smpl:24K ep:40 epch:0.30 loss:0.011 grdn:0.194 lr:1.0e-05 updt_s:0.059 data_s:0.057
+Training:   3%|█▎                                                 | 759/30000 [01:31<1:01:03,  7.98step/s]INFO 2026-05-19 01:13:24 ot_train.py:488 step:760 smpl:24K ep:41 epch:0.30 loss:0.011 grdn:0.175 lr:1.0e-05 updt_s:0.059 data_s:0.060
+Training:   3%|█▎                                                 | 779/30000 [01:34<1:00:30,  8.05step/s]INFO 2026-05-19 01:13:26 ot_train.py:488 step:780 smpl:25K ep:42 epch:0.31 loss:0.013 grdn:0.195 lr:1.0e-05 updt_s:0.058 data_s:0.059
+Training:   3%|█▎                                                 | 799/30000 [01:36<1:00:27,  8.05step/s]INFO 2026-05-19 01:13:29 ot_train.py:488 step:800 smpl:26K ep:43 epch:0.32 loss:0.012 grdn:0.191 lr:1.0e-05 updt_s:0.058 data_s:0.059
+Training:   3%|█▍                                                 | 819/30000 [01:38<1:03:01,  7.72step/s]INFO 2026-05-19 01:13:31 ot_train.py:488 step:820 smpl:26K ep:44 epch:0.33 loss:0.013 grdn:0.191 lr:1.0e-05 updt_s:0.060 data_s:0.058
+Training:   3%|█▍                                                 | 839/30000 [01:41<1:03:57,  7.60step/s]INFO 2026-05-19 01:13:33 ot_train.py:488 step:840 smpl:27K ep:45 epch:0.34 loss:0.012 grdn:0.188 lr:1.0e-05 updt_s:0.059 data_s:0.060
+Training:   3%|█▍                                                 | 859/30000 [01:43<1:02:11,  7.81step/s]INFO 2026-05-19 01:13:36 ot_train.py:488 step:860 smpl:28K ep:46 epch:0.34 loss:0.014 grdn:0.215 lr:1.0e-05 updt_s:0.060 data_s:0.060
+Training:   3%|█▍                                                 | 879/30000 [01:46<1:03:50,  7.60step/s]INFO 2026-05-19 01:13:38 ot_train.py:488 step:880 smpl:28K ep:47 epch:0.35 loss:0.016 grdn:0.226 lr:1.0e-05 updt_s:0.060 data_s:0.061
+Training:   3%|█▌                                                 | 899/30000 [01:48<1:00:32,  8.01step/s]INFO 2026-05-19 01:13:41 ot_train.py:488 step:900 smpl:29K ep:48 epch:0.36 loss:0.010 grdn:0.176 lr:1.0e-05 updt_s:0.060 data_s:0.058
+Training:   3%|█▌                                                   | 919/30000 [01:50<59:46,  8.11step/s]INFO 2026-05-19 01:13:43 ot_train.py:488 step:920 smpl:29K ep:49 epch:0.37 loss:0.012 grdn:0.196 lr:1.0e-05 updt_s:0.059 data_s:0.056
+Training:   3%|█▌                                                 | 939/30000 [01:53<1:03:13,  7.66step/s]INFO 2026-05-19 01:13:45 ot_train.py:488 step:940 smpl:30K ep:50 epch:0.38 loss:0.013 grdn:0.216 lr:1.0e-05 updt_s:0.059 data_s:0.060
+Training:   3%|█▋                                                 | 959/30000 [01:55<1:05:24,  7.40step/s]INFO 2026-05-19 01:13:48 ot_train.py:488 step:960 smpl:31K ep:51 epch:0.38 loss:0.013 grdn:0.218 lr:1.0e-05 updt_s:0.059 data_s:0.060
+Training:   3%|█▋                                                 | 979/30000 [01:57<1:03:30,  7.62step/s]INFO 2026-05-19 01:13:50 ot_train.py:488 step:980 smpl:31K ep:52 epch:0.39 loss:0.012 grdn:0.211 lr:1.0e-05 updt_s:0.060 data_s:0.057
+Training:   3%|█▋                                                 | 999/30000 [02:00<1:01:52,  7.81step/s]INFO 2026-05-19 01:13:53 ot_train.py:488 step:1K smpl:32K ep:54 epch:0.40 loss:0.013 grdn:0.215 lr:1.0e-05 updt_s:0.059 data_s:0.060
+Training:   3%|█▋                                                | 1019/30000 [02:02<1:02:19,  7.75step/s]INFO 2026-05-19 01:13:55 ot_train.py:488 step:1K smpl:33K ep:55 epch:0.41 loss:0.013 grdn:0.220 lr:1.0e-05 updt_s:0.060 data_s:0.060
+Training:   3%|█▊                                                  | 1039/30000 [02:05<59:22,  8.13step/s]INFO 2026-05-19 01:13:57 ot_train.py:488 step:1K smpl:33K ep:56 epch:0.42 loss:0.012 grdn:0.214 lr:1.0e-05 updt_s:0.058 data_s:0.061
+Training:   4%|█▊                                                  | 1059/30000 [02:07<57:08,  8.44step/s]INFO 2026-05-19 01:14:00 ot_train.py:488 step:1K smpl:34K ep:57 epch:0.42 loss:0.014 grdn:0.210 lr:1.0e-05 updt_s:0.060 data_s:0.062
+Training:   4%|█▊                                                  | 1079/30000 [02:10<57:42,  8.35step/s]INFO 2026-05-19 01:14:02 ot_train.py:488 step:1K smpl:35K ep:58 epch:0.43 loss:0.014 grdn:0.216 lr:1.0e-05 updt_s:0.059 data_s:0.062
+Training:   4%|█▉                                                  | 1099/30000 [02:12<55:50,  8.62step/s]INFO 2026-05-19 01:14:05 ot_train.py:488 step:1K smpl:35K ep:59 epch:0.44 loss:0.011 grdn:0.210 lr:1.0e-05 updt_s:0.058 data_s:0.058
+Training:   4%|█▉                                                  | 1119/30000 [02:14<54:56,  8.76step/s]INFO 2026-05-19 01:14:07 ot_train.py:488 step:1K smpl:36K ep:60 epch:0.45 loss:0.013 grdn:0.219 lr:1.0e-05 updt_s:0.058 data_s:0.061
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+Training:   4%|██                                                | 1259/30000 [02:31<1:03:08,  7.59step/s]INFO 2026-05-19 01:14:24 ot_train.py:488 step:1K smpl:40K ep:67 epch:0.50 loss:0.011 grdn:0.217 lr:1.0e-05 updt_s:0.058 data_s:0.063
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+Training:   5%|██▌                                               | 1519/30000 [03:03<1:02:04,  7.65step/s]INFO 2026-05-19 01:14:56 ot_train.py:488 step:2K smpl:49K ep:81 epch:0.61 loss:0.012 grdn:0.207 lr:9.9e-06 updt_s:0.058 data_s:0.062
+Training:   5%|██▌                                               | 1539/30000 [03:06<1:00:44,  7.81step/s]INFO 2026-05-19 01:14:58 ot_train.py:488 step:2K smpl:49K ep:82 epch:0.62 loss:0.010 grdn:0.192 lr:9.9e-06 updt_s:0.060 data_s:0.060
+Training:   5%|██▌                                               | 1559/30000 [03:08<1:01:56,  7.65step/s]INFO 2026-05-19 01:15:01 ot_train.py:488 step:2K smpl:50K ep:84 epch:0.62 loss:0.011 grdn:0.191 lr:9.9e-06 updt_s:0.060 data_s:0.060
+Training:   5%|██▋                                               | 1579/30000 [03:10<1:03:14,  7.49step/s]INFO 2026-05-19 01:15:03 ot_train.py:488 step:2K smpl:51K ep:85 epch:0.63 loss:0.011 grdn:0.189 lr:9.9e-06 updt_s:0.060 data_s:0.060
+Training:   5%|██▋                                               | 1599/30000 [03:13<1:01:53,  7.65step/s]INFO 2026-05-19 01:15:06 ot_train.py:488 step:2K smpl:51K ep:86 epch:0.64 loss:0.010 grdn:0.184 lr:9.9e-06 updt_s:0.059 data_s:0.060
+Training:   5%|██▋                                               | 1619/30000 [03:15<1:02:35,  7.56step/s]INFO 2026-05-19 01:15:08 ot_train.py:488 step:2K smpl:52K ep:87 epch:0.65 loss:0.011 grdn:0.190 lr:9.9e-06 updt_s:0.058 data_s:0.063
+Training:   5%|██▋                                               | 1639/30000 [03:18<1:00:26,  7.82step/s]INFO 2026-05-19 01:15:10 ot_train.py:488 step:2K smpl:52K ep:88 epch:0.66 loss:0.013 grdn:0.222 lr:9.9e-06 updt_s:0.060 data_s:0.060
+Training:   6%|██▊                                               | 1659/30000 [03:20<1:01:41,  7.66step/s]INFO 2026-05-19 01:15:13 ot_train.py:488 step:2K smpl:53K ep:89 epch:0.66 loss:0.011 grdn:0.196 lr:9.9e-06 updt_s:0.059 data_s:0.060
+Training:   6%|██▊                                               | 1679/30000 [03:22<1:02:13,  7.59step/s]INFO 2026-05-19 01:15:15 ot_train.py:488 step:2K smpl:54K ep:90 epch:0.67 loss:0.012 grdn:0.208 lr:9.9e-06 updt_s:0.059 data_s:0.062
+Training:   6%|██▊                                               | 1699/30000 [03:25<1:00:20,  7.82step/s]INFO 2026-05-19 01:15:18 ot_train.py:488 step:2K smpl:54K ep:91 epch:0.68 loss:0.011 grdn:0.215 lr:9.9e-06 updt_s:0.059 data_s:0.059
+Training:   6%|██▊                                               | 1719/30000 [03:27<1:01:09,  7.71step/s]INFO 2026-05-19 01:15:20 ot_train.py:488 step:2K smpl:55K ep:92 epch:0.69 loss:0.013 grdn:0.206 lr:9.9e-06 updt_s:0.058 data_s:0.061
+Training:   6%|███                                                 | 1739/30000 [03:30<59:26,  7.92step/s]INFO 2026-05-19 01:15:22 ot_train.py:488 step:2K smpl:56K ep:93 epch:0.70 loss:0.011 grdn:0.185 lr:9.9e-06 updt_s:0.059 data_s:0.060
+Training:   6%|██▉                                               | 1759/30000 [03:32<1:00:19,  7.80step/s]INFO 2026-05-19 01:15:25 ot_train.py:488 step:2K smpl:56K ep:94 epch:0.70 loss:0.013 grdn:0.227 lr:9.9e-06 updt_s:0.059 data_s:0.063
+Training:   6%|██▉                                               | 1779/30000 [03:34<1:00:17,  7.80step/s]INFO 2026-05-19 01:15:27 ot_train.py:488 step:2K smpl:57K ep:95 epch:0.71 loss:0.010 grdn:0.203 lr:9.9e-06 updt_s:0.059 data_s:0.058
+Training:   6%|██▉                                               | 1799/30000 [03:37<1:00:28,  7.77step/s]INFO 2026-05-19 01:15:30 ot_train.py:488 step:2K smpl:58K ep:96 epch:0.72 loss:0.011 grdn:0.206 lr:9.9e-06 updt_s:0.060 data_s:0.058
+Training:   6%|███                                               | 1819/30000 [03:39<1:01:33,  7.63step/s]INFO 2026-05-19 01:15:32 ot_train.py:488 step:2K smpl:58K ep:97 epch:0.73 loss:0.011 grdn:0.211 lr:9.9e-06 updt_s:0.060 data_s:0.060
+Training:   6%|███                                               | 1839/30000 [03:42<1:01:34,  7.62step/s]INFO 2026-05-19 01:15:34 ot_train.py:488 step:2K smpl:59K ep:99 epch:0.74 loss:0.009 grdn:0.182 lr:9.9e-06 updt_s:0.058 data_s:0.063
+Training:   6%|███                                               | 1859/30000 [03:44<1:01:44,  7.60step/s]INFO 2026-05-19 01:15:37 ot_train.py:488 step:2K smpl:60K ep:100 epch:0.74 loss:0.013 grdn:0.195 lr:9.9e-06 updt_s:0.059 data_s:0.062
+Training:   6%|███▏                                              | 1879/30000 [03:47<1:02:02,  7.56step/s]INFO 2026-05-19 01:15:39 ot_train.py:488 step:2K smpl:60K ep:101 epch:0.75 loss:0.011 grdn:0.191 lr:9.9e-06 updt_s:0.059 data_s:0.062
+Training:   6%|███▏                                              | 1899/30000 [03:49<1:00:16,  7.77step/s]INFO 2026-05-19 01:15:42 ot_train.py:488 step:2K smpl:61K ep:102 epch:0.76 loss:0.012 grdn:0.214 lr:9.9e-06 updt_s:0.060 data_s:0.060
+Training:   6%|███▏                                              | 1919/30000 [03:51<1:00:45,  7.70step/s]INFO 2026-05-19 01:15:44 ot_train.py:488 step:2K smpl:61K ep:103 epch:0.77 loss:0.011 grdn:0.192 lr:9.9e-06 updt_s:0.059 data_s:0.061
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+Training:   7%|███▍                                                | 1959/30000 [03:56<59:40,  7.83step/s]INFO 2026-05-19 01:15:49 ot_train.py:488 step:2K smpl:63K ep:105 epch:0.78 loss:0.009 grdn:0.174 lr:9.9e-06 updt_s:0.059 data_s:0.059
+Training:   7%|███▎                                              | 1979/30000 [03:59<1:01:23,  7.61step/s]INFO 2026-05-19 01:15:51 ot_train.py:488 step:2K smpl:63K ep:106 epch:0.79 loss:0.011 grdn:0.200 lr:9.9e-06 updt_s:0.058 data_s:0.063
+Training:   7%|███▍                                                | 1999/30000 [04:01<59:41,  7.82step/s]INFO 2026-05-19 01:15:54 ot_train.py:488 step:2K smpl:64K ep:107 epch:0.80 loss:0.012 grdn:0.205 lr:9.9e-06 updt_s:0.059 data_s:0.060
+Training:   7%|███▎                                              | 2019/30000 [04:04<1:01:07,  7.63step/s]INFO 2026-05-19 01:15:56 ot_train.py:488 step:2K smpl:65K ep:108 epch:0.81 loss:0.011 grdn:0.192 lr:9.9e-06 updt_s:0.059 data_s:0.062
+Training:   7%|███▍                                              | 2039/30000 [04:06<1:00:10,  7.74step/s]INFO 2026-05-19 01:15:59 ot_train.py:488 step:2K smpl:65K ep:109 epch:0.82 loss:0.010 grdn:0.186 lr:9.9e-06 updt_s:0.059 data_s:0.058
+Training:   7%|███▌                                                | 2059/30000 [04:08<58:39,  7.94step/s]INFO 2026-05-19 01:16:01 ot_train.py:488 step:2K smpl:66K ep:110 epch:0.82 loss:0.013 grdn:0.221 lr:9.9e-06 updt_s:0.059 data_s:0.061
+Training:   7%|███▍                                              | 2079/30000 [04:11<1:01:33,  7.56step/s]INFO 2026-05-19 01:16:03 ot_train.py:488 step:2K smpl:67K ep:111 epch:0.83 loss:0.012 grdn:0.197 lr:9.9e-06 updt_s:0.060 data_s:0.059
+Training:   7%|███▋                                                | 2099/30000 [04:13<57:28,  8.09step/s]INFO 2026-05-19 01:16:06 ot_train.py:488 step:2K smpl:67K ep:112 epch:0.84 loss:0.012 grdn:0.231 lr:9.9e-06 updt_s:0.058 data_s:0.059
+Training:   7%|███▋                                                | 2119/30000 [04:15<57:59,  8.01step/s]INFO 2026-05-19 01:16:08 ot_train.py:488 step:2K smpl:68K ep:114 epch:0.85 loss:0.011 grdn:0.200 lr:9.9e-06 updt_s:0.059 data_s:0.058
+Training:   7%|███▋                                                | 2139/30000 [04:18<59:01,  7.87step/s]INFO 2026-05-19 01:16:10 ot_train.py:488 step:2K smpl:68K ep:115 epch:0.86 loss:0.011 grdn:0.198 lr:9.9e-06 updt_s:0.058 data_s:0.059
+Training:   7%|███▋                                                | 2159/30000 [04:20<59:50,  7.75step/s]INFO 2026-05-19 01:16:13 ot_train.py:488 step:2K smpl:69K ep:116 epch:0.86 loss:0.011 grdn:0.199 lr:9.9e-06 updt_s:0.060 data_s:0.058
+Training:   7%|███▋                                              | 2179/30000 [04:23<1:01:32,  7.54step/s]INFO 2026-05-19 01:16:15 ot_train.py:488 step:2K smpl:70K ep:117 epch:0.87 loss:0.011 grdn:0.203 lr:9.9e-06 updt_s:0.058 data_s:0.061
+Training:   7%|███▊                                                | 2199/30000 [04:25<59:49,  7.75step/s]INFO 2026-05-19 01:16:18 ot_train.py:488 step:2K smpl:70K ep:118 epch:0.88 loss:0.013 grdn:0.220 lr:9.9e-06 updt_s:0.059 data_s:0.058
+Training:   7%|███▋                                              | 2219/30000 [04:27<1:06:04,  7.01step/s]INFO 2026-05-19 01:16:20 ot_train.py:488 step:2K smpl:71K ep:119 epch:0.89 loss:0.012 grdn:0.207 lr:9.9e-06 updt_s:0.060 data_s:0.064
+Training:   7%|███▉                                                | 2239/30000 [04:30<57:29,  8.05step/s]INFO 2026-05-19 01:16:22 ot_train.py:488 step:2K smpl:72K ep:120 epch:0.90 loss:0.011 grdn:0.204 lr:9.9e-06 updt_s:0.059 data_s:0.059
+Training:   8%|███▉                                                | 2259/30000 [04:32<58:56,  7.85step/s]INFO 2026-05-19 01:16:25 ot_train.py:488 step:2K smpl:72K ep:121 epch:0.90 loss:0.012 grdn:0.211 lr:9.9e-06 updt_s:0.059 data_s:0.065
+Training:   8%|███▉                                                | 2279/30000 [04:35<59:56,  7.71step/s]INFO 2026-05-19 01:16:27 ot_train.py:488 step:2K smpl:73K ep:122 epch:0.91 loss:0.011 grdn:0.196 lr:9.9e-06 updt_s:0.058 data_s:0.060
+Training:   8%|███▊                                              | 2299/30000 [04:37<1:00:35,  7.62step/s]INFO 2026-05-19 01:16:30 ot_train.py:488 step:2K smpl:74K ep:123 epch:0.92 loss:0.011 grdn:0.197 lr:9.9e-06 updt_s:0.059 data_s:0.064
+Training:   8%|███▊                                              | 2319/30000 [04:39<1:01:08,  7.54step/s]INFO 2026-05-19 01:16:32 ot_train.py:488 step:2K smpl:74K ep:124 epch:0.93 loss:0.012 grdn:0.209 lr:9.9e-06 updt_s:0.058 data_s:0.061
+Training:   8%|████                                                | 2339/30000 [04:42<59:15,  7.78step/s]INFO 2026-05-19 01:16:35 ot_train.py:488 step:2K smpl:75K ep:125 epch:0.93 loss:0.010 grdn:0.196 lr:9.9e-06 updt_s:0.058 data_s:0.062
+Training:   8%|███▉                                              | 2359/30000 [04:44<1:00:26,  7.62step/s]INFO 2026-05-19 01:16:37 ot_train.py:488 step:2K smpl:76K ep:126 epch:0.94 loss:0.011 grdn:0.203 lr:9.9e-06 updt_s:0.059 data_s:0.060
+Training:   8%|████                                                | 2379/30000 [04:47<59:36,  7.72step/s]INFO 2026-05-19 01:16:39 ot_train.py:488 step:2K smpl:76K ep:127 epch:0.95 loss:0.013 grdn:0.239 lr:9.9e-06 updt_s:0.058 data_s:0.063
+Training:   8%|████▏                                               | 2399/30000 [04:49<59:24,  7.74step/s]INFO 2026-05-19 01:16:42 ot_train.py:488 step:2K smpl:77K ep:129 epch:0.96 loss:0.010 grdn:0.189 lr:9.9e-06 updt_s:0.060 data_s:0.059
+Training:   8%|████▏                                               | 2419/30000 [04:51<59:43,  7.70step/s]INFO 2026-05-19 01:16:44 ot_train.py:488 step:2K smpl:77K ep:130 epch:0.97 loss:0.011 grdn:0.196 lr:9.9e-06 updt_s:0.059 data_s:0.060
+Training:   8%|████▏                                               | 2439/30000 [04:54<58:52,  7.80step/s]INFO 2026-05-19 01:16:47 ot_train.py:488 step:2K smpl:78K ep:131 epch:0.97 loss:0.011 grdn:0.210 lr:9.9e-06 updt_s:0.060 data_s:0.059
+Training:   8%|████▎                                               | 2459/30000 [04:56<57:52,  7.93step/s]INFO 2026-05-19 01:16:49 ot_train.py:488 step:2K smpl:79K ep:132 epch:0.98 loss:0.011 grdn:0.193 lr:9.8e-06 updt_s:0.060 data_s:0.060
+Training:   8%|████▏                                             | 2479/30000 [05:00<1:41:18,  4.53step/s]INFO 2026-05-19 01:16:53 ot_train.py:488 step:2K smpl:79K ep:133 epch:0.99 loss:0.012 grdn:0.211 lr:9.8e-06 updt_s:0.106 data_s:0.073
+Training:   8%|████▏                                             | 2499/30000 [05:02<1:01:43,  7.43step/s]INFO 2026-05-19 01:16:55 ot_train.py:488 step:2K smpl:80K ep:134 epch:1.00 loss:0.012 grdn:0.212 lr:9.8e-06 updt_s:0.059 data_s:0.060
+Training:   8%|████▎                                               | 2519/30000 [05:05<58:55,  7.77step/s]INFO 2026-05-19 01:16:57 ot_train.py:488 step:3K smpl:81K ep:135 epch:1.01 loss:0.010 grdn:0.204 lr:9.8e-06 updt_s:0.060 data_s:0.058
+Training:   8%|████▏                                             | 2539/30000 [05:07<1:01:19,  7.46step/s]INFO 2026-05-19 01:17:00 ot_train.py:488 step:3K smpl:81K ep:136 epch:1.01 loss:0.010 grdn:0.197 lr:9.8e-06 updt_s:0.060 data_s:0.064
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+Training:   9%|████▋                                               | 2679/30000 [05:24<51:28,  8.84step/s]INFO 2026-05-19 01:17:17 ot_train.py:488 step:3K smpl:86K ep:143 epch:1.07 loss:0.012 grdn:0.218 lr:9.8e-06 updt_s:0.060 data_s:0.062
+Training:   9%|████▋                                               | 2699/30000 [05:26<52:53,  8.60step/s]INFO 2026-05-19 01:17:19 ot_train.py:488 step:3K smpl:86K ep:145 epch:1.08 loss:0.011 grdn:0.214 lr:9.8e-06 updt_s:0.059 data_s:0.063
+Training:   9%|████▋                                               | 2719/30000 [05:29<59:02,  7.70step/s]INFO 2026-05-19 01:17:22 ot_train.py:488 step:3K smpl:87K ep:146 epch:1.09 loss:0.012 grdn:0.207 lr:9.8e-06 updt_s:0.059 data_s:0.089
+Training:   9%|████▋                                               | 2739/30000 [05:32<56:58,  7.98step/s]INFO 2026-05-19 01:17:24 ot_train.py:488 step:3K smpl:88K ep:147 epch:1.09 loss:0.010 grdn:0.196 lr:9.8e-06 updt_s:0.059 data_s:0.056
+Training:   9%|████▊                                               | 2759/30000 [05:34<58:11,  7.80step/s]INFO 2026-05-19 01:17:27 ot_train.py:488 step:3K smpl:88K ep:148 epch:1.10 loss:0.011 grdn:0.201 lr:9.8e-06 updt_s:0.060 data_s:0.057
+Training:   9%|████▊                                               | 2779/30000 [05:37<58:19,  7.78step/s]INFO 2026-05-19 01:17:29 ot_train.py:488 step:3K smpl:89K ep:149 epch:1.11 loss:0.011 grdn:0.205 lr:9.8e-06 updt_s:0.060 data_s:0.056
+Training:   9%|████▊                                               | 2799/30000 [05:39<55:47,  8.13step/s]INFO 2026-05-19 01:17:32 ot_train.py:488 step:3K smpl:90K ep:150 epch:1.12 loss:0.010 grdn:0.194 lr:9.8e-06 updt_s:0.058 data_s:0.059
+Training:   9%|████▉                                               | 2819/30000 [05:41<58:32,  7.74step/s]INFO 2026-05-19 01:17:34 ot_train.py:488 step:3K smpl:90K ep:151 epch:1.13 loss:0.012 grdn:0.221 lr:9.8e-06 updt_s:0.058 data_s:0.059
+Training:   9%|████▉                                               | 2839/30000 [05:44<58:38,  7.72step/s]INFO 2026-05-19 01:17:36 ot_train.py:488 step:3K smpl:91K ep:152 epch:1.13 loss:0.009 grdn:0.187 lr:9.8e-06 updt_s:0.058 data_s:0.060
+Training:  10%|████▉                                               | 2859/30000 [05:46<57:49,  7.82step/s]INFO 2026-05-19 01:17:39 ot_train.py:488 step:3K smpl:92K ep:153 epch:1.14 loss:0.011 grdn:0.188 lr:9.8e-06 updt_s:0.058 data_s:0.061
+Training:  10%|████▉                                               | 2879/30000 [05:48<58:05,  7.78step/s]INFO 2026-05-19 01:17:41 ot_train.py:488 step:3K smpl:92K ep:154 epch:1.15 loss:0.010 grdn:0.215 lr:9.8e-06 updt_s:0.058 data_s:0.060
+Training:  10%|█████                                               | 2899/30000 [05:51<59:39,  7.57step/s]INFO 2026-05-19 01:17:44 ot_train.py:488 step:3K smpl:93K ep:155 epch:1.16 loss:0.012 grdn:0.206 lr:9.8e-06 updt_s:0.058 data_s:0.061
+Training:  10%|█████                                               | 2919/30000 [05:53<59:28,  7.59step/s]INFO 2026-05-19 01:17:46 ot_train.py:488 step:3K smpl:93K ep:156 epch:1.17 loss:0.012 grdn:0.221 lr:9.8e-06 updt_s:0.058 data_s:0.062
+Training:  10%|█████                                               | 2939/30000 [05:56<58:02,  7.77step/s]INFO 2026-05-19 01:17:48 ot_train.py:488 step:3K smpl:94K ep:157 epch:1.17 loss:0.010 grdn:0.190 lr:9.8e-06 updt_s:0.059 data_s:0.060
+Training:  10%|█████▏                                              | 2959/30000 [05:58<56:59,  7.91step/s]INFO 2026-05-19 01:17:51 ot_train.py:488 step:3K smpl:95K ep:158 epch:1.18 loss:0.010 grdn:0.190 lr:9.8e-06 updt_s:0.059 data_s:0.060
+Training:  10%|████▉                                             | 2979/30000 [06:00<1:00:08,  7.49step/s]INFO 2026-05-19 01:17:53 ot_train.py:488 step:3K smpl:95K ep:160 epch:1.19 loss:0.012 grdn:0.223 lr:9.8e-06 updt_s:0.058 data_s:0.062
+Training:  10%|█████▏                                              | 2999/30000 [06:03<59:42,  7.54step/s]INFO 2026-05-19 01:17:56 ot_train.py:488 step:3K smpl:96K ep:161 epch:1.20 loss:0.011 grdn:0.219 lr:9.8e-06 updt_s:0.059 data_s:0.068
+Training:  10%|█████▏                                              | 3019/30000 [06:05<57:21,  7.84step/s]INFO 2026-05-19 01:17:58 ot_train.py:488 step:3K smpl:97K ep:162 epch:1.21 loss:0.012 grdn:0.228 lr:9.8e-06 updt_s:0.059 data_s:0.057
+Training:  10%|█████▎                                              | 3039/30000 [06:08<58:01,  7.74step/s]INFO 2026-05-19 01:18:00 ot_train.py:488 step:3K smpl:97K ep:163 epch:1.21 loss:0.011 grdn:0.219 lr:9.8e-06 updt_s:0.059 data_s:0.059
+Training:  10%|█████▎                                              | 3059/30000 [06:10<58:39,  7.65step/s]INFO 2026-05-19 01:18:03 ot_train.py:488 step:3K smpl:98K ep:164 epch:1.22 loss:0.011 grdn:0.213 lr:9.8e-06 updt_s:0.061 data_s:0.058
+Training:  10%|█████▎                                              | 3079/30000 [06:12<55:47,  8.04step/s]INFO 2026-05-19 01:18:05 ot_train.py:488 step:3K smpl:99K ep:165 epch:1.23 loss:0.009 grdn:0.193 lr:9.8e-06 updt_s:0.060 data_s:0.057
+Training:  10%|█████▎                                              | 3099/30000 [06:15<59:23,  7.55step/s]INFO 2026-05-19 01:18:08 ot_train.py:488 step:3K smpl:99K ep:166 epch:1.24 loss:0.013 grdn:0.248 lr:9.8e-06 updt_s:0.060 data_s:0.059
+Training:  10%|█████▍                                              | 3119/30000 [06:17<56:43,  7.90step/s]INFO 2026-05-19 01:18:10 ot_train.py:488 step:3K smpl:100K ep:167 epch:1.25 loss:0.011 grdn:0.211 lr:9.8e-06 updt_s:0.059 data_s:0.056
+Training:  10%|█████▍                                              | 3139/30000 [06:20<54:27,  8.22step/s]INFO 2026-05-19 01:18:12 ot_train.py:488 step:3K smpl:100K ep:168 epch:1.25 loss:0.011 grdn:0.207 lr:9.7e-06 updt_s:0.060 data_s:0.058
+Training:  11%|█████▍                                              | 3159/30000 [06:22<56:55,  7.86step/s]INFO 2026-05-19 01:18:15 ot_train.py:488 step:3K smpl:101K ep:169 epch:1.26 loss:0.011 grdn:0.208 lr:9.7e-06 updt_s:0.062 data_s:0.056
+Training:  11%|█████▌                                              | 3179/30000 [06:24<58:39,  7.62step/s]INFO 2026-05-19 01:18:17 ot_train.py:488 step:3K smpl:102K ep:170 epch:1.27 loss:0.012 grdn:0.240 lr:9.7e-06 updt_s:0.060 data_s:0.060
+Training:  11%|█████▌                                              | 3199/30000 [06:27<57:32,  7.76step/s]INFO 2026-05-19 01:18:19 ot_train.py:488 step:3K smpl:102K ep:171 epch:1.28 loss:0.011 grdn:0.210 lr:9.7e-06 updt_s:0.060 data_s:0.059
+Training:  11%|█████▎                                            | 3219/30000 [06:29<1:00:07,  7.42step/s]INFO 2026-05-19 01:18:22 ot_train.py:488 step:3K smpl:103K ep:172 epch:1.29 loss:0.010 grdn:0.202 lr:9.7e-06 updt_s:0.060 data_s:0.062
+Training:  11%|█████▌                                              | 3239/30000 [06:32<56:40,  7.87step/s]INFO 2026-05-19 01:18:24 ot_train.py:488 step:3K smpl:104K ep:173 epch:1.29 loss:0.009 grdn:0.190 lr:9.7e-06 updt_s:0.062 data_s:0.056
+Training:  11%|█████▋                                              | 3259/30000 [06:34<59:22,  7.51step/s]INFO 2026-05-19 01:18:27 ot_train.py:488 step:3K smpl:104K ep:175 epch:1.30 loss:0.011 grdn:0.215 lr:9.7e-06 updt_s:0.061 data_s:0.060
+Training:  11%|█████▋                                              | 3279/30000 [06:36<58:04,  7.67step/s]INFO 2026-05-19 01:18:29 ot_train.py:488 step:3K smpl:105K ep:176 epch:1.31 loss:0.011 grdn:0.227 lr:9.7e-06 updt_s:0.060 data_s:0.061
+Training:  11%|█████▋                                              | 3299/30000 [06:39<59:20,  7.50step/s]INFO 2026-05-19 01:18:32 ot_train.py:488 step:3K smpl:106K ep:177 epch:1.32 loss:0.012 grdn:0.226 lr:9.7e-06 updt_s:0.059 data_s:0.062
+Training:  11%|█████▊                                              | 3319/30000 [06:41<58:18,  7.63step/s]INFO 2026-05-19 01:18:34 ot_train.py:488 step:3K smpl:106K ep:178 epch:1.33 loss:0.012 grdn:0.215 lr:9.7e-06 updt_s:0.058 data_s:0.061
+Training:  11%|█████▊                                              | 3339/30000 [06:44<58:18,  7.62step/s]INFO 2026-05-19 01:18:36 ot_train.py:488 step:3K smpl:107K ep:179 epch:1.33 loss:0.010 grdn:0.222 lr:9.7e-06 updt_s:0.059 data_s:0.060
+Training:  11%|█████▊                                              | 3359/30000 [06:46<56:08,  7.91step/s]INFO 2026-05-19 01:18:39 ot_train.py:488 step:3K smpl:108K ep:180 epch:1.34 loss:0.010 grdn:0.209 lr:9.7e-06 updt_s:0.058 data_s:0.059
+Training:  11%|█████▊                                              | 3379/30000 [06:48<57:36,  7.70step/s]INFO 2026-05-19 01:18:41 ot_train.py:488 step:3K smpl:108K ep:181 epch:1.35 loss:0.012 grdn:0.230 lr:9.7e-06 updt_s:0.059 data_s:0.060
+Training:  11%|█████▉                                              | 3399/30000 [06:51<56:26,  7.85step/s]INFO 2026-05-19 01:18:43 ot_train.py:488 step:3K smpl:109K ep:182 epch:1.36 loss:0.011 grdn:0.206 lr:9.7e-06 updt_s:0.061 data_s:0.056
+Training:  11%|█████▉                                              | 3419/30000 [06:53<57:59,  7.64step/s]INFO 2026-05-19 01:18:46 ot_train.py:488 step:3K smpl:109K ep:183 epch:1.37 loss:0.012 grdn:0.237 lr:9.7e-06 updt_s:0.060 data_s:0.059
+Training:  11%|█████▉                                              | 3439/30000 [06:55<55:24,  7.99step/s]INFO 2026-05-19 01:18:48 ot_train.py:488 step:3K smpl:110K ep:184 epch:1.37 loss:0.011 grdn:0.217 lr:9.7e-06 updt_s:0.058 data_s:0.056
+Training:  12%|█████▉                                              | 3459/30000 [06:58<56:23,  7.84step/s]INFO 2026-05-19 01:18:51 ot_train.py:488 step:3K smpl:111K ep:185 epch:1.38 loss:0.011 grdn:0.216 lr:9.7e-06 updt_s:0.061 data_s:0.057
+Training:  12%|██████                                              | 3479/30000 [07:00<55:48,  7.92step/s]INFO 2026-05-19 01:18:53 ot_train.py:488 step:3K smpl:111K ep:186 epch:1.39 loss:0.010 grdn:0.205 lr:9.7e-06 updt_s:0.058 data_s:0.058
+Training:  12%|██████                                              | 3499/30000 [07:03<57:59,  7.62step/s]INFO 2026-05-19 01:18:55 ot_train.py:488 step:4K smpl:112K ep:187 epch:1.40 loss:0.013 grdn:0.231 lr:9.7e-06 updt_s:0.059 data_s:0.060
+Training:  12%|██████                                              | 3519/30000 [07:05<57:50,  7.63step/s]INFO 2026-05-19 01:18:58 ot_train.py:488 step:4K smpl:113K ep:188 epch:1.41 loss:0.013 grdn:0.230 lr:9.7e-06 updt_s:0.060 data_s:0.059
+Training:  12%|██████▏                                             | 3539/30000 [07:07<55:13,  7.99step/s]INFO 2026-05-19 01:19:00 ot_train.py:488 step:4K smpl:113K ep:190 epch:1.41 loss:0.011 grdn:0.222 lr:9.7e-06 updt_s:0.058 data_s:0.057
+Training:  12%|██████▏                                             | 3559/30000 [07:10<58:15,  7.56step/s]INFO 2026-05-19 01:19:02 ot_train.py:488 step:4K smpl:114K ep:191 epch:1.42 loss:0.011 grdn:0.206 lr:9.7e-06 updt_s:0.058 data_s:0.061
+Training:  12%|██████▏                                             | 3579/30000 [07:12<55:33,  7.93step/s]INFO 2026-05-19 01:19:05 ot_train.py:488 step:4K smpl:115K ep:192 epch:1.43 loss:0.011 grdn:0.197 lr:9.7e-06 updt_s:0.059 data_s:0.056
+Training:  12%|██████▏                                             | 3599/30000 [07:14<56:44,  7.76step/s]INFO 2026-05-19 01:19:07 ot_train.py:488 step:4K smpl:115K ep:193 epch:1.44 loss:0.012 grdn:0.214 lr:9.7e-06 updt_s:0.059 data_s:0.059
+Training:  12%|██████▎                                             | 3619/30000 [07:17<55:32,  7.92step/s]INFO 2026-05-19 01:19:09 ot_train.py:488 step:4K smpl:116K ep:194 epch:1.45 loss:0.012 grdn:0.199 lr:9.7e-06 updt_s:0.059 data_s:0.057
+Training:  12%|██████▎                                             | 3639/30000 [07:19<56:03,  7.84step/s]INFO 2026-05-19 01:19:12 ot_train.py:488 step:4K smpl:116K ep:195 epch:1.45 loss:0.011 grdn:0.214 lr:9.7e-06 updt_s:0.059 data_s:0.058
+Training:  12%|██████▎                                             | 3659/30000 [07:21<55:13,  7.95step/s]INFO 2026-05-19 01:19:14 ot_train.py:488 step:4K smpl:117K ep:196 epch:1.46 loss:0.009 grdn:0.181 lr:9.7e-06 updt_s:0.060 data_s:0.058
+Training:  12%|██████▍                                             | 3679/30000 [07:24<57:19,  7.65step/s]INFO 2026-05-19 01:19:17 ot_train.py:488 step:4K smpl:118K ep:197 epch:1.47 loss:0.010 grdn:0.202 lr:9.7e-06 updt_s:0.060 data_s:0.059
+Training:  12%|██████▍                                             | 3699/30000 [07:26<55:57,  7.83step/s]INFO 2026-05-19 01:19:19 ot_train.py:488 step:4K smpl:118K ep:198 epch:1.48 loss:0.010 grdn:0.212 lr:9.6e-06 updt_s:0.059 data_s:0.058
+Training:  12%|██████▍                                             | 3719/30000 [07:29<56:37,  7.73step/s]INFO 2026-05-19 01:19:21 ot_train.py:488 step:4K smpl:119K ep:199 epch:1.49 loss:0.011 grdn:0.226 lr:9.6e-06 updt_s:0.058 data_s:0.061
+Training:  12%|██████▍                                             | 3739/30000 [07:31<57:14,  7.65step/s]INFO 2026-05-19 01:19:24 ot_train.py:488 step:4K smpl:120K ep:200 epch:1.49 loss:0.011 grdn:0.220 lr:9.6e-06 updt_s:0.058 data_s:0.061
+Training:  13%|██████▌                                             | 3759/30000 [07:33<53:52,  8.12step/s]INFO 2026-05-19 01:19:26 ot_train.py:488 step:4K smpl:120K ep:201 epch:1.50 loss:0.009 grdn:0.200 lr:9.6e-06 updt_s:0.058 data_s:0.059
+Training:  13%|██████▌                                             | 3779/30000 [07:36<57:52,  7.55step/s]INFO 2026-05-19 01:19:29 ot_train.py:488 step:4K smpl:121K ep:202 epch:1.51 loss:0.010 grdn:0.197 lr:9.6e-06 updt_s:0.059 data_s:0.061
+Training:  13%|██████▌                                             | 3799/30000 [07:38<57:58,  7.53step/s]INFO 2026-05-19 01:19:31 ot_train.py:488 step:4K smpl:122K ep:203 epch:1.52 loss:0.011 grdn:0.222 lr:9.6e-06 updt_s:0.059 data_s:0.064
+Training:  13%|██████▌                                             | 3819/30000 [07:41<56:38,  7.70step/s]INFO 2026-05-19 01:19:33 ot_train.py:488 step:4K smpl:122K ep:205 epch:1.53 loss:0.009 grdn:0.201 lr:9.6e-06 updt_s:0.058 data_s:0.061
+Training:  13%|██████▋                                             | 3839/30000 [07:43<55:52,  7.80step/s]INFO 2026-05-19 01:19:36 ot_train.py:488 step:4K smpl:123K ep:206 epch:1.53 loss:0.011 grdn:0.223 lr:9.6e-06 updt_s:0.058 data_s:0.060
+Training:  13%|██████▋                                             | 3859/30000 [07:45<55:49,  7.80step/s]INFO 2026-05-19 01:19:38 ot_train.py:488 step:4K smpl:124K ep:207 epch:1.54 loss:0.012 grdn:0.208 lr:9.6e-06 updt_s:0.059 data_s:0.058
+Training:  13%|██████▋                                             | 3879/30000 [07:48<56:50,  7.66step/s]INFO 2026-05-19 01:19:41 ot_train.py:488 step:4K smpl:124K ep:208 epch:1.55 loss:0.011 grdn:0.213 lr:9.6e-06 updt_s:0.060 data_s:0.061
+Training:  13%|██████▊                                             | 3899/30000 [07:50<57:02,  7.63step/s]INFO 2026-05-19 01:19:43 ot_train.py:488 step:4K smpl:125K ep:209 epch:1.56 loss:0.009 grdn:0.209 lr:9.6e-06 updt_s:0.061 data_s:0.061
+Training:  13%|██████▊                                             | 3919/30000 [07:53<56:11,  7.74step/s]INFO 2026-05-19 01:19:45 ot_train.py:488 step:4K smpl:125K ep:210 epch:1.57 loss:0.011 grdn:0.203 lr:9.6e-06 updt_s:0.059 data_s:0.060
+Training:  13%|██████▌                                           | 3939/30000 [07:55<1:00:34,  7.17step/s]INFO 2026-05-19 01:19:48 ot_train.py:488 step:4K smpl:126K ep:211 epch:1.57 loss:0.011 grdn:0.212 lr:9.6e-06 updt_s:0.059 data_s:0.072
+Training:  13%|██████▊                                             | 3959/30000 [07:58<55:17,  7.85step/s]INFO 2026-05-19 01:19:50 ot_train.py:488 step:4K smpl:127K ep:212 epch:1.58 loss:0.011 grdn:0.232 lr:9.6e-06 updt_s:0.059 data_s:0.059
+Training:  13%|██████▉                                             | 3979/30000 [08:00<56:01,  7.74step/s]INFO 2026-05-19 01:19:53 ot_train.py:488 step:4K smpl:127K ep:213 epch:1.59 loss:0.012 grdn:0.239 lr:9.6e-06 updt_s:0.059 data_s:0.061
+Training:  13%|██████▉                                             | 3999/30000 [08:03<55:55,  7.75step/s]INFO 2026-05-19 01:19:55 ot_train.py:488 step:4K smpl:128K ep:214 epch:1.60 loss:0.011 grdn:0.248 lr:9.6e-06 updt_s:0.059 data_s:0.059
+Training:  13%|██████▋                                           | 4019/30000 [08:05<1:11:12,  6.08step/s]INFO 2026-05-19 01:19:58 ot_train.py:488 step:4K smpl:129K ep:215 epch:1.61 loss:0.011 grdn:0.207 lr:9.6e-06 updt_s:0.059 data_s:0.073
+Training:  13%|███████                                             | 4039/30000 [08:08<57:16,  7.56step/s]INFO 2026-05-19 01:20:00 ot_train.py:488 step:4K smpl:129K ep:216 epch:1.61 loss:0.012 grdn:0.225 lr:9.6e-06 updt_s:0.058 data_s:0.061
+Training:  14%|███████                                             | 4059/30000 [08:10<55:49,  7.75step/s]INFO 2026-05-19 01:20:03 ot_train.py:488 step:4K smpl:130K ep:217 epch:1.62 loss:0.011 grdn:0.191 lr:9.6e-06 updt_s:0.059 data_s:0.061
+Training:  14%|███████                                             | 4079/30000 [08:12<56:02,  7.71step/s]INFO 2026-05-19 01:20:05 ot_train.py:488 step:4K smpl:131K ep:218 epch:1.63 loss:0.010 grdn:0.195 lr:9.6e-06 updt_s:0.059 data_s:0.059
+Training:  14%|███████                                             | 4099/30000 [08:15<54:15,  7.96step/s]INFO 2026-05-19 01:20:07 ot_train.py:488 step:4K smpl:131K ep:220 epch:1.64 loss:0.013 grdn:0.224 lr:9.6e-06 updt_s:0.059 data_s:0.058
+Training:  14%|███████▏                                            | 4119/30000 [08:17<59:45,  7.22step/s]INFO 2026-05-19 01:20:10 ot_train.py:488 step:4K smpl:132K ep:221 epch:1.65 loss:0.010 grdn:0.190 lr:9.6e-06 updt_s:0.060 data_s:0.067
+Training:  14%|███████▏                                            | 4139/30000 [08:20<56:21,  7.65step/s]INFO 2026-05-19 01:20:12 ot_train.py:488 step:4K smpl:132K ep:222 epch:1.65 loss:0.010 grdn:0.198 lr:9.6e-06 updt_s:0.058 data_s:0.060
+Training:  14%|███████▏                                            | 4159/30000 [08:22<55:59,  7.69step/s]INFO 2026-05-19 01:20:15 ot_train.py:488 step:4K smpl:133K ep:223 epch:1.66 loss:0.012 grdn:0.254 lr:9.6e-06 updt_s:0.058 data_s:0.059
+Training:  14%|███████▏                                            | 4179/30000 [08:24<58:02,  7.41step/s]INFO 2026-05-19 01:20:17 ot_train.py:488 step:4K smpl:134K ep:224 epch:1.67 loss:0.011 grdn:0.199 lr:9.5e-06 updt_s:0.058 data_s:0.062
+Training:  14%|███████▎                                            | 4199/30000 [08:27<55:25,  7.76step/s]INFO 2026-05-19 01:20:20 ot_train.py:488 step:4K smpl:134K ep:225 epch:1.68 loss:0.010 grdn:0.211 lr:9.5e-06 updt_s:0.060 data_s:0.059
+Training:  14%|███████▎                                            | 4219/30000 [08:29<57:23,  7.49step/s]INFO 2026-05-19 01:20:22 ot_train.py:488 step:4K smpl:135K ep:226 epch:1.69 loss:0.009 grdn:0.192 lr:9.5e-06 updt_s:0.060 data_s:0.060
+Training:  14%|███████▎                                            | 4239/30000 [08:32<55:42,  7.71step/s]INFO 2026-05-19 01:20:24 ot_train.py:488 step:4K smpl:136K ep:227 epch:1.69 loss:0.011 grdn:0.202 lr:9.5e-06 updt_s:0.060 data_s:0.060
+Training:  14%|███████▍                                            | 4259/30000 [08:34<56:51,  7.54step/s]INFO 2026-05-19 01:20:27 ot_train.py:488 step:4K smpl:136K ep:228 epch:1.70 loss:0.011 grdn:0.216 lr:9.5e-06 updt_s:0.059 data_s:0.062
+Training:  14%|███████▍                                            | 4279/30000 [08:36<55:30,  7.72step/s]INFO 2026-05-19 01:20:29 ot_train.py:488 step:4K smpl:137K ep:229 epch:1.71 loss:0.011 grdn:0.216 lr:9.5e-06 updt_s:0.058 data_s:0.059
+Training:  14%|███████▍                                            | 4299/30000 [08:39<56:11,  7.62step/s]INFO 2026-05-19 01:20:32 ot_train.py:488 step:4K smpl:138K ep:230 epch:1.72 loss:0.011 grdn:0.201 lr:9.5e-06 updt_s:0.060 data_s:0.059
+Training:  14%|███████▍                                            | 4319/30000 [08:41<53:00,  8.07step/s]INFO 2026-05-19 01:20:34 ot_train.py:488 step:4K smpl:138K ep:231 epch:1.73 loss:0.011 grdn:0.214 lr:9.5e-06 updt_s:0.059 data_s:0.060
+Training:  14%|███████▌                                            | 4339/30000 [08:44<55:10,  7.75step/s]INFO 2026-05-19 01:20:36 ot_train.py:488 step:4K smpl:139K ep:232 epch:1.73 loss:0.011 grdn:0.219 lr:9.5e-06 updt_s:0.059 data_s:0.062
+Training:  15%|███████▌                                            | 4359/30000 [08:46<54:41,  7.81step/s]INFO 2026-05-19 01:20:39 ot_train.py:488 step:4K smpl:140K ep:233 epch:1.74 loss:0.009 grdn:0.195 lr:9.5e-06 updt_s:0.058 data_s:0.061
+Training:  15%|███████▌                                            | 4379/30000 [08:49<56:22,  7.58step/s]INFO 2026-05-19 01:20:41 ot_train.py:488 step:4K smpl:140K ep:235 epch:1.75 loss:0.013 grdn:0.241 lr:9.5e-06 updt_s:0.059 data_s:0.060
+Training:  15%|███████▌                                            | 4399/30000 [08:51<54:36,  7.81step/s]INFO 2026-05-19 01:20:44 ot_train.py:488 step:4K smpl:141K ep:236 epch:1.76 loss:0.010 grdn:0.199 lr:9.5e-06 updt_s:0.059 data_s:0.060
+Training:  15%|███████▋                                            | 4419/30000 [08:53<55:39,  7.66step/s]INFO 2026-05-19 01:20:46 ot_train.py:488 step:4K smpl:141K ep:237 epch:1.77 loss:0.010 grdn:0.190 lr:9.5e-06 updt_s:0.059 data_s:0.059
+Training:  15%|███████▋                                            | 4439/30000 [08:56<53:45,  7.92step/s]INFO 2026-05-19 01:20:48 ot_train.py:488 step:4K smpl:142K ep:238 epch:1.77 loss:0.012 grdn:0.225 lr:9.5e-06 updt_s:0.059 data_s:0.058
+Training:  15%|███████▋                                            | 4459/30000 [08:58<54:54,  7.75step/s]INFO 2026-05-19 01:20:51 ot_train.py:488 step:4K smpl:143K ep:239 epch:1.78 loss:0.011 grdn:0.229 lr:9.5e-06 updt_s:0.059 data_s:0.061
+Training:  15%|███████▊                                            | 4479/30000 [09:00<55:34,  7.65step/s]INFO 2026-05-19 01:20:53 ot_train.py:488 step:4K smpl:143K ep:240 epch:1.79 loss:0.011 grdn:0.231 lr:9.5e-06 updt_s:0.058 data_s:0.063
+Training:  15%|███████▊                                            | 4499/30000 [09:03<51:09,  8.31step/s]INFO 2026-05-19 01:20:56 ot_train.py:488 step:4K smpl:144K ep:241 epch:1.80 loss:0.012 grdn:0.220 lr:9.5e-06 updt_s:0.059 data_s:0.058
+Training:  15%|███████▊                                            | 4519/30000 [09:05<55:18,  7.68step/s]INFO 2026-05-19 01:20:58 ot_train.py:488 step:5K smpl:145K ep:242 epch:1.81 loss:0.010 grdn:0.201 lr:9.5e-06 updt_s:0.059 data_s:0.060
+Training:  15%|███████▊                                            | 4539/30000 [09:08<54:00,  7.86step/s]INFO 2026-05-19 01:21:00 ot_train.py:488 step:5K smpl:145K ep:243 epch:1.81 loss:0.011 grdn:0.214 lr:9.5e-06 updt_s:0.058 data_s:0.060
+Training:  15%|███████▉                                            | 4559/30000 [09:10<53:09,  7.98step/s]INFO 2026-05-19 01:21:03 ot_train.py:488 step:5K smpl:146K ep:244 epch:1.82 loss:0.010 grdn:0.192 lr:9.5e-06 updt_s:0.058 data_s:0.060
+Training:  15%|███████▉                                            | 4579/30000 [09:12<55:18,  7.66step/s]INFO 2026-05-19 01:21:05 ot_train.py:488 step:5K smpl:147K ep:245 epch:1.83 loss:0.010 grdn:0.189 lr:9.5e-06 updt_s:0.059 data_s:0.060
+Training:  15%|███████▉                                            | 4599/30000 [09:15<55:36,  7.61step/s]INFO 2026-05-19 01:21:08 ot_train.py:488 step:5K smpl:147K ep:246 epch:1.84 loss:0.010 grdn:0.217 lr:9.5e-06 updt_s:0.058 data_s:0.071
+Training:  15%|████████                                            | 4619/30000 [09:17<56:45,  7.45step/s]INFO 2026-05-19 01:21:10 ot_train.py:488 step:5K smpl:148K ep:247 epch:1.85 loss:0.009 grdn:0.196 lr:9.4e-06 updt_s:0.059 data_s:0.060
+Training:  15%|████████                                            | 4639/30000 [09:20<54:24,  7.77step/s]INFO 2026-05-19 01:21:13 ot_train.py:488 step:5K smpl:148K ep:248 epch:1.85 loss:0.011 grdn:0.223 lr:9.4e-06 updt_s:0.059 data_s:0.062
+Training:  16%|████████                                            | 4659/30000 [09:22<55:15,  7.64step/s]INFO 2026-05-19 01:21:15 ot_train.py:488 step:5K smpl:149K ep:250 epch:1.86 loss:0.011 grdn:0.220 lr:9.4e-06 updt_s:0.059 data_s:0.058
+Training:  16%|████████                                            | 4679/30000 [09:25<55:13,  7.64step/s]INFO 2026-05-19 01:21:17 ot_train.py:488 step:5K smpl:150K ep:251 epch:1.87 loss:0.012 grdn:0.205 lr:9.4e-06 updt_s:0.058 data_s:0.062
+Training:  16%|████████▏                                           | 4699/30000 [09:27<56:06,  7.52step/s]INFO 2026-05-19 01:21:20 ot_train.py:488 step:5K smpl:150K ep:252 epch:1.88 loss:0.015 grdn:0.222 lr:9.4e-06 updt_s:0.059 data_s:0.062
+Training:  16%|████████▏                                           | 4719/30000 [09:29<55:06,  7.64step/s]INFO 2026-05-19 01:21:22 ot_train.py:488 step:5K smpl:151K ep:253 epch:1.89 loss:0.012 grdn:0.221 lr:9.4e-06 updt_s:0.059 data_s:0.061
+Training:  16%|████████▏                                           | 4739/30000 [09:32<55:06,  7.64step/s]INFO 2026-05-19 01:21:25 ot_train.py:488 step:5K smpl:152K ep:254 epch:1.89 loss:0.010 grdn:0.191 lr:9.4e-06 updt_s:0.058 data_s:0.062
+Training:  16%|████████▏                                           | 4759/30000 [09:34<53:55,  7.80step/s]INFO 2026-05-19 01:21:27 ot_train.py:488 step:5K smpl:152K ep:255 epch:1.90 loss:0.011 grdn:0.209 lr:9.4e-06 updt_s:0.058 data_s:0.059
+Training:  16%|████████▎                                           | 4779/30000 [09:37<51:09,  8.22step/s]INFO 2026-05-19 01:21:29 ot_train.py:488 step:5K smpl:153K ep:256 epch:1.91 loss:0.011 grdn:0.211 lr:9.4e-06 updt_s:0.058 data_s:0.058
+Training:  16%|████████▎                                           | 4799/30000 [09:39<54:00,  7.78step/s]INFO 2026-05-19 01:21:32 ot_train.py:488 step:5K smpl:154K ep:257 epch:1.92 loss:0.011 grdn:0.210 lr:9.4e-06 updt_s:0.058 data_s:0.060
+Training:  16%|████████▎                                           | 4819/30000 [09:41<54:25,  7.71step/s]INFO 2026-05-19 01:21:34 ot_train.py:488 step:5K smpl:154K ep:258 epch:1.93 loss:0.011 grdn:0.212 lr:9.4e-06 updt_s:0.058 data_s:0.060
+Training:  16%|████████▍                                           | 4839/30000 [09:44<54:10,  7.74step/s]INFO 2026-05-19 01:21:36 ot_train.py:488 step:5K smpl:155K ep:259 epch:1.93 loss:0.012 grdn:0.214 lr:9.4e-06 updt_s:0.058 data_s:0.059
+Training:  16%|████████▍                                           | 4859/30000 [09:46<54:06,  7.74step/s]INFO 2026-05-19 01:21:39 ot_train.py:488 step:5K smpl:156K ep:260 epch:1.94 loss:0.009 grdn:0.202 lr:9.4e-06 updt_s:0.059 data_s:0.061
+Training:  16%|████████▍                                           | 4879/30000 [09:48<53:52,  7.77step/s]INFO 2026-05-19 01:21:41 ot_train.py:488 step:5K smpl:156K ep:261 epch:1.95 loss:0.011 grdn:0.220 lr:9.4e-06 updt_s:0.059 data_s:0.058
+Training:  16%|████████▍                                           | 4899/30000 [09:51<54:44,  7.64step/s]INFO 2026-05-19 01:21:44 ot_train.py:488 step:5K smpl:157K ep:262 epch:1.96 loss:0.011 grdn:0.205 lr:9.4e-06 updt_s:0.059 data_s:0.060
+Training:  16%|████████▌                                           | 4919/30000 [09:53<54:03,  7.73step/s]INFO 2026-05-19 01:21:46 ot_train.py:488 step:5K smpl:157K ep:263 epch:1.97 loss:0.012 grdn:0.222 lr:9.4e-06 updt_s:0.059 data_s:0.058
+Training:  16%|████████▌                                           | 4939/30000 [09:55<48:43,  8.57step/s]INFO 2026-05-19 01:21:48 ot_train.py:488 step:5K smpl:158K ep:264 epch:1.97 loss:0.011 grdn:0.196 lr:9.4e-06 updt_s:0.058 data_s:0.053
+Training:  17%|████████▌                                           | 4959/30000 [09:58<49:11,  8.49step/s]INFO 2026-05-19 01:21:51 ot_train.py:488 step:5K smpl:159K ep:266 epch:1.98 loss:0.010 grdn:0.202 lr:9.4e-06 updt_s:0.057 data_s:0.070
+Training:  17%|████████▋                                           | 4979/30000 [10:00<45:48,  9.11step/s]INFO 2026-05-19 01:21:53 ot_train.py:488 step:5K smpl:159K ep:267 epch:1.99 loss:0.011 grdn:0.203 lr:9.4e-06 updt_s:0.059 data_s:0.056
+Training:  17%|████████▋                                           | 4999/30000 [10:03<46:03,  9.05step/s]INFO 2026-05-19 01:21:55 ot_train.py:488 step:5K smpl:160K ep:268 epch:2.00 loss:0.010 grdn:0.201 lr:9.4e-06 updt_s:0.058 data_s:0.061
+INFO 2026-05-19 01:21:55 ot_train.py:502 Checkpoint policy after step 5000
+Training:  17%|████████▋                                           | 5019/30000 [10:11<45:38,  9.12step/s]INFO 2026-05-19 01:22:04 ot_train.py:488 step:5K smpl:161K ep:269 epch:2.01 loss:0.010 grdn:0.218 lr:9.3e-06 updt_s:0.059 data_s:0.005
+Training:  17%|████████▋                                           | 5039/30000 [10:14<52:43,  7.89step/s]INFO 2026-05-19 01:22:06 ot_train.py:488 step:5K smpl:161K ep:270 epch:2.01 loss:0.009 grdn:0.201 lr:9.3e-06 updt_s:0.060 data_s:0.060
+Training:  17%|████████▊                                           | 5059/30000 [10:16<47:15,  8.79step/s]INFO 2026-05-19 01:22:09 ot_train.py:488 step:5K smpl:162K ep:271 epch:2.02 loss:0.011 grdn:0.210 lr:9.3e-06 updt_s:0.060 data_s:0.062
+Training:  17%|████████▊                                           | 5079/30000 [10:19<46:25,  8.95step/s]INFO 2026-05-19 01:22:11 ot_train.py:488 step:5K smpl:163K ep:272 epch:2.03 loss:0.012 grdn:0.221 lr:9.3e-06 updt_s:0.060 data_s:0.062
+Training:  17%|████████▊                                           | 5099/30000 [10:21<50:43,  8.18step/s]INFO 2026-05-19 01:22:14 ot_train.py:488 step:5K smpl:163K ep:273 epch:2.04 loss:0.011 grdn:0.219 lr:9.3e-06 updt_s:0.060 data_s:0.071
+Training:  17%|████████▊                                           | 5119/30000 [10:24<45:55,  9.03step/s]INFO 2026-05-19 01:22:16 ot_train.py:488 step:5K smpl:164K ep:274 epch:2.05 loss:0.011 grdn:0.221 lr:9.3e-06 updt_s:0.059 data_s:0.061
+Training:  17%|████████▉                                           | 5139/30000 [10:26<46:33,  8.90step/s]INFO 2026-05-19 01:22:19 ot_train.py:488 step:5K smpl:164K ep:275 epch:2.05 loss:0.010 grdn:0.197 lr:9.3e-06 updt_s:0.059 data_s:0.062
+Training:  17%|████████▉                                           | 5159/30000 [10:28<46:03,  8.99step/s]INFO 2026-05-19 01:22:21 ot_train.py:488 step:5K smpl:165K ep:276 epch:2.06 loss:0.011 grdn:0.245 lr:9.3e-06 updt_s:0.060 data_s:0.061
+Training:  17%|████████▉                                           | 5179/30000 [10:31<46:43,  8.85step/s]INFO 2026-05-19 01:22:24 ot_train.py:488 step:5K smpl:166K ep:277 epch:2.07 loss:0.010 grdn:0.207 lr:9.3e-06 updt_s:0.059 data_s:0.064
+Training:  17%|█████████                                           | 5199/30000 [10:33<46:57,  8.80step/s]INFO 2026-05-19 01:22:26 ot_train.py:488 step:5K smpl:166K ep:278 epch:2.08 loss:0.011 grdn:0.202 lr:9.3e-06 updt_s:0.058 data_s:0.063
+Training:  17%|█████████                                           | 5219/30000 [10:36<45:34,  9.06step/s]INFO 2026-05-19 01:22:29 ot_train.py:488 step:5K smpl:167K ep:279 epch:2.09 loss:0.008 grdn:0.183 lr:9.3e-06 updt_s:0.059 data_s:0.060
+Training:  17%|█████████                                           | 5239/30000 [10:38<45:41,  9.03step/s]INFO 2026-05-19 01:22:31 ot_train.py:488 step:5K smpl:168K ep:281 epch:2.09 loss:0.010 grdn:0.203 lr:9.3e-06 updt_s:0.058 data_s:0.062
+Training:  18%|█████████                                           | 5259/30000 [10:41<46:14,  8.92step/s]INFO 2026-05-19 01:22:33 ot_train.py:488 step:5K smpl:168K ep:282 epch:2.10 loss:0.012 grdn:0.245 lr:9.3e-06 updt_s:0.059 data_s:0.062
+Training:  18%|█████████▏                                          | 5279/30000 [10:43<44:58,  9.16step/s]INFO 2026-05-19 01:22:36 ot_train.py:488 step:5K smpl:169K ep:283 epch:2.11 loss:0.010 grdn:0.204 lr:9.3e-06 updt_s:0.059 data_s:0.061
+Training:  18%|█████████▏                                          | 5299/30000 [10:46<44:20,  9.28step/s]INFO 2026-05-19 01:22:38 ot_train.py:488 step:5K smpl:170K ep:284 epch:2.12 loss:0.008 grdn:0.183 lr:9.3e-06 updt_s:0.058 data_s:0.065
+Training:  18%|█████████▏                                          | 5319/30000 [10:48<43:58,  9.36step/s]INFO 2026-05-19 01:22:41 ot_train.py:488 step:5K smpl:170K ep:285 epch:2.13 loss:0.010 grdn:0.204 lr:9.3e-06 updt_s:0.058 data_s:0.059
+Training:  18%|█████████▎                                          | 5339/30000 [10:50<45:44,  8.98step/s]INFO 2026-05-19 01:22:43 ot_train.py:488 step:5K smpl:171K ep:286 epch:2.13 loss:0.009 grdn:0.196 lr:9.3e-06 updt_s:0.058 data_s:0.063
+Training:  18%|█████████▎                                          | 5359/30000 [10:53<51:19,  8.00step/s]INFO 2026-05-19 01:22:46 ot_train.py:488 step:5K smpl:172K ep:287 epch:2.14 loss:0.011 grdn:0.219 lr:9.3e-06 updt_s:0.058 data_s:0.067
+Training:  18%|█████████▎                                          | 5379/30000 [10:55<51:02,  8.04step/s]INFO 2026-05-19 01:22:48 ot_train.py:488 step:5K smpl:172K ep:288 epch:2.15 loss:0.013 grdn:0.218 lr:9.3e-06 updt_s:0.059 data_s:0.065
+Training:  18%|█████████▎                                          | 5399/30000 [10:58<48:22,  8.47step/s]INFO 2026-05-19 01:22:51 ot_train.py:488 step:5K smpl:173K ep:289 epch:2.16 loss:0.009 grdn:0.193 lr:9.2e-06 updt_s:0.058 data_s:0.072
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+Training:  18%|█████████▍                                          | 5479/30000 [11:08<45:55,  8.90step/s]INFO 2026-05-19 01:23:00 ot_train.py:488 step:5K smpl:175K ep:293 epch:2.19 loss:0.011 grdn:0.201 lr:9.2e-06 updt_s:0.059 data_s:0.064
+Training:  18%|█████████▌                                          | 5499/30000 [11:10<45:35,  8.96step/s]INFO 2026-05-19 01:23:03 ot_train.py:488 step:6K smpl:176K ep:294 epch:2.20 loss:0.010 grdn:0.197 lr:9.2e-06 updt_s:0.059 data_s:0.061
+Training:  18%|█████████▌                                          | 5519/30000 [11:12<45:08,  9.04step/s]INFO 2026-05-19 01:23:05 ot_train.py:488 step:6K smpl:177K ep:296 epch:2.21 loss:0.011 grdn:0.207 lr:9.2e-06 updt_s:0.059 data_s:0.059
+Training:  18%|█████████▌                                          | 5539/30000 [11:15<45:18,  9.00step/s]INFO 2026-05-19 01:23:08 ot_train.py:488 step:6K smpl:177K ep:297 epch:2.21 loss:0.010 grdn:0.197 lr:9.2e-06 updt_s:0.059 data_s:0.058
+Training:  19%|█████████▋                                          | 5559/30000 [11:17<41:45,  9.75step/s]INFO 2026-05-19 01:23:10 ot_train.py:488 step:6K smpl:178K ep:298 epch:2.22 loss:0.012 grdn:0.245 lr:9.2e-06 updt_s:0.058 data_s:0.064
+Training:  19%|█████████▋                                          | 5579/30000 [11:20<44:16,  9.19step/s]INFO 2026-05-19 01:23:12 ot_train.py:488 step:6K smpl:179K ep:299 epch:2.23 loss:0.010 grdn:0.206 lr:9.2e-06 updt_s:0.059 data_s:0.057
+Training:  19%|█████████▋                                          | 5599/30000 [11:22<44:49,  9.07step/s]INFO 2026-05-19 01:23:15 ot_train.py:488 step:6K smpl:179K ep:300 epch:2.24 loss:0.012 grdn:0.235 lr:9.2e-06 updt_s:0.058 data_s:0.062
+Training:  19%|█████████▋                                          | 5619/30000 [11:24<45:16,  8.97step/s]INFO 2026-05-19 01:23:17 ot_train.py:488 step:6K smpl:180K ep:301 epch:2.25 loss:0.011 grdn:0.205 lr:9.2e-06 updt_s:0.058 data_s:0.061
+Training:  19%|█████████▊                                          | 5639/30000 [11:27<46:03,  8.82step/s]INFO 2026-05-19 01:23:20 ot_train.py:488 step:6K smpl:180K ep:302 epch:2.25 loss:0.009 grdn:0.181 lr:9.2e-06 updt_s:0.059 data_s:0.063
+Training:  19%|█████████▊                                          | 5659/30000 [11:29<45:04,  9.00step/s]INFO 2026-05-19 01:23:22 ot_train.py:488 step:6K smpl:181K ep:303 epch:2.26 loss:0.010 grdn:0.208 lr:9.2e-06 updt_s:0.059 data_s:0.060
+Training:  19%|█████████▊                                          | 5679/30000 [11:32<45:21,  8.94step/s]INFO 2026-05-19 01:23:24 ot_train.py:488 step:6K smpl:182K ep:304 epch:2.27 loss:0.010 grdn:0.217 lr:9.2e-06 updt_s:0.058 data_s:0.061
+Training:  19%|█████████▉                                          | 5699/30000 [11:34<43:29,  9.31step/s]INFO 2026-05-19 01:23:27 ot_train.py:488 step:6K smpl:182K ep:305 epch:2.28 loss:0.010 grdn:0.193 lr:9.2e-06 updt_s:0.058 data_s:0.059
+Training:  19%|█████████▉                                          | 5719/30000 [11:36<43:38,  9.27step/s]INFO 2026-05-19 01:23:29 ot_train.py:488 step:6K smpl:183K ep:306 epch:2.29 loss:0.011 grdn:0.218 lr:9.2e-06 updt_s:0.059 data_s:0.059
+Training:  19%|█████████▉                                          | 5739/30000 [11:39<44:48,  9.02step/s]INFO 2026-05-19 01:23:32 ot_train.py:488 step:6K smpl:184K ep:307 epch:2.29 loss:0.009 grdn:0.200 lr:9.2e-06 updt_s:0.059 data_s:0.063
+Training:  19%|█████████▉                                          | 5759/30000 [11:41<44:31,  9.07step/s]INFO 2026-05-19 01:23:34 ot_train.py:488 step:6K smpl:184K ep:308 epch:2.30 loss:0.010 grdn:0.194 lr:9.1e-06 updt_s:0.058 data_s:0.061
+Training:  19%|██████████                                          | 5779/30000 [11:44<44:59,  8.97step/s]INFO 2026-05-19 01:23:36 ot_train.py:488 step:6K smpl:185K ep:309 epch:2.31 loss:0.011 grdn:0.196 lr:9.1e-06 updt_s:0.060 data_s:0.061
+Training:  19%|██████████                                          | 5799/30000 [11:46<44:14,  9.12step/s]INFO 2026-05-19 01:23:39 ot_train.py:488 step:6K smpl:186K ep:311 epch:2.32 loss:0.010 grdn:0.187 lr:9.1e-06 updt_s:0.059 data_s:0.061
+Training:  19%|██████████                                          | 5819/30000 [11:49<44:32,  9.05step/s]INFO 2026-05-19 01:23:41 ot_train.py:488 step:6K smpl:186K ep:312 epch:2.33 loss:0.010 grdn:0.193 lr:9.1e-06 updt_s:0.058 data_s:0.063
+Training:  19%|██████████                                          | 5839/30000 [11:51<53:33,  7.52step/s]INFO 2026-05-19 01:23:44 ot_train.py:488 step:6K smpl:187K ep:313 epch:2.33 loss:0.010 grdn:0.196 lr:9.1e-06 updt_s:0.060 data_s:0.074
+Training:  20%|██████████▏                                         | 5859/30000 [11:54<43:58,  9.15step/s]INFO 2026-05-19 01:23:46 ot_train.py:488 step:6K smpl:188K ep:314 epch:2.34 loss:0.011 grdn:0.214 lr:9.1e-06 updt_s:0.061 data_s:0.055
+Training:  20%|██████████▏                                         | 5879/30000 [11:56<45:11,  8.90step/s]INFO 2026-05-19 01:23:49 ot_train.py:488 step:6K smpl:188K ep:315 epch:2.35 loss:0.011 grdn:0.225 lr:9.1e-06 updt_s:0.060 data_s:0.058
+Training:  20%|██████████▏                                         | 5899/30000 [11:58<43:21,  9.27step/s]INFO 2026-05-19 01:23:51 ot_train.py:488 step:6K smpl:189K ep:316 epch:2.36 loss:0.009 grdn:0.198 lr:9.1e-06 updt_s:0.061 data_s:0.058
+Training:  20%|██████████▎                                         | 5919/30000 [12:01<43:11,  9.29step/s]INFO 2026-05-19 01:23:54 ot_train.py:488 step:6K smpl:189K ep:317 epch:2.37 loss:0.012 grdn:0.219 lr:9.1e-06 updt_s:0.059 data_s:0.061
+Training:  20%|██████████▎                                         | 5939/30000 [12:03<43:15,  9.27step/s]INFO 2026-05-19 01:23:56 ot_train.py:488 step:6K smpl:190K ep:318 epch:2.37 loss:0.009 grdn:0.212 lr:9.1e-06 updt_s:0.059 data_s:0.057
+Training:  20%|██████████▎                                         | 5959/30000 [12:06<43:55,  9.12step/s]INFO 2026-05-19 01:23:58 ot_train.py:488 step:6K smpl:191K ep:319 epch:2.38 loss:0.011 grdn:0.227 lr:9.1e-06 updt_s:0.059 data_s:0.062
+Training:  20%|██████████▎                                         | 5979/30000 [12:08<43:33,  9.19step/s]INFO 2026-05-19 01:24:01 ot_train.py:488 step:6K smpl:191K ep:320 epch:2.39 loss:0.010 grdn:0.213 lr:9.1e-06 updt_s:0.060 data_s:0.060
+Training:  20%|██████████▍                                         | 5999/30000 [12:10<44:43,  8.94step/s]INFO 2026-05-19 01:24:03 ot_train.py:488 step:6K smpl:192K ep:321 epch:2.40 loss:0.010 grdn:0.212 lr:9.1e-06 updt_s:0.059 data_s:0.060
+Training:  20%|██████████▍                                         | 6019/30000 [12:13<44:57,  8.89step/s]INFO 2026-05-19 01:24:05 ot_train.py:488 step:6K smpl:193K ep:322 epch:2.41 loss:0.011 grdn:0.217 lr:9.1e-06 updt_s:0.060 data_s:0.059
+Training:  20%|██████████▍                                         | 6039/30000 [12:15<44:13,  9.03step/s]INFO 2026-05-19 01:24:08 ot_train.py:488 step:6K smpl:193K ep:323 epch:2.41 loss:0.009 grdn:0.186 lr:9.1e-06 updt_s:0.060 data_s:0.057
+Training:  20%|██████████▌                                         | 6059/30000 [12:17<42:25,  9.41step/s]INFO 2026-05-19 01:24:10 ot_train.py:488 step:6K smpl:194K ep:324 epch:2.42 loss:0.011 grdn:0.213 lr:9.1e-06 updt_s:0.059 data_s:0.054
+Training:  20%|██████████▌                                         | 6079/30000 [12:20<43:46,  9.11step/s]INFO 2026-05-19 01:24:12 ot_train.py:488 step:6K smpl:195K ep:326 epch:2.43 loss:0.010 grdn:0.205 lr:9.0e-06 updt_s:0.059 data_s:0.060
+Training:  20%|██████████▌                                         | 6099/30000 [12:22<43:45,  9.10step/s]INFO 2026-05-19 01:24:15 ot_train.py:488 step:6K smpl:195K ep:327 epch:2.44 loss:0.010 grdn:0.203 lr:9.0e-06 updt_s:0.060 data_s:0.060
+Training:  20%|██████████▌                                         | 6119/30000 [12:25<42:22,  9.39step/s]INFO 2026-05-19 01:24:17 ot_train.py:488 step:6K smpl:196K ep:328 epch:2.45 loss:0.011 grdn:0.228 lr:9.0e-06 updt_s:0.059 data_s:0.060
+Training:  20%|██████████▋                                         | 6139/30000 [12:27<43:29,  9.14step/s]INFO 2026-05-19 01:24:20 ot_train.py:488 step:6K smpl:196K ep:329 epch:2.45 loss:0.009 grdn:0.204 lr:9.0e-06 updt_s:0.058 data_s:0.062
+Training:  21%|██████████▋                                         | 6159/30000 [12:29<42:45,  9.29step/s]INFO 2026-05-19 01:24:22 ot_train.py:488 step:6K smpl:197K ep:330 epch:2.46 loss:0.011 grdn:0.212 lr:9.0e-06 updt_s:0.059 data_s:0.058
+Training:  21%|██████████▋                                         | 6179/30000 [12:32<43:12,  9.19step/s]INFO 2026-05-19 01:24:24 ot_train.py:488 step:6K smpl:198K ep:331 epch:2.47 loss:0.010 grdn:0.204 lr:9.0e-06 updt_s:0.058 data_s:0.060
+Training:  21%|██████████▋                                         | 6199/30000 [12:34<42:41,  9.29step/s]INFO 2026-05-19 01:24:27 ot_train.py:488 step:6K smpl:198K ep:332 epch:2.48 loss:0.010 grdn:0.192 lr:9.0e-06 updt_s:0.058 data_s:0.058
+Training:  21%|██████████▊                                         | 6219/30000 [12:36<42:23,  9.35step/s]INFO 2026-05-19 01:24:29 ot_train.py:488 step:6K smpl:199K ep:333 epch:2.49 loss:0.011 grdn:0.238 lr:9.0e-06 updt_s:0.058 data_s:0.057
+Training:  21%|██████████▊                                         | 6239/30000 [12:39<43:50,  9.03step/s]INFO 2026-05-19 01:24:31 ot_train.py:488 step:6K smpl:200K ep:334 epch:2.49 loss:0.011 grdn:0.224 lr:9.0e-06 updt_s:0.059 data_s:0.062
+Training:  21%|██████████▊                                         | 6259/30000 [12:41<44:47,  8.83step/s]INFO 2026-05-19 01:24:34 ot_train.py:488 step:6K smpl:200K ep:335 epch:2.50 loss:0.010 grdn:0.207 lr:9.0e-06 updt_s:0.059 data_s:0.061
+Training:  21%|██████████▉                                         | 6279/30000 [12:44<43:19,  9.12step/s]INFO 2026-05-19 01:24:36 ot_train.py:488 step:6K smpl:201K ep:336 epch:2.51 loss:0.010 grdn:0.208 lr:9.0e-06 updt_s:0.060 data_s:0.059
+Training:  21%|██████████▉                                         | 6299/30000 [12:46<43:13,  9.14step/s]INFO 2026-05-19 01:24:39 ot_train.py:488 step:6K smpl:202K ep:337 epch:2.52 loss:0.010 grdn:0.214 lr:9.0e-06 updt_s:0.058 data_s:0.062
+Training:  21%|██████████▉                                         | 6319/30000 [12:48<43:41,  9.03step/s]INFO 2026-05-19 01:24:41 ot_train.py:488 step:6K smpl:202K ep:338 epch:2.53 loss:0.011 grdn:0.213 lr:9.0e-06 updt_s:0.059 data_s:0.060
+Training:  21%|██████████▉                                         | 6339/30000 [12:51<43:56,  8.97step/s]INFO 2026-05-19 01:24:44 ot_train.py:488 step:6K smpl:203K ep:339 epch:2.53 loss:0.012 grdn:0.215 lr:9.0e-06 updt_s:0.060 data_s:0.060
+Training:  21%|███████████                                         | 6359/30000 [12:53<43:43,  9.01step/s]INFO 2026-05-19 01:24:46 ot_train.py:488 step:6K smpl:204K ep:341 epch:2.54 loss:0.011 grdn:0.208 lr:9.0e-06 updt_s:0.060 data_s:0.060
+Training:  21%|███████████                                         | 6379/30000 [12:56<43:22,  9.08step/s]INFO 2026-05-19 01:24:48 ot_train.py:488 step:6K smpl:204K ep:342 epch:2.55 loss:0.010 grdn:0.204 lr:9.0e-06 updt_s:0.060 data_s:0.058
+Training:  21%|███████████                                         | 6399/30000 [12:58<42:38,  9.22step/s]INFO 2026-05-19 01:24:51 ot_train.py:488 step:6K smpl:205K ep:343 epch:2.56 loss:0.009 grdn:0.187 lr:8.9e-06 updt_s:0.059 data_s:0.059
+Training:  21%|███████████▏                                        | 6419/30000 [13:00<44:41,  8.79step/s]INFO 2026-05-19 01:24:53 ot_train.py:488 step:6K smpl:205K ep:344 epch:2.57 loss:0.009 grdn:0.204 lr:8.9e-06 updt_s:0.060 data_s:0.062
+Training:  21%|███████████▏                                        | 6439/30000 [13:03<43:48,  8.96step/s]INFO 2026-05-19 01:24:56 ot_train.py:488 step:6K smpl:206K ep:345 epch:2.57 loss:0.010 grdn:0.212 lr:8.9e-06 updt_s:0.060 data_s:0.058
+Training:  22%|███████████▏                                        | 6459/30000 [13:05<43:43,  8.97step/s]INFO 2026-05-19 01:24:58 ot_train.py:488 step:6K smpl:207K ep:346 epch:2.58 loss:0.009 grdn:0.189 lr:8.9e-06 updt_s:0.061 data_s:0.058
+Training:  22%|███████████▏                                        | 6479/30000 [13:08<44:16,  8.85step/s]INFO 2026-05-19 01:25:00 ot_train.py:488 step:6K smpl:207K ep:347 epch:2.59 loss:0.010 grdn:0.225 lr:8.9e-06 updt_s:0.060 data_s:0.057
+Training:  22%|███████████▎                                        | 6499/30000 [13:10<43:04,  9.09step/s]INFO 2026-05-19 01:25:03 ot_train.py:488 step:6K smpl:208K ep:348 epch:2.60 loss:0.012 grdn:0.233 lr:8.9e-06 updt_s:0.059 data_s:0.058
+Training:  22%|███████████▎                                        | 6519/30000 [13:12<43:46,  8.94step/s]INFO 2026-05-19 01:25:05 ot_train.py:488 step:7K smpl:209K ep:349 epch:2.61 loss:0.011 grdn:0.227 lr:8.9e-06 updt_s:0.059 data_s:0.059
+Training:  22%|███████████▎                                        | 6539/30000 [13:15<42:51,  9.12step/s]INFO 2026-05-19 01:25:07 ot_train.py:488 step:7K smpl:209K ep:350 epch:2.61 loss:0.009 grdn:0.200 lr:8.9e-06 updt_s:0.062 data_s:0.056
+Training:  22%|███████████▎                                        | 6559/30000 [13:17<43:04,  9.07step/s]INFO 2026-05-19 01:25:10 ot_train.py:488 step:7K smpl:210K ep:351 epch:2.62 loss:0.010 grdn:0.210 lr:8.9e-06 updt_s:0.060 data_s:0.060
+Training:  22%|███████████▍                                        | 6579/30000 [13:20<44:02,  8.86step/s]INFO 2026-05-19 01:25:12 ot_train.py:488 step:7K smpl:211K ep:352 epch:2.63 loss:0.013 grdn:0.227 lr:8.9e-06 updt_s:0.060 data_s:0.060
+Training:  22%|███████████▍                                        | 6599/30000 [13:22<44:48,  8.70step/s]INFO 2026-05-19 01:25:15 ot_train.py:488 step:7K smpl:211K ep:353 epch:2.64 loss:0.010 grdn:0.210 lr:8.9e-06 updt_s:0.060 data_s:0.062
+Training:  22%|███████████▍                                        | 6619/30000 [13:24<42:46,  9.11step/s]INFO 2026-05-19 01:25:17 ot_train.py:488 step:7K smpl:212K ep:354 epch:2.65 loss:0.010 grdn:0.200 lr:8.9e-06 updt_s:0.060 data_s:0.058
+Training:  22%|███████████▌                                        | 6639/30000 [13:27<43:46,  8.89step/s]INFO 2026-05-19 01:25:19 ot_train.py:488 step:7K smpl:212K ep:356 epch:2.65 loss:0.009 grdn:0.195 lr:8.9e-06 updt_s:0.060 data_s:0.060
+Training:  22%|███████████▌                                        | 6659/30000 [13:29<42:31,  9.15step/s]INFO 2026-05-19 01:25:22 ot_train.py:488 step:7K smpl:213K ep:357 epch:2.66 loss:0.010 grdn:0.211 lr:8.9e-06 updt_s:0.060 data_s:0.058
+Training:  22%|███████████▌                                        | 6679/30000 [13:31<41:56,  9.27step/s]INFO 2026-05-19 01:25:24 ot_train.py:488 step:7K smpl:214K ep:358 epch:2.67 loss:0.011 grdn:0.216 lr:8.9e-06 updt_s:0.059 data_s:0.057
+Training:  22%|███████████▌                                        | 6699/30000 [13:34<41:38,  9.33step/s]INFO 2026-05-19 01:25:27 ot_train.py:488 step:7K smpl:214K ep:359 epch:2.68 loss:0.010 grdn:0.211 lr:8.8e-06 updt_s:0.058 data_s:0.059
+Training:  22%|███████████▋                                        | 6719/30000 [13:36<41:27,  9.36step/s]INFO 2026-05-19 01:25:29 ot_train.py:488 step:7K smpl:215K ep:360 epch:2.69 loss:0.011 grdn:0.210 lr:8.8e-06 updt_s:0.058 data_s:0.057
+Training:  22%|███████████▋                                        | 6739/30000 [13:39<41:01,  9.45step/s]INFO 2026-05-19 01:25:31 ot_train.py:488 step:7K smpl:216K ep:361 epch:2.69 loss:0.010 grdn:0.229 lr:8.8e-06 updt_s:0.059 data_s:0.057
+Training:  23%|███████████▋                                        | 6759/30000 [13:41<41:12,  9.40step/s]INFO 2026-05-19 01:25:34 ot_train.py:488 step:7K smpl:216K ep:362 epch:2.70 loss:0.010 grdn:0.207 lr:8.8e-06 updt_s:0.058 data_s:0.058
+Training:  23%|███████████▊                                        | 6779/30000 [13:43<42:09,  9.18step/s]INFO 2026-05-19 01:25:36 ot_train.py:488 step:7K smpl:217K ep:363 epch:2.71 loss:0.009 grdn:0.179 lr:8.8e-06 updt_s:0.058 data_s:0.058
+Training:  23%|███████████▊                                        | 6799/30000 [13:46<41:33,  9.30step/s]INFO 2026-05-19 01:25:38 ot_train.py:488 step:7K smpl:218K ep:364 epch:2.72 loss:0.009 grdn:0.209 lr:8.8e-06 updt_s:0.058 data_s:0.058
+Training:  23%|███████████▊                                        | 6819/30000 [13:48<41:28,  9.31step/s]INFO 2026-05-19 01:25:41 ot_train.py:488 step:7K smpl:218K ep:365 epch:2.73 loss:0.009 grdn:0.191 lr:8.8e-06 updt_s:0.058 data_s:0.058
+Training:  23%|███████████▊                                        | 6839/30000 [13:50<40:47,  9.46step/s]INFO 2026-05-19 01:25:43 ot_train.py:488 step:7K smpl:219K ep:366 epch:2.73 loss:0.010 grdn:0.221 lr:8.8e-06 updt_s:0.058 data_s:0.058
+Training:  23%|███████████▉                                        | 6859/30000 [13:53<41:02,  9.40step/s]INFO 2026-05-19 01:25:45 ot_train.py:488 step:7K smpl:220K ep:367 epch:2.74 loss:0.010 grdn:0.210 lr:8.8e-06 updt_s:0.058 data_s:0.057
+Training:  23%|███████████▉                                        | 6879/30000 [13:55<42:56,  8.97step/s]INFO 2026-05-19 01:25:48 ot_train.py:488 step:7K smpl:220K ep:368 epch:2.75 loss:0.010 grdn:0.225 lr:8.8e-06 updt_s:0.058 data_s:0.062
+Training:  23%|███████████▉                                        | 6899/30000 [13:57<41:50,  9.20step/s]INFO 2026-05-19 01:25:50 ot_train.py:488 step:7K smpl:221K ep:369 epch:2.76 loss:0.009 grdn:0.189 lr:8.8e-06 updt_s:0.058 data_s:0.059
+Training:  23%|███████████▉                                        | 6919/30000 [14:00<42:18,  9.09step/s]INFO 2026-05-19 01:25:52 ot_train.py:488 step:7K smpl:221K ep:371 epch:2.76 loss:0.011 grdn:0.222 lr:8.8e-06 updt_s:0.059 data_s:0.060
+Training:  23%|████████████                                        | 6939/30000 [14:02<42:46,  8.99step/s]INFO 2026-05-19 01:25:55 ot_train.py:488 step:7K smpl:222K ep:372 epch:2.77 loss:0.009 grdn:0.207 lr:8.8e-06 updt_s:0.059 data_s:0.059
+Training:  23%|████████████                                        | 6959/30000 [14:04<42:01,  9.14step/s]INFO 2026-05-19 01:25:57 ot_train.py:488 step:7K smpl:223K ep:373 epch:2.78 loss:0.011 grdn:0.200 lr:8.8e-06 updt_s:0.060 data_s:0.058
+Training:  23%|████████████                                        | 6979/30000 [14:07<41:52,  9.16step/s]INFO 2026-05-19 01:26:00 ot_train.py:488 step:7K smpl:223K ep:374 epch:2.79 loss:0.010 grdn:0.211 lr:8.8e-06 updt_s:0.058 data_s:0.059
+Training:  23%|████████████▏                                       | 6999/30000 [14:09<42:30,  9.02step/s]INFO 2026-05-19 01:26:02 ot_train.py:488 step:7K smpl:224K ep:375 epch:2.80 loss:0.010 grdn:0.197 lr:8.7e-06 updt_s:0.059 data_s:0.061
+Training:  23%|████████████▏                                       | 7019/30000 [14:12<42:53,  8.93step/s]INFO 2026-05-19 01:26:04 ot_train.py:488 step:7K smpl:225K ep:376 epch:2.80 loss:0.010 grdn:0.205 lr:8.7e-06 updt_s:0.059 data_s:0.062
+Training:  23%|████████████▏                                       | 7039/30000 [14:14<42:05,  9.09step/s]INFO 2026-05-19 01:26:07 ot_train.py:488 step:7K smpl:225K ep:377 epch:2.81 loss:0.011 grdn:0.220 lr:8.7e-06 updt_s:0.060 data_s:0.055
+Training:  24%|████████████▏                                       | 7059/30000 [14:16<42:05,  9.08step/s]INFO 2026-05-19 01:26:09 ot_train.py:488 step:7K smpl:226K ep:378 epch:2.82 loss:0.009 grdn:0.199 lr:8.7e-06 updt_s:0.059 data_s:0.060
+Training:  24%|████████████▎                                       | 7079/30000 [14:19<42:27,  9.00step/s]INFO 2026-05-19 01:26:12 ot_train.py:488 step:7K smpl:227K ep:379 epch:2.83 loss:0.010 grdn:0.225 lr:8.7e-06 updt_s:0.061 data_s:0.059
+Training:  24%|████████████▎                                       | 7099/30000 [14:21<43:38,  8.74step/s]INFO 2026-05-19 01:26:14 ot_train.py:488 step:7K smpl:227K ep:380 epch:2.84 loss:0.012 grdn:0.232 lr:8.7e-06 updt_s:0.062 data_s:0.061
+Training:  24%|████████████▎                                       | 7119/30000 [14:24<44:02,  8.66step/s]INFO 2026-05-19 01:26:17 ot_train.py:488 step:7K smpl:228K ep:381 epch:2.84 loss:0.012 grdn:0.247 lr:8.7e-06 updt_s:0.061 data_s:0.061
+Training:  24%|████████████▎                                       | 7139/30000 [14:26<43:26,  8.77step/s]INFO 2026-05-19 01:26:19 ot_train.py:488 step:7K smpl:228K ep:382 epch:2.85 loss:0.011 grdn:0.247 lr:8.7e-06 updt_s:0.060 data_s:0.062
+Training:  24%|████████████▍                                       | 7159/30000 [14:29<43:02,  8.84step/s]INFO 2026-05-19 01:26:21 ot_train.py:488 step:7K smpl:229K ep:383 epch:2.86 loss:0.010 grdn:0.220 lr:8.7e-06 updt_s:0.061 data_s:0.059
+Training:  24%|████████████▍                                       | 7179/30000 [14:31<42:10,  9.02step/s]INFO 2026-05-19 01:26:24 ot_train.py:488 step:7K smpl:230K ep:384 epch:2.87 loss:0.010 grdn:0.214 lr:8.7e-06 updt_s:0.060 data_s:0.062
+Training:  24%|████████████▍                                       | 7199/30000 [14:34<42:35,  8.92step/s]INFO 2026-05-19 01:26:26 ot_train.py:488 step:7K smpl:230K ep:386 epch:2.88 loss:0.010 grdn:0.207 lr:8.7e-06 updt_s:0.060 data_s:0.061
+Training:  24%|████████████▌                                       | 7219/30000 [14:36<43:35,  8.71step/s]INFO 2026-05-19 01:26:29 ot_train.py:488 step:7K smpl:231K ep:387 epch:2.88 loss:0.010 grdn:0.221 lr:8.7e-06 updt_s:0.061 data_s:0.063
+Training:  24%|████████████▌                                       | 7239/30000 [14:39<43:12,  8.78step/s]INFO 2026-05-19 01:26:31 ot_train.py:488 step:7K smpl:232K ep:388 epch:2.89 loss:0.011 grdn:0.219 lr:8.7e-06 updt_s:0.060 data_s:0.063
+Training:  24%|████████████▌                                       | 7259/30000 [14:41<42:54,  8.83step/s]INFO 2026-05-19 01:26:34 ot_train.py:488 step:7K smpl:232K ep:389 epch:2.90 loss:0.010 grdn:0.217 lr:8.7e-06 updt_s:0.060 data_s:0.060
+Training:  24%|████████████▌                                       | 7279/30000 [14:43<42:34,  8.90step/s]INFO 2026-05-19 01:26:36 ot_train.py:488 step:7K smpl:233K ep:390 epch:2.91 loss:0.010 grdn:0.210 lr:8.6e-06 updt_s:0.061 data_s:0.061
+Training:  24%|████████████▋                                       | 7299/30000 [14:46<42:33,  8.89step/s]INFO 2026-05-19 01:26:39 ot_train.py:488 step:7K smpl:234K ep:391 epch:2.92 loss:0.011 grdn:0.209 lr:8.6e-06 updt_s:0.060 data_s:0.060
+Training:  24%|████████████▋                                       | 7319/30000 [14:48<43:58,  8.60step/s]INFO 2026-05-19 01:26:41 ot_train.py:488 step:7K smpl:234K ep:392 epch:2.92 loss:0.009 grdn:0.199 lr:8.6e-06 updt_s:0.060 data_s:0.063
+Training:  24%|████████████▋                                       | 7339/30000 [14:51<40:36,  9.30step/s]INFO 2026-05-19 01:26:43 ot_train.py:488 step:7K smpl:235K ep:393 epch:2.93 loss:0.010 grdn:0.210 lr:8.6e-06 updt_s:0.059 data_s:0.060
+Training:  25%|████████████▊                                       | 7359/30000 [14:53<42:11,  8.94step/s]INFO 2026-05-19 01:26:46 ot_train.py:488 step:7K smpl:236K ep:394 epch:2.94 loss:0.011 grdn:0.206 lr:8.6e-06 updt_s:0.059 data_s:0.062
+Training:  25%|████████████▊                                       | 7379/30000 [14:56<42:50,  8.80step/s]INFO 2026-05-19 01:26:48 ot_train.py:488 step:7K smpl:236K ep:395 epch:2.95 loss:0.011 grdn:0.207 lr:8.6e-06 updt_s:0.060 data_s:0.062
+Training:  25%|████████████▊                                       | 7399/30000 [14:58<42:44,  8.81step/s]INFO 2026-05-19 01:26:51 ot_train.py:488 step:7K smpl:237K ep:396 epch:2.96 loss:0.010 grdn:0.203 lr:8.6e-06 updt_s:0.061 data_s:0.060
+Training:  25%|████████████▊                                       | 7419/30000 [15:00<38:17,  9.83step/s]INFO 2026-05-19 01:26:53 ot_train.py:488 step:7K smpl:237K ep:397 epch:2.96 loss:0.010 grdn:0.187 lr:8.6e-06 updt_s:0.058 data_s:0.055
+Training:  25%|████████████▉                                       | 7439/30000 [15:03<49:56,  7.53step/s]INFO 2026-05-19 01:26:56 ot_train.py:488 step:7K smpl:238K ep:398 epch:2.97 loss:0.009 grdn:0.186 lr:8.6e-06 updt_s:0.059 data_s:0.081
+Training:  25%|████████████▉                                       | 7459/30000 [15:06<49:32,  7.58step/s]INFO 2026-05-19 01:26:58 ot_train.py:488 step:7K smpl:239K ep:399 epch:2.98 loss:0.010 grdn:0.193 lr:8.6e-06 updt_s:0.059 data_s:0.062
+Training:  25%|████████████▉                                       | 7479/30000 [15:08<49:52,  7.52step/s]INFO 2026-05-19 01:27:01 ot_train.py:488 step:7K smpl:239K ep:400 epch:2.99 loss:0.011 grdn:0.205 lr:8.6e-06 updt_s:0.059 data_s:0.062
+Training:  25%|████████████▉                                       | 7499/30000 [15:10<50:03,  7.49step/s]INFO 2026-05-19 01:27:03 ot_train.py:488 step:8K smpl:240K ep:402 epch:3.00 loss:0.012 grdn:0.218 lr:8.6e-06 updt_s:0.060 data_s:0.062
+Training:  25%|█████████████                                       | 7519/30000 [15:13<47:53,  7.82step/s]INFO 2026-05-19 01:27:06 ot_train.py:488 step:8K smpl:241K ep:403 epch:3.00 loss:0.012 grdn:0.230 lr:8.6e-06 updt_s:0.058 data_s:0.062
+Training:  25%|█████████████                                       | 7539/30000 [15:15<47:33,  7.87step/s]INFO 2026-05-19 01:27:08 ot_train.py:488 step:8K smpl:241K ep:404 epch:3.01 loss:0.009 grdn:0.201 lr:8.6e-06 updt_s:0.059 data_s:0.058
+Training:  25%|█████████████                                       | 7559/30000 [15:18<48:58,  7.64step/s]INFO 2026-05-19 01:27:10 ot_train.py:488 step:8K smpl:242K ep:405 epch:3.02 loss:0.011 grdn:0.217 lr:8.5e-06 updt_s:0.060 data_s:0.061
+Training:  25%|█████████████▏                                      | 7579/30000 [15:20<49:20,  7.57step/s]INFO 2026-05-19 01:27:13 ot_train.py:488 step:8K smpl:243K ep:406 epch:3.03 loss:0.011 grdn:0.213 lr:8.5e-06 updt_s:0.059 data_s:0.064
+Training:  25%|█████████████▏                                      | 7599/30000 [15:23<49:29,  7.54step/s]INFO 2026-05-19 01:27:15 ot_train.py:488 step:8K smpl:243K ep:407 epch:3.04 loss:0.011 grdn:0.221 lr:8.5e-06 updt_s:0.059 data_s:0.062
+Training:  25%|█████████████▏                                      | 7619/30000 [15:25<49:51,  7.48step/s]INFO 2026-05-19 01:27:18 ot_train.py:488 step:8K smpl:244K ep:408 epch:3.04 loss:0.010 grdn:0.192 lr:8.5e-06 updt_s:0.060 data_s:0.063
+Training:  25%|█████████████▏                                      | 7639/30000 [15:28<50:41,  7.35step/s]INFO 2026-05-19 01:27:20 ot_train.py:488 step:8K smpl:244K ep:409 epch:3.05 loss:0.010 grdn:0.194 lr:8.5e-06 updt_s:0.060 data_s:0.064
+Training:  26%|█████████████▎                                      | 7659/30000 [15:30<48:19,  7.71step/s]INFO 2026-05-19 01:27:23 ot_train.py:488 step:8K smpl:245K ep:410 epch:3.06 loss:0.010 grdn:0.215 lr:8.5e-06 updt_s:0.060 data_s:0.060
+Training:  26%|█████████████▎                                      | 7679/30000 [15:32<50:31,  7.36step/s]INFO 2026-05-19 01:27:25 ot_train.py:488 step:8K smpl:246K ep:411 epch:3.07 loss:0.010 grdn:0.237 lr:8.5e-06 updt_s:0.062 data_s:0.060
+Training:  26%|█████████████▎                                      | 7699/30000 [15:35<49:17,  7.54step/s]INFO 2026-05-19 01:27:28 ot_train.py:488 step:8K smpl:246K ep:412 epch:3.08 loss:0.012 grdn:0.237 lr:8.5e-06 updt_s:0.060 data_s:0.061
+Training:  26%|█████████████▍                                      | 7719/30000 [15:37<52:18,  7.10step/s]INFO 2026-05-19 01:27:30 ot_train.py:488 step:8K smpl:247K ep:413 epch:3.08 loss:0.010 grdn:0.209 lr:8.5e-06 updt_s:0.060 data_s:0.064
+Training:  26%|█████████████▍                                      | 7739/30000 [15:40<49:11,  7.54step/s]INFO 2026-05-19 01:27:33 ot_train.py:488 step:8K smpl:248K ep:414 epch:3.09 loss:0.009 grdn:0.198 lr:8.5e-06 updt_s:0.061 data_s:0.060
+Training:  26%|█████████████▍                                      | 7759/30000 [15:42<47:41,  7.77step/s]INFO 2026-05-19 01:27:35 ot_train.py:488 step:8K smpl:248K ep:415 epch:3.10 loss:0.010 grdn:0.224 lr:8.5e-06 updt_s:0.059 data_s:0.061
+Training:  26%|█████████████▍                                      | 7779/30000 [15:45<48:43,  7.60step/s]INFO 2026-05-19 01:27:37 ot_train.py:488 step:8K smpl:249K ep:417 epch:3.11 loss:0.011 grdn:0.216 lr:8.5e-06 updt_s:0.059 data_s:0.062
+Training:  26%|█████████████▌                                      | 7799/30000 [15:47<47:08,  7.85step/s]INFO 2026-05-19 01:27:40 ot_train.py:488 step:8K smpl:250K ep:418 epch:3.12 loss:0.009 grdn:0.214 lr:8.5e-06 updt_s:0.059 data_s:0.060
+Training:  26%|█████████████▌                                      | 7819/30000 [15:49<47:51,  7.73step/s]INFO 2026-05-19 01:27:42 ot_train.py:488 step:8K smpl:250K ep:419 epch:3.12 loss:0.010 grdn:0.208 lr:8.4e-06 updt_s:0.061 data_s:0.059
+Training:  26%|█████████████▌                                      | 7839/30000 [15:52<47:09,  7.83step/s]INFO 2026-05-19 01:27:45 ot_train.py:488 step:8K smpl:251K ep:420 epch:3.13 loss:0.009 grdn:0.201 lr:8.4e-06 updt_s:0.060 data_s:0.060
+Training:  26%|█████████████▌                                      | 7859/30000 [15:54<47:43,  7.73step/s]INFO 2026-05-19 01:27:47 ot_train.py:488 step:8K smpl:252K ep:421 epch:3.14 loss:0.011 grdn:0.234 lr:8.4e-06 updt_s:0.060 data_s:0.058
+Training:  26%|█████████████▋                                      | 7879/30000 [15:57<49:36,  7.43step/s]INFO 2026-05-19 01:27:49 ot_train.py:488 step:8K smpl:252K ep:422 epch:3.15 loss:0.008 grdn:0.204 lr:8.4e-06 updt_s:0.060 data_s:0.060
+Training:  26%|█████████████▋                                      | 7899/30000 [15:59<48:38,  7.57step/s]INFO 2026-05-19 01:27:52 ot_train.py:488 step:8K smpl:253K ep:423 epch:3.16 loss:0.011 grdn:0.205 lr:8.4e-06 updt_s:0.061 data_s:0.061
+Training:  26%|█████████████▋                                      | 7919/30000 [16:02<47:04,  7.82step/s]INFO 2026-05-19 01:27:54 ot_train.py:488 step:8K smpl:253K ep:424 epch:3.16 loss:0.010 grdn:0.225 lr:8.4e-06 updt_s:0.060 data_s:0.062
+Training:  26%|█████████████▊                                      | 7939/30000 [16:04<45:59,  7.99step/s]INFO 2026-05-19 01:27:57 ot_train.py:488 step:8K smpl:254K ep:425 epch:3.17 loss:0.010 grdn:0.224 lr:8.4e-06 updt_s:0.060 data_s:0.058
+Training:  27%|█████████████▊                                      | 7959/30000 [16:06<45:35,  8.06step/s]INFO 2026-05-19 01:27:59 ot_train.py:488 step:8K smpl:255K ep:426 epch:3.18 loss:0.010 grdn:0.245 lr:8.4e-06 updt_s:0.060 data_s:0.056
+Training:  27%|█████████████▊                                      | 7979/30000 [16:09<49:32,  7.41step/s]INFO 2026-05-19 01:28:02 ot_train.py:488 step:8K smpl:255K ep:427 epch:3.19 loss:0.009 grdn:0.203 lr:8.4e-06 updt_s:0.060 data_s:0.065
+Training:  27%|█████████████▊                                      | 7999/30000 [16:11<50:20,  7.28step/s]INFO 2026-05-19 01:28:04 ot_train.py:488 step:8K smpl:256K ep:428 epch:3.20 loss:0.009 grdn:0.199 lr:8.4e-06 updt_s:0.059 data_s:0.071
+Training:  27%|█████████████▉                                      | 8019/30000 [16:14<47:42,  7.68step/s]INFO 2026-05-19 01:28:07 ot_train.py:488 step:8K smpl:257K ep:429 epch:3.20 loss:0.011 grdn:0.216 lr:8.4e-06 updt_s:0.059 data_s:0.061
+Training:  27%|█████████████▉                                      | 8039/30000 [16:16<48:09,  7.60step/s]INFO 2026-05-19 01:28:09 ot_train.py:488 step:8K smpl:257K ep:430 epch:3.21 loss:0.009 grdn:0.192 lr:8.4e-06 updt_s:0.059 data_s:0.061
+Training:  27%|█████████████▉                                      | 8059/30000 [16:19<49:03,  7.45step/s]INFO 2026-05-19 01:28:11 ot_train.py:488 step:8K smpl:258K ep:432 epch:3.22 loss:0.010 grdn:0.200 lr:8.4e-06 updt_s:0.060 data_s:0.063
+Training:  27%|██████████████                                      | 8079/30000 [16:21<48:43,  7.50step/s]INFO 2026-05-19 01:28:14 ot_train.py:488 step:8K smpl:259K ep:433 epch:3.23 loss:0.010 grdn:0.227 lr:8.3e-06 updt_s:0.060 data_s:0.062
+Training:  27%|██████████████                                      | 8099/30000 [16:24<46:45,  7.81step/s]INFO 2026-05-19 01:28:16 ot_train.py:488 step:8K smpl:259K ep:434 epch:3.24 loss:0.010 grdn:0.242 lr:8.3e-06 updt_s:0.059 data_s:0.066
+Training:  27%|██████████████                                      | 8119/30000 [16:26<46:13,  7.89step/s]INFO 2026-05-19 01:28:19 ot_train.py:488 step:8K smpl:260K ep:435 epch:3.24 loss:0.011 grdn:0.224 lr:8.3e-06 updt_s:0.059 data_s:0.059
+Training:  27%|██████████████                                      | 8139/30000 [16:29<47:38,  7.65step/s]INFO 2026-05-19 01:28:21 ot_train.py:488 step:8K smpl:260K ep:436 epch:3.25 loss:0.010 grdn:0.222 lr:8.3e-06 updt_s:0.058 data_s:0.062
+Training:  27%|██████████████▏                                     | 8159/30000 [16:31<45:55,  7.93step/s]INFO 2026-05-19 01:28:24 ot_train.py:488 step:8K smpl:261K ep:437 epch:3.26 loss:0.011 grdn:0.222 lr:8.3e-06 updt_s:0.058 data_s:0.060
+Training:  27%|██████████████▏                                     | 8179/30000 [16:33<55:30,  6.55step/s]INFO 2026-05-19 01:28:26 ot_train.py:488 step:8K smpl:262K ep:438 epch:3.27 loss:0.008 grdn:0.195 lr:8.3e-06 updt_s:0.058 data_s:0.066
+Training:  27%|██████████████▏                                     | 8199/30000 [16:36<45:46,  7.94step/s]INFO 2026-05-19 01:28:28 ot_train.py:488 step:8K smpl:262K ep:439 epch:3.28 loss:0.011 grdn:0.230 lr:8.3e-06 updt_s:0.059 data_s:0.055
+Training:  27%|██████████████▏                                     | 8219/30000 [16:38<45:27,  7.99step/s]INFO 2026-05-19 01:28:31 ot_train.py:488 step:8K smpl:263K ep:440 epch:3.28 loss:0.010 grdn:0.221 lr:8.3e-06 updt_s:0.059 data_s:0.057
+Training:  27%|██████████████▎                                     | 8239/30000 [16:40<46:38,  7.78step/s]INFO 2026-05-19 01:28:33 ot_train.py:488 step:8K smpl:264K ep:441 epch:3.29 loss:0.010 grdn:0.211 lr:8.3e-06 updt_s:0.059 data_s:0.058
+Training:  28%|██████████████▎                                     | 8259/30000 [16:43<47:04,  7.70step/s]INFO 2026-05-19 01:28:35 ot_train.py:488 step:8K smpl:264K ep:442 epch:3.30 loss:0.010 grdn:0.209 lr:8.3e-06 updt_s:0.059 data_s:0.060
+Training:  28%|██████████████▎                                     | 8279/30000 [16:45<49:30,  7.31step/s]INFO 2026-05-19 01:28:38 ot_train.py:488 step:8K smpl:265K ep:443 epch:3.31 loss:0.010 grdn:0.221 lr:8.3e-06 updt_s:0.058 data_s:0.066
+Training:  28%|██████████████▍                                     | 8299/30000 [16:48<46:17,  7.81step/s]INFO 2026-05-19 01:28:40 ot_train.py:488 step:8K smpl:266K ep:444 epch:3.32 loss:0.010 grdn:0.205 lr:8.3e-06 updt_s:0.058 data_s:0.060
+Training:  28%|██████████████▍                                     | 8319/30000 [16:50<44:56,  8.04step/s]INFO 2026-05-19 01:28:43 ot_train.py:488 step:8K smpl:266K ep:445 epch:3.32 loss:0.010 grdn:0.205 lr:8.3e-06 updt_s:0.060 data_s:0.057
+Training:  28%|██████████████▍                                     | 8339/30000 [16:52<46:08,  7.82step/s]INFO 2026-05-19 01:28:45 ot_train.py:488 step:8K smpl:267K ep:447 epch:3.33 loss:0.008 grdn:0.191 lr:8.2e-06 updt_s:0.060 data_s:0.057
+Training:  28%|██████████████▍                                     | 8359/30000 [16:55<45:35,  7.91step/s]INFO 2026-05-19 01:28:47 ot_train.py:488 step:8K smpl:268K ep:448 epch:3.34 loss:0.008 grdn:0.196 lr:8.2e-06 updt_s:0.060 data_s:0.058
+Training:  28%|██████████████▌                                     | 8379/30000 [16:57<47:03,  7.66step/s]INFO 2026-05-19 01:28:50 ot_train.py:488 step:8K smpl:268K ep:449 epch:3.35 loss:0.009 grdn:0.202 lr:8.2e-06 updt_s:0.060 data_s:0.061
+Training:  28%|██████████████▌                                     | 8399/30000 [16:59<45:34,  7.90step/s]INFO 2026-05-19 01:28:52 ot_train.py:488 step:8K smpl:269K ep:450 epch:3.36 loss:0.011 grdn:0.208 lr:8.2e-06 updt_s:0.061 data_s:0.058
+Training:  28%|██████████████▌                                     | 8419/30000 [17:02<43:54,  8.19step/s]INFO 2026-05-19 01:28:54 ot_train.py:488 step:8K smpl:269K ep:451 epch:3.36 loss:0.010 grdn:0.209 lr:8.2e-06 updt_s:0.060 data_s:0.054
+Training:  28%|██████████████▋                                     | 8439/30000 [17:04<45:20,  7.93step/s]INFO 2026-05-19 01:28:57 ot_train.py:488 step:8K smpl:270K ep:452 epch:3.37 loss:0.011 grdn:0.210 lr:8.2e-06 updt_s:0.059 data_s:0.059
+Training:  28%|██████████████▋                                     | 8459/30000 [17:06<45:34,  7.88step/s]INFO 2026-05-19 01:28:59 ot_train.py:488 step:8K smpl:271K ep:453 epch:3.38 loss:0.011 grdn:0.225 lr:8.2e-06 updt_s:0.059 data_s:0.058
+Training:  28%|██████████████▋                                     | 8479/30000 [17:09<46:11,  7.76step/s]INFO 2026-05-19 01:29:02 ot_train.py:488 step:8K smpl:271K ep:454 epch:3.39 loss:0.010 grdn:0.204 lr:8.2e-06 updt_s:0.059 data_s:0.066
+Training:  28%|██████████████▋                                     | 8499/30000 [17:11<46:43,  7.67step/s]INFO 2026-05-19 01:29:04 ot_train.py:488 step:8K smpl:272K ep:455 epch:3.40 loss:0.011 grdn:0.214 lr:8.2e-06 updt_s:0.059 data_s:0.061
+Training:  28%|██████████████▊                                     | 8519/30000 [17:14<46:38,  7.67step/s]INFO 2026-05-19 01:29:06 ot_train.py:488 step:9K smpl:273K ep:456 epch:3.40 loss:0.010 grdn:0.227 lr:8.2e-06 updt_s:0.058 data_s:0.059
+Training:  28%|██████████████▊                                     | 8539/30000 [17:16<45:44,  7.82step/s]INFO 2026-05-19 01:29:09 ot_train.py:488 step:9K smpl:273K ep:457 epch:3.41 loss:0.008 grdn:0.176 lr:8.2e-06 updt_s:0.058 data_s:0.060
+Training:  29%|██████████████▊                                     | 8559/30000 [17:19<46:37,  7.67step/s]INFO 2026-05-19 01:29:11 ot_train.py:488 step:9K smpl:274K ep:458 epch:3.42 loss:0.010 grdn:0.230 lr:8.2e-06 updt_s:0.059 data_s:0.061
+Training:  29%|██████████████▊                                     | 8579/30000 [17:21<48:20,  7.39step/s]INFO 2026-05-19 01:29:14 ot_train.py:488 step:9K smpl:275K ep:459 epch:3.43 loss:0.009 grdn:0.234 lr:8.1e-06 updt_s:0.060 data_s:0.064
+Training:  29%|██████████████▉                                     | 8599/30000 [17:24<46:58,  7.59step/s]INFO 2026-05-19 01:29:16 ot_train.py:488 step:9K smpl:275K ep:460 epch:3.44 loss:0.010 grdn:0.236 lr:8.1e-06 updt_s:0.060 data_s:0.062
+Training:  29%|██████████████▉                                     | 8619/30000 [17:26<45:09,  7.89step/s]INFO 2026-05-19 01:29:19 ot_train.py:488 step:9K smpl:276K ep:462 epch:3.44 loss:0.012 grdn:0.242 lr:8.1e-06 updt_s:0.059 data_s:0.060
+Training:  29%|██████████████▉                                     | 8639/30000 [17:28<45:15,  7.87step/s]INFO 2026-05-19 01:29:21 ot_train.py:488 step:9K smpl:276K ep:463 epch:3.45 loss:0.011 grdn:0.223 lr:8.1e-06 updt_s:0.060 data_s:0.058
+Training:  29%|███████████████                                     | 8659/30000 [17:31<43:21,  8.20step/s]INFO 2026-05-19 01:29:23 ot_train.py:488 step:9K smpl:277K ep:464 epch:3.46 loss:0.010 grdn:0.213 lr:8.1e-06 updt_s:0.059 data_s:0.058
+Training:  29%|███████████████                                     | 8679/30000 [17:33<46:27,  7.65step/s]INFO 2026-05-19 01:29:26 ot_train.py:488 step:9K smpl:278K ep:465 epch:3.47 loss:0.013 grdn:0.251 lr:8.1e-06 updt_s:0.059 data_s:0.062
+Training:  29%|███████████████                                     | 8699/30000 [17:36<45:51,  7.74step/s]INFO 2026-05-19 01:29:28 ot_train.py:488 step:9K smpl:278K ep:466 epch:3.48 loss:0.009 grdn:0.225 lr:8.1e-06 updt_s:0.060 data_s:0.059
+Training:  29%|███████████████                                     | 8719/30000 [17:38<44:51,  7.91step/s]INFO 2026-05-19 01:29:31 ot_train.py:488 step:9K smpl:279K ep:467 epch:3.48 loss:0.011 grdn:0.208 lr:8.1e-06 updt_s:0.058 data_s:0.061
+Training:  29%|███████████████▏                                    | 8739/30000 [17:40<46:50,  7.56step/s]INFO 2026-05-19 01:29:33 ot_train.py:488 step:9K smpl:280K ep:468 epch:3.49 loss:0.010 grdn:0.218 lr:8.1e-06 updt_s:0.058 data_s:0.064
+Training:  29%|███████████████▏                                    | 8759/30000 [17:43<45:55,  7.71step/s]INFO 2026-05-19 01:29:36 ot_train.py:488 step:9K smpl:280K ep:469 epch:3.50 loss:0.012 grdn:0.215 lr:8.1e-06 updt_s:0.059 data_s:0.061
+Training:  29%|███████████████▏                                    | 8779/30000 [17:45<45:39,  7.75step/s]INFO 2026-05-19 01:29:38 ot_train.py:488 step:9K smpl:281K ep:470 epch:3.51 loss:0.011 grdn:0.233 lr:8.1e-06 updt_s:0.060 data_s:0.060
+Training:  29%|███████████████▎                                    | 8799/30000 [17:48<47:09,  7.49step/s]INFO 2026-05-19 01:29:40 ot_train.py:488 step:9K smpl:282K ep:471 epch:3.52 loss:0.009 grdn:0.197 lr:8.1e-06 updt_s:0.060 data_s:0.060
+Training:  29%|███████████████▎                                    | 8819/30000 [17:50<45:10,  7.82step/s]INFO 2026-05-19 01:29:43 ot_train.py:488 step:9K smpl:282K ep:472 epch:3.52 loss:0.010 grdn:0.221 lr:8.0e-06 updt_s:0.060 data_s:0.061
+Training:  29%|███████████████▎                                    | 8839/30000 [17:53<45:37,  7.73step/s]INFO 2026-05-19 01:29:45 ot_train.py:488 step:9K smpl:283K ep:473 epch:3.53 loss:0.010 grdn:0.196 lr:8.0e-06 updt_s:0.060 data_s:0.060
+Training:  30%|███████████████▎                                    | 8859/30000 [17:55<46:03,  7.65step/s]INFO 2026-05-19 01:29:48 ot_train.py:488 step:9K smpl:284K ep:474 epch:3.54 loss:0.009 grdn:0.215 lr:8.0e-06 updt_s:0.060 data_s:0.063
+Training:  30%|███████████████▍                                    | 8879/30000 [17:57<46:09,  7.63step/s]INFO 2026-05-19 01:29:50 ot_train.py:488 step:9K smpl:284K ep:475 epch:3.55 loss:0.010 grdn:0.214 lr:8.0e-06 updt_s:0.059 data_s:0.061
+Training:  30%|███████████████▍                                    | 8899/30000 [18:00<46:39,  7.54step/s]INFO 2026-05-19 01:29:53 ot_train.py:488 step:9K smpl:285K ep:477 epch:3.56 loss:0.010 grdn:0.216 lr:8.0e-06 updt_s:0.059 data_s:0.062
+Training:  30%|███████████████▍                                    | 8919/30000 [18:02<45:46,  7.68step/s]INFO 2026-05-19 01:29:55 ot_train.py:488 step:9K smpl:285K ep:478 epch:3.56 loss:0.009 grdn:0.212 lr:8.0e-06 updt_s:0.059 data_s:0.059
+Training:  30%|███████████████▍                                    | 8939/30000 [18:05<46:20,  7.57step/s]INFO 2026-05-19 01:29:57 ot_train.py:488 step:9K smpl:286K ep:479 epch:3.57 loss:0.009 grdn:0.202 lr:8.0e-06 updt_s:0.059 data_s:0.061
+Training:  30%|███████████████▌                                    | 8959/30000 [18:07<45:59,  7.62step/s]INFO 2026-05-19 01:30:00 ot_train.py:488 step:9K smpl:287K ep:480 epch:3.58 loss:0.010 grdn:0.208 lr:8.0e-06 updt_s:0.059 data_s:0.063
+Training:  30%|███████████████▌                                    | 8979/30000 [18:10<44:53,  7.80step/s]INFO 2026-05-19 01:30:02 ot_train.py:488 step:9K smpl:287K ep:481 epch:3.59 loss:0.009 grdn:0.204 lr:8.0e-06 updt_s:0.059 data_s:0.061
+Training:  30%|███████████████▌                                    | 8999/30000 [18:12<47:28,  7.37step/s]INFO 2026-05-19 01:30:05 ot_train.py:488 step:9K smpl:288K ep:482 epch:3.60 loss:0.009 grdn:0.220 lr:8.0e-06 updt_s:0.060 data_s:0.064
+Training:  30%|███████████████▋                                    | 9019/30000 [18:14<45:50,  7.63step/s]INFO 2026-05-19 01:30:07 ot_train.py:488 step:9K smpl:289K ep:483 epch:3.60 loss:0.010 grdn:0.210 lr:8.0e-06 updt_s:0.059 data_s:0.060
+Training:  30%|███████████████▋                                    | 9039/30000 [18:17<45:41,  7.64step/s]INFO 2026-05-19 01:30:10 ot_train.py:488 step:9K smpl:289K ep:484 epch:3.61 loss:0.010 grdn:0.237 lr:8.0e-06 updt_s:0.059 data_s:0.059
+Training:  30%|███████████████▋                                    | 9059/30000 [18:19<44:50,  7.78step/s]INFO 2026-05-19 01:30:12 ot_train.py:488 step:9K smpl:290K ep:485 epch:3.62 loss:0.010 grdn:0.216 lr:7.9e-06 updt_s:0.060 data_s:0.058
+Training:  30%|███████████████▋                                    | 9079/30000 [18:22<45:22,  7.68step/s]INFO 2026-05-19 01:30:14 ot_train.py:488 step:9K smpl:291K ep:486 epch:3.63 loss:0.009 grdn:0.204 lr:7.9e-06 updt_s:0.059 data_s:0.060
+Training:  30%|███████████████▊                                    | 9099/30000 [18:24<45:19,  7.69step/s]INFO 2026-05-19 01:30:17 ot_train.py:488 step:9K smpl:291K ep:487 epch:3.64 loss:0.010 grdn:0.197 lr:7.9e-06 updt_s:0.061 data_s:0.059
+Training:  30%|███████████████▊                                    | 9119/30000 [18:26<45:39,  7.62step/s]INFO 2026-05-19 01:30:19 ot_train.py:488 step:9K smpl:292K ep:488 epch:3.64 loss:0.011 grdn:0.220 lr:7.9e-06 updt_s:0.060 data_s:0.060
+Training:  30%|███████████████▊                                    | 9139/30000 [18:29<44:49,  7.76step/s]INFO 2026-05-19 01:30:22 ot_train.py:488 step:9K smpl:292K ep:489 epch:3.65 loss:0.009 grdn:0.213 lr:7.9e-06 updt_s:0.061 data_s:0.058
+Training:  31%|███████████████▉                                    | 9159/30000 [18:31<44:50,  7.75step/s]INFO 2026-05-19 01:30:24 ot_train.py:488 step:9K smpl:293K ep:490 epch:3.66 loss:0.009 grdn:0.210 lr:7.9e-06 updt_s:0.061 data_s:0.059
+Training:  31%|███████████████▉                                    | 9179/30000 [18:34<45:06,  7.69step/s]INFO 2026-05-19 01:30:26 ot_train.py:488 step:9K smpl:294K ep:492 epch:3.67 loss:0.009 grdn:0.207 lr:7.9e-06 updt_s:0.060 data_s:0.059
+Training:  31%|███████████████▉                                    | 9199/30000 [18:36<45:15,  7.66step/s]INFO 2026-05-19 01:30:29 ot_train.py:488 step:9K smpl:294K ep:493 epch:3.68 loss:0.010 grdn:0.211 lr:7.9e-06 updt_s:0.061 data_s:0.058
+Training:  31%|███████████████▉                                    | 9219/30000 [18:38<46:03,  7.52step/s]INFO 2026-05-19 01:30:31 ot_train.py:488 step:9K smpl:295K ep:494 epch:3.68 loss:0.008 grdn:0.189 lr:7.9e-06 updt_s:0.059 data_s:0.064
+Training:  31%|████████████████                                    | 9239/30000 [18:41<44:10,  7.83step/s]INFO 2026-05-19 01:30:34 ot_train.py:488 step:9K smpl:296K ep:495 epch:3.69 loss:0.009 grdn:0.208 lr:7.9e-06 updt_s:0.059 data_s:0.060
+Training:  31%|████████████████                                    | 9259/30000 [18:43<43:53,  7.87step/s]INFO 2026-05-19 01:30:36 ot_train.py:488 step:9K smpl:296K ep:496 epch:3.70 loss:0.010 grdn:0.211 lr:7.9e-06 updt_s:0.060 data_s:0.059
+Training:  31%|████████████████                                    | 9279/30000 [18:46<44:49,  7.70step/s]INFO 2026-05-19 01:30:38 ot_train.py:488 step:9K smpl:297K ep:497 epch:3.71 loss:0.012 grdn:0.223 lr:7.9e-06 updt_s:0.060 data_s:0.059
+Training:  31%|████████████████                                    | 9299/30000 [18:48<44:27,  7.76step/s]INFO 2026-05-19 01:30:41 ot_train.py:488 step:9K smpl:298K ep:498 epch:3.72 loss:0.011 grdn:0.238 lr:7.8e-06 updt_s:0.059 data_s:0.060
+Training:  31%|████████████████▏                                   | 9319/30000 [18:50<43:24,  7.94step/s]INFO 2026-05-19 01:30:43 ot_train.py:488 step:9K smpl:298K ep:499 epch:3.72 loss:0.010 grdn:0.226 lr:7.8e-06 updt_s:0.060 data_s:0.057
+Training:  31%|████████████████▏                                   | 9339/30000 [18:53<45:10,  7.62step/s]INFO 2026-05-19 01:30:46 ot_train.py:488 step:9K smpl:299K ep:500 epch:3.73 loss:0.011 grdn:0.225 lr:7.8e-06 updt_s:0.061 data_s:0.060
+Training:  31%|████████████████▏                                   | 9359/30000 [18:55<44:45,  7.69step/s]INFO 2026-05-19 01:30:48 ot_train.py:488 step:9K smpl:300K ep:501 epch:3.74 loss:0.010 grdn:0.216 lr:7.8e-06 updt_s:0.059 data_s:0.062
+Training:  31%|████████████████▎                                   | 9379/30000 [18:58<44:35,  7.71step/s]INFO 2026-05-19 01:30:50 ot_train.py:488 step:9K smpl:300K ep:502 epch:3.75 loss:0.010 grdn:0.207 lr:7.8e-06 updt_s:0.060 data_s:0.059
+Training:  31%|████████████████▎                                   | 9399/30000 [19:00<54:13,  6.33step/s]INFO 2026-05-19 01:30:53 ot_train.py:488 step:9K smpl:301K ep:503 epch:3.76 loss:0.011 grdn:0.225 lr:7.8e-06 updt_s:0.059 data_s:0.068
+Training:  31%|████████████████▎                                   | 9419/30000 [19:03<43:59,  7.80step/s]INFO 2026-05-19 01:30:55 ot_train.py:488 step:9K smpl:301K ep:504 epch:3.76 loss:0.011 grdn:0.207 lr:7.8e-06 updt_s:0.058 data_s:0.058
+Training:  31%|████████████████▎                                   | 9439/30000 [19:05<45:05,  7.60step/s]INFO 2026-05-19 01:30:58 ot_train.py:488 step:9K smpl:302K ep:505 epch:3.77 loss:0.009 grdn:0.212 lr:7.8e-06 updt_s:0.059 data_s:0.060
+Training:  32%|████████████████▍                                   | 9459/30000 [19:07<45:07,  7.59step/s]INFO 2026-05-19 01:31:00 ot_train.py:488 step:9K smpl:303K ep:507 epch:3.78 loss:0.010 grdn:0.208 lr:7.8e-06 updt_s:0.059 data_s:0.062
+Training:  32%|████████████████▍                                   | 9479/30000 [19:10<42:34,  8.03step/s]INFO 2026-05-19 01:31:02 ot_train.py:488 step:9K smpl:303K ep:508 epch:3.79 loss:0.010 grdn:0.207 lr:7.8e-06 updt_s:0.058 data_s:0.056
+Training:  32%|████████████████▍                                   | 9499/30000 [19:12<43:53,  7.79step/s]INFO 2026-05-19 01:31:05 ot_train.py:488 step:10K smpl:304K ep:509 epch:3.80 loss:0.010 grdn:0.205 lr:7.8e-06 updt_s:0.059 data_s:0.058
+Training:  32%|████████████████▍                                   | 9519/30000 [19:14<43:33,  7.84step/s]INFO 2026-05-19 01:31:07 ot_train.py:488 step:10K smpl:305K ep:510 epch:3.80 loss:0.009 grdn:0.204 lr:7.7e-06 updt_s:0.058 data_s:0.060
+Training:  32%|████████████████▌                                   | 9539/30000 [19:17<42:38,  8.00step/s]INFO 2026-05-19 01:31:09 ot_train.py:488 step:10K smpl:305K ep:511 epch:3.81 loss:0.010 grdn:0.213 lr:7.7e-06 updt_s:0.058 data_s:0.057
+Training:  32%|████████████████▌                                   | 9559/30000 [19:19<41:36,  8.19step/s]INFO 2026-05-19 01:31:12 ot_train.py:488 step:10K smpl:306K ep:512 epch:3.82 loss:0.009 grdn:0.201 lr:7.7e-06 updt_s:0.058 data_s:0.058
+Training:  32%|████████████████▌                                   | 9579/30000 [19:21<44:32,  7.64step/s]INFO 2026-05-19 01:31:14 ot_train.py:488 step:10K smpl:307K ep:513 epch:3.83 loss:0.011 grdn:0.233 lr:7.7e-06 updt_s:0.059 data_s:0.061
+Training:  32%|████████████████▋                                   | 9599/30000 [19:24<43:46,  7.77step/s]INFO 2026-05-19 01:31:17 ot_train.py:488 step:10K smpl:307K ep:514 epch:3.84 loss:0.011 grdn:0.212 lr:7.7e-06 updt_s:0.059 data_s:0.059
+Training:  32%|████████████████▋                                   | 9619/30000 [19:26<43:46,  7.76step/s]INFO 2026-05-19 01:31:19 ot_train.py:488 step:10K smpl:308K ep:515 epch:3.84 loss:0.009 grdn:0.195 lr:7.7e-06 updt_s:0.060 data_s:0.059
+Training:  32%|████████████████▋                                   | 9639/30000 [19:29<44:00,  7.71step/s]INFO 2026-05-19 01:31:21 ot_train.py:488 step:10K smpl:308K ep:516 epch:3.85 loss:0.010 grdn:0.217 lr:7.7e-06 updt_s:0.059 data_s:0.060
+Training:  32%|████████████████▋                                   | 9659/30000 [19:31<42:42,  7.94step/s]INFO 2026-05-19 01:31:24 ot_train.py:488 step:10K smpl:309K ep:517 epch:3.86 loss:0.010 grdn:0.210 lr:7.7e-06 updt_s:0.059 data_s:0.059
+Training:  32%|████████████████▊                                   | 9679/30000 [19:33<43:41,  7.75step/s]INFO 2026-05-19 01:31:26 ot_train.py:488 step:10K smpl:310K ep:518 epch:3.87 loss:0.010 grdn:0.196 lr:7.7e-06 updt_s:0.059 data_s:0.060
+Training:  32%|████████████████▊                                   | 9699/30000 [19:36<42:56,  7.88step/s]INFO 2026-05-19 01:31:28 ot_train.py:488 step:10K smpl:310K ep:519 epch:3.88 loss:0.010 grdn:0.207 lr:7.7e-06 updt_s:0.058 data_s:0.058
+Training:  32%|████████████████▊                                   | 9719/30000 [19:38<43:07,  7.84step/s]INFO 2026-05-19 01:31:31 ot_train.py:488 step:10K smpl:311K ep:520 epch:3.88 loss:0.011 grdn:0.213 lr:7.7e-06 updt_s:0.058 data_s:0.060
+Training:  32%|████████████████▉                                   | 9739/30000 [19:41<43:16,  7.80step/s]INFO 2026-05-19 01:31:33 ot_train.py:488 step:10K smpl:312K ep:521 epch:3.89 loss:0.011 grdn:0.211 lr:7.7e-06 updt_s:0.059 data_s:0.061
+Training:  33%|████████████████▉                                   | 9759/30000 [19:43<44:21,  7.61step/s]INFO 2026-05-19 01:31:36 ot_train.py:488 step:10K smpl:312K ep:523 epch:3.90 loss:0.013 grdn:0.245 lr:7.6e-06 updt_s:0.060 data_s:0.059
+Training:  33%|████████████████▉                                   | 9779/30000 [19:45<43:23,  7.77step/s]INFO 2026-05-19 01:31:38 ot_train.py:488 step:10K smpl:313K ep:524 epch:3.91 loss:0.008 grdn:0.208 lr:7.6e-06 updt_s:0.062 data_s:0.058
+Training:  33%|████████████████▉                                   | 9799/30000 [19:48<43:24,  7.76step/s]INFO 2026-05-19 01:31:40 ot_train.py:488 step:10K smpl:314K ep:525 epch:3.92 loss:0.010 grdn:0.227 lr:7.6e-06 updt_s:0.061 data_s:0.058
+Training:  33%|█████████████████                                   | 9819/30000 [19:50<44:03,  7.64step/s]INFO 2026-05-19 01:31:43 ot_train.py:488 step:10K smpl:314K ep:526 epch:3.92 loss:0.010 grdn:0.193 lr:7.6e-06 updt_s:0.062 data_s:0.060
+Training:  33%|█████████████████                                   | 9839/30000 [19:53<43:31,  7.72step/s]INFO 2026-05-19 01:31:45 ot_train.py:488 step:10K smpl:315K ep:527 epch:3.93 loss:0.009 grdn:0.220 lr:7.6e-06 updt_s:0.061 data_s:0.058
+Training:  33%|█████████████████                                   | 9859/30000 [19:55<43:41,  7.68step/s]INFO 2026-05-19 01:31:48 ot_train.py:488 step:10K smpl:316K ep:528 epch:3.94 loss:0.011 grdn:0.219 lr:7.6e-06 updt_s:0.059 data_s:0.060
+Training:  33%|█████████████████                                   | 9879/30000 [19:58<46:32,  7.20step/s]INFO 2026-05-19 01:31:50 ot_train.py:488 step:10K smpl:316K ep:529 epch:3.95 loss:0.011 grdn:0.211 lr:7.6e-06 updt_s:0.060 data_s:0.062
+Training:  33%|█████████████████▏                                  | 9899/30000 [20:00<47:26,  7.06step/s]INFO 2026-05-19 01:31:53 ot_train.py:488 step:10K smpl:317K ep:530 epch:3.96 loss:0.011 grdn:0.224 lr:7.6e-06 updt_s:0.057 data_s:0.070
+Training:  33%|█████████████████▏                                  | 9919/30000 [20:02<36:53,  9.07step/s]INFO 2026-05-19 01:31:55 ot_train.py:488 step:10K smpl:317K ep:531 epch:3.96 loss:0.010 grdn:0.218 lr:7.6e-06 updt_s:0.058 data_s:0.052
+Training:  33%|█████████████████▏                                  | 9939/30000 [20:05<37:16,  8.97step/s]INFO 2026-05-19 01:31:57 ot_train.py:488 step:10K smpl:318K ep:532 epch:3.97 loss:0.012 grdn:0.228 lr:7.6e-06 updt_s:0.059 data_s:0.062
+Training:  33%|█████████████████▎                                  | 9959/30000 [20:07<36:24,  9.17step/s]INFO 2026-05-19 01:32:00 ot_train.py:488 step:10K smpl:319K ep:533 epch:3.98 loss:0.010 grdn:0.196 lr:7.6e-06 updt_s:0.060 data_s:0.058
+Training:  33%|█████████████████▎                                  | 9979/30000 [20:09<36:01,  9.26step/s]INFO 2026-05-19 01:32:02 ot_train.py:488 step:10K smpl:319K ep:534 epch:3.99 loss:0.008 grdn:0.188 lr:7.5e-06 updt_s:0.059 data_s:0.057
+Training:  33%|█████████████████▎                                  | 9999/30000 [20:12<38:00,  8.77step/s]INFO 2026-05-19 01:32:05 ot_train.py:488 step:10K smpl:320K ep:535 epch:4.00 loss:0.009 grdn:0.198 lr:7.5e-06 updt_s:0.060 data_s:0.061
+INFO 2026-05-19 01:32:05 ot_train.py:502 Checkpoint policy after step 10000
+Training:  33%|█████████████████                                  | 10019/30000 [20:20<35:47,  9.31step/s]INFO 2026-05-19 01:32:12 ot_train.py:488 step:10K smpl:321K ep:536 epch:4.00 loss:0.011 grdn:0.233 lr:7.5e-06 updt_s:0.059 data_s:0.005
+Training:  33%|█████████████████                                  | 10039/30000 [20:22<41:23,  8.04step/s]INFO 2026-05-19 01:32:15 ot_train.py:488 step:10K smpl:321K ep:538 epch:4.01 loss:0.009 grdn:0.199 lr:7.5e-06 updt_s:0.059 data_s:0.065
+Training:  34%|█████████████████                                  | 10059/30000 [20:25<37:40,  8.82step/s]INFO 2026-05-19 01:32:17 ot_train.py:488 step:10K smpl:322K ep:539 epch:4.02 loss:0.010 grdn:0.212 lr:7.5e-06 updt_s:0.060 data_s:0.067
+Training:  34%|█████████████████▏                                 | 10079/30000 [20:27<36:55,  8.99step/s]INFO 2026-05-19 01:32:20 ot_train.py:488 step:10K smpl:323K ep:540 epch:4.03 loss:0.009 grdn:0.206 lr:7.5e-06 updt_s:0.058 data_s:0.062
+Training:  34%|█████████████████▏                                 | 10099/30000 [20:30<36:41,  9.04step/s]INFO 2026-05-19 01:32:22 ot_train.py:488 step:10K smpl:323K ep:541 epch:4.04 loss:0.010 grdn:0.201 lr:7.5e-06 updt_s:0.058 data_s:0.063
+Training:  34%|█████████████████▏                                 | 10119/30000 [20:32<36:19,  9.12step/s]INFO 2026-05-19 01:32:25 ot_train.py:488 step:10K smpl:324K ep:542 epch:4.04 loss:0.009 grdn:0.204 lr:7.5e-06 updt_s:0.058 data_s:0.062
+Training:  34%|█████████████████▏                                 | 10139/30000 [20:34<37:01,  8.94step/s]INFO 2026-05-19 01:32:27 ot_train.py:488 step:10K smpl:324K ep:543 epch:4.05 loss:0.010 grdn:0.222 lr:7.5e-06 updt_s:0.059 data_s:0.063
+Training:  34%|█████████████████▎                                 | 10159/30000 [20:37<37:13,  8.88step/s]INFO 2026-05-19 01:32:30 ot_train.py:488 step:10K smpl:325K ep:544 epch:4.06 loss:0.009 grdn:0.212 lr:7.5e-06 updt_s:0.059 data_s:0.062
+Training:  34%|█████████████████▎                                 | 10179/30000 [20:39<37:56,  8.71step/s]INFO 2026-05-19 01:32:32 ot_train.py:488 step:10K smpl:326K ep:545 epch:4.07 loss:0.010 grdn:0.197 lr:7.5e-06 updt_s:0.058 data_s:0.064
+Training:  34%|█████████████████▎                                 | 10199/30000 [20:42<36:15,  9.10step/s]INFO 2026-05-19 01:32:34 ot_train.py:488 step:10K smpl:326K ep:546 epch:4.08 loss:0.012 grdn:0.219 lr:7.4e-06 updt_s:0.058 data_s:0.061
+Training:  34%|█████████████████▎                                 | 10219/30000 [20:44<36:51,  8.94step/s]INFO 2026-05-19 01:32:37 ot_train.py:488 step:10K smpl:327K ep:547 epch:4.08 loss:0.008 grdn:0.201 lr:7.4e-06 updt_s:0.059 data_s:0.062
+Training:  34%|█████████████████▍                                 | 10239/30000 [20:47<37:08,  8.87step/s]INFO 2026-05-19 01:32:39 ot_train.py:488 step:10K smpl:328K ep:548 epch:4.09 loss:0.010 grdn:0.208 lr:7.4e-06 updt_s:0.059 data_s:0.066
+Training:  34%|█████████████████▍                                 | 10259/30000 [20:49<37:08,  8.86step/s]INFO 2026-05-19 01:32:42 ot_train.py:488 step:10K smpl:328K ep:549 epch:4.10 loss:0.009 grdn:0.213 lr:7.4e-06 updt_s:0.060 data_s:0.063
+Training:  34%|█████████████████▍                                 | 10279/30000 [20:52<36:19,  9.05step/s]INFO 2026-05-19 01:32:44 ot_train.py:488 step:10K smpl:329K ep:550 epch:4.11 loss:0.010 grdn:0.216 lr:7.4e-06 updt_s:0.059 data_s:0.061
+Training:  34%|█████████████████▌                                 | 10299/30000 [20:54<37:32,  8.75step/s]INFO 2026-05-19 01:32:47 ot_train.py:488 step:10K smpl:330K ep:551 epch:4.12 loss:0.010 grdn:0.224 lr:7.4e-06 updt_s:0.058 data_s:0.070
+Training:  34%|█████████████████▌                                 | 10319/30000 [20:57<35:50,  9.15step/s]INFO 2026-05-19 01:32:49 ot_train.py:488 step:10K smpl:330K ep:553 epch:4.12 loss:0.010 grdn:0.220 lr:7.4e-06 updt_s:0.059 data_s:0.060
+Training:  34%|█████████████████▌                                 | 10339/30000 [20:59<35:31,  9.22step/s]INFO 2026-05-19 01:32:52 ot_train.py:488 step:10K smpl:331K ep:554 epch:4.13 loss:0.010 grdn:0.220 lr:7.4e-06 updt_s:0.062 data_s:0.060
+Training:  35%|█████████████████▌                                 | 10359/30000 [21:01<35:17,  9.28step/s]INFO 2026-05-19 01:32:54 ot_train.py:488 step:10K smpl:332K ep:555 epch:4.14 loss:0.010 grdn:0.213 lr:7.4e-06 updt_s:0.060 data_s:0.058
+Training:  35%|█████████████████▋                                 | 10379/30000 [21:04<36:01,  9.08step/s]INFO 2026-05-19 01:32:57 ot_train.py:488 step:10K smpl:332K ep:556 epch:4.15 loss:0.009 grdn:0.226 lr:7.4e-06 updt_s:0.059 data_s:0.062
+Training:  35%|█████████████████▋                                 | 10399/30000 [21:06<37:11,  8.78step/s]INFO 2026-05-19 01:32:59 ot_train.py:488 step:10K smpl:333K ep:557 epch:4.16 loss:0.010 grdn:0.217 lr:7.4e-06 updt_s:0.061 data_s:0.061
+Training:  35%|█████████████████▋                                 | 10419/30000 [21:09<35:19,  9.24step/s]INFO 2026-05-19 01:33:01 ot_train.py:488 step:10K smpl:333K ep:558 epch:4.16 loss:0.009 grdn:0.202 lr:7.3e-06 updt_s:0.059 data_s:0.063
+Training:  35%|█████████████████▋                                 | 10439/30000 [21:11<36:07,  9.02step/s]INFO 2026-05-19 01:33:04 ot_train.py:488 step:10K smpl:334K ep:559 epch:4.17 loss:0.010 grdn:0.222 lr:7.3e-06 updt_s:0.059 data_s:0.063
+Training:  35%|█████████████████▊                                 | 10459/30000 [21:14<35:33,  9.16step/s]INFO 2026-05-19 01:33:06 ot_train.py:488 step:10K smpl:335K ep:560 epch:4.18 loss:0.009 grdn:0.207 lr:7.3e-06 updt_s:0.059 data_s:0.060
+Training:  35%|█████████████████▊                                 | 10479/30000 [21:16<35:42,  9.11step/s]INFO 2026-05-19 01:33:09 ot_train.py:488 step:10K smpl:335K ep:561 epch:4.19 loss:0.011 grdn:0.212 lr:7.3e-06 updt_s:0.060 data_s:0.060
+Training:  35%|█████████████████▊                                 | 10499/30000 [21:18<36:59,  8.78step/s]INFO 2026-05-19 01:33:11 ot_train.py:488 step:10K smpl:336K ep:562 epch:4.20 loss:0.011 grdn:0.209 lr:7.3e-06 updt_s:0.059 data_s:0.066
+Training:  35%|█████████████████▉                                 | 10519/30000 [21:21<35:37,  9.11step/s]INFO 2026-05-19 01:33:14 ot_train.py:488 step:11K smpl:337K ep:563 epch:4.20 loss:0.009 grdn:0.200 lr:7.3e-06 updt_s:0.058 data_s:0.060
+Training:  35%|█████████████████▉                                 | 10539/30000 [21:23<35:20,  9.18step/s]INFO 2026-05-19 01:33:16 ot_train.py:488 step:11K smpl:337K ep:564 epch:4.21 loss:0.009 grdn:0.211 lr:7.3e-06 updt_s:0.058 data_s:0.060
+Training:  35%|█████████████████▉                                 | 10559/30000 [21:26<35:20,  9.17step/s]INFO 2026-05-19 01:33:18 ot_train.py:488 step:11K smpl:338K ep:565 epch:4.22 loss:0.009 grdn:0.204 lr:7.3e-06 updt_s:0.059 data_s:0.060
+Training:  35%|█████████████████▉                                 | 10579/30000 [21:28<35:48,  9.04step/s]INFO 2026-05-19 01:33:21 ot_train.py:488 step:11K smpl:339K ep:566 epch:4.23 loss:0.012 grdn:0.209 lr:7.3e-06 updt_s:0.058 data_s:0.060
+Training:  35%|██████████████████                                 | 10599/30000 [21:30<36:05,  8.96step/s]INFO 2026-05-19 01:33:23 ot_train.py:488 step:11K smpl:339K ep:568 epch:4.24 loss:0.010 grdn:0.227 lr:7.3e-06 updt_s:0.060 data_s:0.059
+Training:  35%|██████████████████                                 | 10619/30000 [21:33<35:09,  9.19step/s]INFO 2026-05-19 01:33:25 ot_train.py:488 step:11K smpl:340K ep:569 epch:4.24 loss:0.010 grdn:0.206 lr:7.2e-06 updt_s:0.060 data_s:0.058
+Training:  35%|██████████████████                                 | 10639/30000 [21:35<36:20,  8.88step/s]INFO 2026-05-19 01:33:28 ot_train.py:488 step:11K smpl:340K ep:570 epch:4.25 loss:0.009 grdn:0.200 lr:7.2e-06 updt_s:0.060 data_s:0.060
+Training:  36%|██████████████████                                 | 10659/30000 [21:38<36:43,  8.78step/s]INFO 2026-05-19 01:33:30 ot_train.py:488 step:11K smpl:341K ep:571 epch:4.26 loss:0.010 grdn:0.231 lr:7.2e-06 updt_s:0.059 data_s:0.063
+Training:  36%|██████████████████▏                                | 10679/30000 [21:40<38:08,  8.44step/s]INFO 2026-05-19 01:33:33 ot_train.py:488 step:11K smpl:342K ep:572 epch:4.27 loss:0.009 grdn:0.199 lr:7.2e-06 updt_s:0.059 data_s:0.079
+Training:  36%|██████████████████▏                                | 10699/30000 [21:43<35:31,  9.06step/s]INFO 2026-05-19 01:33:36 ot_train.py:488 step:11K smpl:342K ep:573 epch:4.28 loss:0.010 grdn:0.205 lr:7.2e-06 updt_s:0.059 data_s:0.062
+Training:  36%|██████████████████▏                                | 10719/30000 [21:45<35:17,  9.11step/s]INFO 2026-05-19 01:33:38 ot_train.py:488 step:11K smpl:343K ep:574 epch:4.28 loss:0.010 grdn:0.205 lr:7.2e-06 updt_s:0.060 data_s:0.060
+Training:  36%|██████████████████▎                                | 10739/30000 [21:48<35:25,  9.06step/s]INFO 2026-05-19 01:33:40 ot_train.py:488 step:11K smpl:344K ep:575 epch:4.29 loss:0.010 grdn:0.212 lr:7.2e-06 updt_s:0.060 data_s:0.060
+Training:  36%|██████████████████▎                                | 10759/30000 [21:50<38:17,  8.38step/s]INFO 2026-05-19 01:33:43 ot_train.py:488 step:11K smpl:344K ep:576 epch:4.30 loss:0.010 grdn:0.215 lr:7.2e-06 updt_s:0.059 data_s:0.065
+Training:  36%|██████████████████▎                                | 10779/30000 [21:53<35:31,  9.02step/s]INFO 2026-05-19 01:33:45 ot_train.py:488 step:11K smpl:345K ep:577 epch:4.31 loss:0.009 grdn:0.218 lr:7.2e-06 updt_s:0.059 data_s:0.061
+Training:  36%|██████████████████▎                                | 10799/30000 [21:55<35:00,  9.14step/s]INFO 2026-05-19 01:33:48 ot_train.py:488 step:11K smpl:346K ep:578 epch:4.32 loss:0.009 grdn:0.207 lr:7.2e-06 updt_s:0.059 data_s:0.060
+Training:  36%|██████████████████▍                                | 10819/30000 [21:57<34:52,  9.17step/s]INFO 2026-05-19 01:33:50 ot_train.py:488 step:11K smpl:346K ep:579 epch:4.32 loss:0.008 grdn:0.179 lr:7.2e-06 updt_s:0.060 data_s:0.061
+Training:  36%|██████████████████▍                                | 10839/30000 [22:00<35:52,  8.90step/s]INFO 2026-05-19 01:33:53 ot_train.py:488 step:11K smpl:347K ep:580 epch:4.33 loss:0.010 grdn:0.216 lr:7.1e-06 updt_s:0.061 data_s:0.060
+Training:  36%|██████████████████▍                                | 10859/30000 [22:02<36:45,  8.68step/s]INFO 2026-05-19 01:33:55 ot_train.py:488 step:11K smpl:348K ep:581 epch:4.34 loss:0.011 grdn:0.216 lr:7.1e-06 updt_s:0.061 data_s:0.063
+Training:  36%|██████████████████▍                                | 10879/30000 [22:05<34:41,  9.19step/s]INFO 2026-05-19 01:33:57 ot_train.py:488 step:11K smpl:348K ep:583 epch:4.35 loss:0.012 grdn:0.234 lr:7.1e-06 updt_s:0.059 data_s:0.061
+Training:  36%|██████████████████▌                                | 10899/30000 [22:07<33:28,  9.51step/s]INFO 2026-05-19 01:34:00 ot_train.py:488 step:11K smpl:349K ep:584 epch:4.36 loss:0.010 grdn:0.209 lr:7.1e-06 updt_s:0.059 data_s:0.058
+Training:  36%|██████████████████▌                                | 10919/30000 [22:10<35:41,  8.91step/s]INFO 2026-05-19 01:34:02 ot_train.py:488 step:11K smpl:349K ep:585 epch:4.36 loss:0.010 grdn:0.209 lr:7.1e-06 updt_s:0.060 data_s:0.060
+Training:  36%|██████████████████▌                                | 10939/30000 [22:12<34:37,  9.17step/s]INFO 2026-05-19 01:34:05 ot_train.py:488 step:11K smpl:350K ep:586 epch:4.37 loss:0.011 grdn:0.236 lr:7.1e-06 updt_s:0.060 data_s:0.057
+Training:  37%|██████████████████▋                                | 10959/30000 [22:14<35:15,  9.00step/s]INFO 2026-05-19 01:34:07 ot_train.py:488 step:11K smpl:351K ep:587 epch:4.38 loss:0.010 grdn:0.227 lr:7.1e-06 updt_s:0.059 data_s:0.059
+Training:  37%|██████████████████▋                                | 10979/30000 [22:17<36:42,  8.63step/s]INFO 2026-05-19 01:34:10 ot_train.py:488 step:11K smpl:351K ep:588 epch:4.39 loss:0.011 grdn:0.244 lr:7.1e-06 updt_s:0.059 data_s:0.064
+Training:  37%|██████████████████▋                                | 10999/30000 [22:19<35:28,  8.93step/s]INFO 2026-05-19 01:34:12 ot_train.py:488 step:11K smpl:352K ep:589 epch:4.40 loss:0.009 grdn:0.206 lr:7.1e-06 updt_s:0.059 data_s:0.064
+Training:  37%|██████████████████▋                                | 11019/30000 [22:22<35:23,  8.94step/s]INFO 2026-05-19 01:34:14 ot_train.py:488 step:11K smpl:353K ep:590 epch:4.40 loss:0.012 grdn:0.215 lr:7.1e-06 updt_s:0.060 data_s:0.063
+Training:  37%|██████████████████▊                                | 11039/30000 [22:24<35:02,  9.02step/s]INFO 2026-05-19 01:34:17 ot_train.py:488 step:11K smpl:353K ep:591 epch:4.41 loss:0.013 grdn:0.274 lr:7.0e-06 updt_s:0.059 data_s:0.061
+Training:  37%|██████████████████▊                                | 11059/30000 [22:27<33:29,  9.43step/s]INFO 2026-05-19 01:34:19 ot_train.py:488 step:11K smpl:354K ep:592 epch:4.42 loss:0.010 grdn:0.201 lr:7.0e-06 updt_s:0.059 data_s:0.059
+Training:  37%|██████████████████▊                                | 11079/30000 [22:29<34:52,  9.04step/s]INFO 2026-05-19 01:34:22 ot_train.py:488 step:11K smpl:355K ep:593 epch:4.43 loss:0.009 grdn:0.199 lr:7.0e-06 updt_s:0.059 data_s:0.061
+Training:  37%|██████████████████▊                                | 11099/30000 [22:31<35:29,  8.88step/s]INFO 2026-05-19 01:34:24 ot_train.py:488 step:11K smpl:355K ep:594 epch:4.44 loss:0.008 grdn:0.197 lr:7.0e-06 updt_s:0.060 data_s:0.062
+Training:  37%|██████████████████▉                                | 11119/30000 [22:34<35:10,  8.95step/s]INFO 2026-05-19 01:34:26 ot_train.py:488 step:11K smpl:356K ep:595 epch:4.44 loss:0.009 grdn:0.215 lr:7.0e-06 updt_s:0.058 data_s:0.062
+Training:  37%|██████████████████▉                                | 11139/30000 [22:36<35:36,  8.83step/s]INFO 2026-05-19 01:34:29 ot_train.py:488 step:11K smpl:356K ep:596 epch:4.45 loss:0.011 grdn:0.220 lr:7.0e-06 updt_s:0.058 data_s:0.065
+Training:  37%|██████████████████▉                                | 11159/30000 [22:39<34:47,  9.03step/s]INFO 2026-05-19 01:34:31 ot_train.py:488 step:11K smpl:357K ep:598 epch:4.46 loss:0.010 grdn:0.212 lr:7.0e-06 updt_s:0.060 data_s:0.061
+Training:  37%|███████████████████                                | 11179/30000 [22:41<35:41,  8.79step/s]INFO 2026-05-19 01:34:34 ot_train.py:488 step:11K smpl:358K ep:599 epch:4.47 loss:0.009 grdn:0.200 lr:7.0e-06 updt_s:0.060 data_s:0.063
+Training:  37%|███████████████████                                | 11199/30000 [22:44<33:41,  9.30step/s]INFO 2026-05-19 01:34:36 ot_train.py:488 step:11K smpl:358K ep:600 epch:4.48 loss:0.010 grdn:0.227 lr:7.0e-06 updt_s:0.059 data_s:0.059
+Training:  37%|███████████████████                                | 11219/30000 [22:46<34:39,  9.03step/s]INFO 2026-05-19 01:34:39 ot_train.py:488 step:11K smpl:359K ep:601 epch:4.48 loss:0.010 grdn:0.240 lr:7.0e-06 updt_s:0.059 data_s:0.059
+Training:  37%|███████████████████                                | 11239/30000 [22:48<34:30,  9.06step/s]INFO 2026-05-19 01:34:41 ot_train.py:488 step:11K smpl:360K ep:602 epch:4.49 loss:0.011 grdn:0.222 lr:7.0e-06 updt_s:0.059 data_s:0.058
+Training:  38%|███████████████████▏                               | 11259/30000 [22:51<33:40,  9.27step/s]INFO 2026-05-19 01:34:43 ot_train.py:488 step:11K smpl:360K ep:603 epch:4.50 loss:0.010 grdn:0.219 lr:6.9e-06 updt_s:0.059 data_s:0.060
+Training:  38%|███████████████████▏                               | 11279/30000 [22:53<33:15,  9.38step/s]INFO 2026-05-19 01:34:46 ot_train.py:488 step:11K smpl:361K ep:604 epch:4.51 loss:0.011 grdn:0.242 lr:6.9e-06 updt_s:0.058 data_s:0.060
+Training:  38%|███████████████████▏                               | 11299/30000 [22:55<34:03,  9.15step/s]INFO 2026-05-19 01:34:48 ot_train.py:488 step:11K smpl:362K ep:605 epch:4.52 loss:0.010 grdn:0.226 lr:6.9e-06 updt_s:0.059 data_s:0.058
+Training:  38%|███████████████████▏                               | 11319/30000 [22:58<34:05,  9.13step/s]INFO 2026-05-19 01:34:50 ot_train.py:488 step:11K smpl:362K ep:606 epch:4.52 loss:0.010 grdn:0.213 lr:6.9e-06 updt_s:0.059 data_s:0.058
+Training:  38%|███████████████████▎                               | 11339/30000 [23:00<33:17,  9.34step/s]INFO 2026-05-19 01:34:53 ot_train.py:488 step:11K smpl:363K ep:607 epch:4.53 loss:0.008 grdn:0.192 lr:6.9e-06 updt_s:0.059 data_s:0.056
+Training:  38%|███████████████████▎                               | 11359/30000 [23:02<33:19,  9.32step/s]INFO 2026-05-19 01:34:55 ot_train.py:488 step:11K smpl:364K ep:608 epch:4.54 loss:0.009 grdn:0.220 lr:6.9e-06 updt_s:0.059 data_s:0.057
+Training:  38%|███████████████████▎                               | 11379/30000 [23:05<34:38,  8.96step/s]INFO 2026-05-19 01:34:58 ot_train.py:488 step:11K smpl:364K ep:609 epch:4.55 loss:0.009 grdn:0.221 lr:6.9e-06 updt_s:0.060 data_s:0.060
+Training:  38%|███████████████████▍                               | 11399/30000 [23:07<33:55,  9.14step/s]INFO 2026-05-19 01:35:00 ot_train.py:488 step:11K smpl:365K ep:610 epch:4.56 loss:0.008 grdn:0.185 lr:6.9e-06 updt_s:0.059 data_s:0.061
+Training:  38%|███████████████████▍                               | 11419/30000 [23:10<35:02,  8.84step/s]INFO 2026-05-19 01:35:02 ot_train.py:488 step:11K smpl:365K ep:611 epch:4.56 loss:0.010 grdn:0.203 lr:6.9e-06 updt_s:0.059 data_s:0.060
+Training:  38%|███████████████████▍                               | 11439/30000 [23:12<33:34,  9.21step/s]INFO 2026-05-19 01:35:05 ot_train.py:488 step:11K smpl:366K ep:613 epch:4.57 loss:0.011 grdn:0.219 lr:6.9e-06 updt_s:0.058 data_s:0.059
+Training:  38%|███████████████████▍                               | 11459/30000 [23:14<34:26,  8.97step/s]INFO 2026-05-19 01:35:07 ot_train.py:488 step:11K smpl:367K ep:614 epch:4.58 loss:0.011 grdn:0.224 lr:6.8e-06 updt_s:0.059 data_s:0.059
+Training:  38%|███████████████████▌                               | 11479/30000 [23:17<33:53,  9.11step/s]INFO 2026-05-19 01:35:09 ot_train.py:488 step:11K smpl:367K ep:615 epch:4.59 loss:0.013 grdn:0.236 lr:6.8e-06 updt_s:0.059 data_s:0.059
+Training:  38%|███████████████████▌                               | 11499/30000 [23:19<33:10,  9.30step/s]INFO 2026-05-19 01:35:12 ot_train.py:488 step:12K smpl:368K ep:616 epch:4.60 loss:0.010 grdn:0.229 lr:6.8e-06 updt_s:0.059 data_s:0.060
+Training:  38%|███████████████████▌                               | 11519/30000 [23:22<34:11,  9.01step/s]INFO 2026-05-19 01:35:14 ot_train.py:488 step:12K smpl:369K ep:617 epch:4.60 loss:0.008 grdn:0.194 lr:6.8e-06 updt_s:0.059 data_s:0.060
+Training:  38%|███████████████████▌                               | 11539/30000 [23:24<33:35,  9.16step/s]INFO 2026-05-19 01:35:17 ot_train.py:488 step:12K smpl:369K ep:618 epch:4.61 loss:0.009 grdn:0.226 lr:6.8e-06 updt_s:0.058 data_s:0.061
+Training:  39%|███████████████████▋                               | 11559/30000 [23:26<33:08,  9.27step/s]INFO 2026-05-19 01:35:19 ot_train.py:488 step:12K smpl:370K ep:619 epch:4.62 loss:0.009 grdn:0.223 lr:6.8e-06 updt_s:0.060 data_s:0.056
+Training:  39%|███████████████████▋                               | 11579/30000 [23:29<33:02,  9.29step/s]INFO 2026-05-19 01:35:21 ot_train.py:488 step:12K smpl:371K ep:620 epch:4.63 loss:0.010 grdn:0.228 lr:6.8e-06 updt_s:0.058 data_s:0.059
+Training:  39%|███████████████████▋                               | 11599/30000 [23:31<32:58,  9.30step/s]INFO 2026-05-19 01:35:24 ot_train.py:488 step:12K smpl:371K ep:621 epch:4.63 loss:0.013 grdn:0.257 lr:6.8e-06 updt_s:0.059 data_s:0.057
+Training:  39%|███████████████████▊                               | 11619/30000 [23:33<32:32,  9.41step/s]INFO 2026-05-19 01:35:26 ot_train.py:488 step:12K smpl:372K ep:622 epch:4.64 loss:0.010 grdn:0.223 lr:6.8e-06 updt_s:0.059 data_s:0.057
+Training:  39%|███████████████████▊                               | 11639/30000 [23:36<32:29,  9.42step/s]INFO 2026-05-19 01:35:28 ot_train.py:488 step:12K smpl:372K ep:623 epch:4.65 loss:0.011 grdn:0.213 lr:6.8e-06 updt_s:0.058 data_s:0.061
+Training:  39%|███████████████████▊                               | 11659/30000 [23:38<33:59,  8.99step/s]INFO 2026-05-19 01:35:31 ot_train.py:488 step:12K smpl:373K ep:624 epch:4.66 loss:0.009 grdn:0.211 lr:6.7e-06 updt_s:0.059 data_s:0.062
+Training:  39%|███████████████████▊                               | 11679/30000 [23:40<33:31,  9.11step/s]INFO 2026-05-19 01:35:33 ot_train.py:488 step:12K smpl:374K ep:625 epch:4.67 loss:0.009 grdn:0.208 lr:6.7e-06 updt_s:0.059 data_s:0.059
+Training:  39%|███████████████████▉                               | 11699/30000 [23:43<32:16,  9.45step/s]INFO 2026-05-19 01:35:35 ot_train.py:488 step:12K smpl:374K ep:626 epch:4.67 loss:0.010 grdn:0.219 lr:6.7e-06 updt_s:0.059 data_s:0.056
+Training:  39%|███████████████████▉                               | 11719/30000 [23:45<33:19,  9.14step/s]INFO 2026-05-19 01:35:38 ot_train.py:488 step:12K smpl:375K ep:628 epch:4.68 loss:0.013 grdn:0.254 lr:6.7e-06 updt_s:0.059 data_s:0.060
+Training:  39%|███████████████████▉                               | 11739/30000 [23:48<33:04,  9.20step/s]INFO 2026-05-19 01:35:40 ot_train.py:488 step:12K smpl:376K ep:629 epch:4.69 loss:0.010 grdn:0.215 lr:6.7e-06 updt_s:0.060 data_s:0.058
+Training:  39%|███████████████████▉                               | 11759/30000 [23:50<33:25,  9.10step/s]INFO 2026-05-19 01:35:43 ot_train.py:488 step:12K smpl:376K ep:630 epch:4.70 loss:0.010 grdn:0.231 lr:6.7e-06 updt_s:0.060 data_s:0.059
+Training:  39%|████████████████████                               | 11779/30000 [23:52<33:19,  9.11step/s]INFO 2026-05-19 01:35:45 ot_train.py:488 step:12K smpl:377K ep:631 epch:4.71 loss:0.008 grdn:0.191 lr:6.7e-06 updt_s:0.059 data_s:0.060
+Training:  39%|████████████████████                               | 11799/30000 [23:55<32:45,  9.26step/s]INFO 2026-05-19 01:35:47 ot_train.py:488 step:12K smpl:378K ep:632 epch:4.71 loss:0.011 grdn:0.226 lr:6.7e-06 updt_s:0.059 data_s:0.060
+Training:  39%|████████████████████                               | 11819/30000 [23:57<33:13,  9.12step/s]INFO 2026-05-19 01:35:50 ot_train.py:488 step:12K smpl:378K ep:633 epch:4.72 loss:0.008 grdn:0.193 lr:6.7e-06 updt_s:0.060 data_s:0.057
+Training:  39%|████████████████████▏                              | 11839/30000 [23:59<33:10,  9.12step/s]INFO 2026-05-19 01:35:52 ot_train.py:488 step:12K smpl:379K ep:634 epch:4.73 loss:0.009 grdn:0.218 lr:6.7e-06 updt_s:0.061 data_s:0.057
+Training:  40%|████████████████████▏                              | 11859/30000 [24:02<32:14,  9.38step/s]INFO 2026-05-19 01:35:54 ot_train.py:488 step:12K smpl:380K ep:635 epch:4.74 loss:0.010 grdn:0.209 lr:6.6e-06 updt_s:0.060 data_s:0.057
+Training:  40%|████████████████████▏                              | 11879/30000 [24:04<31:43,  9.52step/s]INFO 2026-05-19 01:35:57 ot_train.py:488 step:12K smpl:380K ep:636 epch:4.75 loss:0.012 grdn:0.225 lr:6.6e-06 updt_s:0.059 data_s:0.056
+Training:  40%|████████████████████▏                              | 11899/30000 [24:06<30:16,  9.97step/s]INFO 2026-05-19 01:35:59 ot_train.py:488 step:12K smpl:381K ep:637 epch:4.75 loss:0.008 grdn:0.190 lr:6.6e-06 updt_s:0.059 data_s:0.056
+Training:  40%|████████████████████▎                              | 11919/30000 [24:09<32:55,  9.15step/s]INFO 2026-05-19 01:36:02 ot_train.py:488 step:12K smpl:381K ep:638 epch:4.76 loss:0.012 grdn:0.239 lr:6.6e-06 updt_s:0.059 data_s:0.061
+Training:  40%|████████████████████▎                              | 11939/30000 [24:11<32:48,  9.17step/s]INFO 2026-05-19 01:36:04 ot_train.py:488 step:12K smpl:382K ep:639 epch:4.77 loss:0.010 grdn:0.233 lr:6.6e-06 updt_s:0.059 data_s:0.059
+Training:  40%|████████████████████▎                              | 11959/30000 [24:14<31:53,  9.43step/s]INFO 2026-05-19 01:36:06 ot_train.py:488 step:12K smpl:383K ep:640 epch:4.78 loss:0.010 grdn:0.217 lr:6.6e-06 updt_s:0.059 data_s:0.056
+Training:  40%|████████████████████▎                              | 11979/30000 [24:16<32:08,  9.35step/s]INFO 2026-05-19 01:36:09 ot_train.py:488 step:12K smpl:383K ep:641 epch:4.79 loss:0.011 grdn:0.228 lr:6.6e-06 updt_s:0.058 data_s:0.059
+Training:  40%|████████████████████▍                              | 11999/30000 [24:18<32:40,  9.18step/s]INFO 2026-05-19 01:36:11 ot_train.py:488 step:12K smpl:384K ep:643 epch:4.79 loss:0.009 grdn:0.201 lr:6.6e-06 updt_s:0.059 data_s:0.059
+Training:  40%|████████████████████▍                              | 12019/30000 [24:21<32:19,  9.27step/s]INFO 2026-05-19 01:36:13 ot_train.py:488 step:12K smpl:385K ep:644 epch:4.80 loss:0.010 grdn:0.206 lr:6.6e-06 updt_s:0.059 data_s:0.060
+Training:  40%|████████████████████▍                              | 12039/30000 [24:23<33:46,  8.86step/s]INFO 2026-05-19 01:36:16 ot_train.py:488 step:12K smpl:385K ep:645 epch:4.81 loss:0.010 grdn:0.193 lr:6.6e-06 updt_s:0.059 data_s:0.062
+Training:  40%|████████████████████▌                              | 12059/30000 [24:25<32:52,  9.09step/s]INFO 2026-05-19 01:36:18 ot_train.py:488 step:12K smpl:386K ep:646 epch:4.82 loss:0.009 grdn:0.206 lr:6.5e-06 updt_s:0.060 data_s:0.054
+Training:  40%|████████████████████▌                              | 12079/30000 [24:28<32:45,  9.12step/s]INFO 2026-05-19 01:36:20 ot_train.py:488 step:12K smpl:387K ep:647 epch:4.83 loss:0.011 grdn:0.238 lr:6.5e-06 updt_s:0.060 data_s:0.058
+Training:  40%|████████████████████▌                              | 12099/30000 [24:30<31:17,  9.53step/s]INFO 2026-05-19 01:36:23 ot_train.py:488 step:12K smpl:387K ep:648 epch:4.83 loss:0.008 grdn:0.213 lr:6.5e-06 updt_s:0.060 data_s:0.053
+Training:  40%|████████████████████▌                              | 12119/30000 [24:32<31:51,  9.35step/s]INFO 2026-05-19 01:36:25 ot_train.py:488 step:12K smpl:388K ep:649 epch:4.84 loss:0.008 grdn:0.200 lr:6.5e-06 updt_s:0.059 data_s:0.056
+Training:  40%|████████████████████▋                              | 12139/30000 [24:35<31:37,  9.41step/s]INFO 2026-05-19 01:36:27 ot_train.py:488 step:12K smpl:388K ep:650 epch:4.85 loss:0.009 grdn:0.205 lr:6.5e-06 updt_s:0.058 data_s:0.057
+Training:  41%|████████████████████▋                              | 12159/30000 [24:37<32:20,  9.20step/s]INFO 2026-05-19 01:36:30 ot_train.py:488 step:12K smpl:389K ep:651 epch:4.86 loss:0.010 grdn:0.216 lr:6.5e-06 updt_s:0.058 data_s:0.059
+Training:  41%|████████████████████▋                              | 12179/30000 [24:39<31:44,  9.36step/s]INFO 2026-05-19 01:36:32 ot_train.py:488 step:12K smpl:390K ep:652 epch:4.87 loss:0.010 grdn:0.219 lr:6.5e-06 updt_s:0.059 data_s:0.057
+Training:  41%|████████████████████▋                              | 12199/30000 [24:42<31:54,  9.30step/s]INFO 2026-05-19 01:36:34 ot_train.py:488 step:12K smpl:390K ep:653 epch:4.87 loss:0.010 grdn:0.203 lr:6.5e-06 updt_s:0.058 data_s:0.059
+Training:  41%|████████████████████▊                              | 12219/30000 [24:44<31:46,  9.32step/s]INFO 2026-05-19 01:36:37 ot_train.py:488 step:12K smpl:391K ep:654 epch:4.88 loss:0.011 grdn:0.211 lr:6.5e-06 updt_s:0.058 data_s:0.059
+Training:  41%|████████████████████▊                              | 12239/30000 [24:46<31:30,  9.39step/s]INFO 2026-05-19 01:36:39 ot_train.py:488 step:12K smpl:392K ep:655 epch:4.89 loss:0.010 grdn:0.223 lr:6.5e-06 updt_s:0.058 data_s:0.057
+Training:  41%|████████████████████▊                              | 12259/30000 [24:49<32:36,  9.07step/s]INFO 2026-05-19 01:36:41 ot_train.py:488 step:12K smpl:392K ep:656 epch:4.90 loss:0.009 grdn:0.213 lr:6.4e-06 updt_s:0.058 data_s:0.058
+Training:  41%|████████████████████▊                              | 12279/30000 [24:51<28:30, 10.36step/s]INFO 2026-05-19 01:36:44 ot_train.py:488 step:12K smpl:393K ep:657 epch:4.91 loss:0.011 grdn:0.241 lr:6.4e-06 updt_s:0.059 data_s:0.058
+Training:  41%|████████████████████▉                              | 12299/30000 [24:53<32:13,  9.15step/s]INFO 2026-05-19 01:36:46 ot_train.py:488 step:12K smpl:394K ep:659 epch:4.91 loss:0.010 grdn:0.213 lr:6.4e-06 updt_s:0.058 data_s:0.060
+Training:  41%|████████████████████▉                              | 12319/30000 [24:56<31:51,  9.25step/s]INFO 2026-05-19 01:36:49 ot_train.py:488 step:12K smpl:394K ep:660 epch:4.92 loss:0.009 grdn:0.214 lr:6.4e-06 updt_s:0.059 data_s:0.060
+Training:  41%|████████████████████▉                              | 12339/30000 [24:58<32:22,  9.09step/s]INFO 2026-05-19 01:36:51 ot_train.py:488 step:12K smpl:395K ep:661 epch:4.93 loss:0.010 grdn:0.221 lr:6.4e-06 updt_s:0.061 data_s:0.060
+Training:  41%|█████████████████████                              | 12359/30000 [25:01<29:52,  9.84step/s]INFO 2026-05-19 01:36:53 ot_train.py:488 step:12K smpl:396K ep:662 epch:4.94 loss:0.009 grdn:0.198 lr:6.4e-06 updt_s:0.060 data_s:0.054
+Training:  41%|█████████████████████                              | 12379/30000 [25:03<42:54,  6.85step/s]INFO 2026-05-19 01:36:56 ot_train.py:488 step:12K smpl:396K ep:663 epch:4.95 loss:0.010 grdn:0.230 lr:6.4e-06 updt_s:0.059 data_s:0.084
+Training:  41%|█████████████████████                              | 12399/30000 [25:06<36:58,  7.93step/s]INFO 2026-05-19 01:36:58 ot_train.py:488 step:12K smpl:397K ep:664 epch:4.95 loss:0.008 grdn:0.197 lr:6.4e-06 updt_s:0.059 data_s:0.055
+Training:  41%|█████████████████████                              | 12419/30000 [25:08<37:53,  7.73step/s]INFO 2026-05-19 01:37:01 ot_train.py:488 step:12K smpl:397K ep:665 epch:4.96 loss:0.010 grdn:0.220 lr:6.4e-06 updt_s:0.059 data_s:0.058
+Training:  41%|█████████████████████▏                             | 12439/30000 [25:11<39:21,  7.43step/s]INFO 2026-05-19 01:37:03 ot_train.py:488 step:12K smpl:398K ep:666 epch:4.97 loss:0.009 grdn:0.246 lr:6.4e-06 updt_s:0.059 data_s:0.061
+Training:  42%|█████████████████████▏                             | 12459/30000 [25:13<37:52,  7.72step/s]INFO 2026-05-19 01:37:06 ot_train.py:488 step:12K smpl:399K ep:667 epch:4.98 loss:0.009 grdn:0.219 lr:6.3e-06 updt_s:0.059 data_s:0.058
+Training:  42%|█████████████████████▏                             | 12479/30000 [25:15<35:52,  8.14step/s]INFO 2026-05-19 01:37:08 ot_train.py:488 step:12K smpl:399K ep:668 epch:4.99 loss:0.010 grdn:0.219 lr:6.3e-06 updt_s:0.058 data_s:0.058
+Training:  42%|█████████████████████▏                             | 12499/30000 [25:18<36:10,  8.06step/s]INFO 2026-05-19 01:37:10 ot_train.py:488 step:12K smpl:400K ep:669 epch:4.99 loss:0.011 grdn:0.228 lr:6.3e-06 updt_s:0.060 data_s:0.055
+Training:  42%|█████████████████████▎                             | 12519/30000 [25:20<36:49,  7.91step/s]INFO 2026-05-19 01:37:13 ot_train.py:488 step:13K smpl:401K ep:670 epch:5.00 loss:0.009 grdn:0.207 lr:6.3e-06 updt_s:0.058 data_s:0.059
+Training:  42%|█████████████████████▎                             | 12539/30000 [25:22<36:04,  8.07step/s]INFO 2026-05-19 01:37:15 ot_train.py:488 step:13K smpl:401K ep:671 epch:5.01 loss:0.009 grdn:0.203 lr:6.3e-06 updt_s:0.060 data_s:0.055
+Training:  42%|█████████████████████▎                             | 12559/30000 [25:25<36:40,  7.93step/s]INFO 2026-05-19 01:37:17 ot_train.py:488 step:13K smpl:402K ep:672 epch:5.02 loss:0.009 grdn:0.201 lr:6.3e-06 updt_s:0.058 data_s:0.058
+Training:  42%|█████████████████████▍                             | 12579/30000 [25:27<35:10,  8.25step/s]INFO 2026-05-19 01:37:20 ot_train.py:488 step:13K smpl:403K ep:674 epch:5.03 loss:0.010 grdn:0.211 lr:6.3e-06 updt_s:0.058 data_s:0.057
+Training:  42%|█████████████████████▍                             | 12599/30000 [25:29<34:29,  8.41step/s]INFO 2026-05-19 01:37:22 ot_train.py:488 step:13K smpl:403K ep:675 epch:5.03 loss:0.008 grdn:0.186 lr:6.3e-06 updt_s:0.058 data_s:0.056
+Training:  42%|█████████████████████▍                             | 12619/30000 [25:31<33:00,  8.77step/s]INFO 2026-05-19 01:37:24 ot_train.py:488 step:13K smpl:404K ep:676 epch:5.04 loss:0.010 grdn:0.216 lr:6.3e-06 updt_s:0.058 data_s:0.056
+Training:  42%|█████████████████████▍                             | 12639/30000 [25:34<33:34,  8.62step/s]INFO 2026-05-19 01:37:27 ot_train.py:488 step:13K smpl:404K ep:677 epch:5.05 loss:0.010 grdn:0.213 lr:6.3e-06 updt_s:0.059 data_s:0.057
+Training:  42%|█████████████████████▌                             | 12659/30000 [25:36<33:00,  8.76step/s]INFO 2026-05-19 01:37:29 ot_train.py:488 step:13K smpl:405K ep:678 epch:5.06 loss:0.013 grdn:0.235 lr:6.2e-06 updt_s:0.058 data_s:0.060
+Training:  42%|█████████████████████▌                             | 12679/30000 [25:39<31:23,  9.19step/s]INFO 2026-05-19 01:37:31 ot_train.py:488 step:13K smpl:406K ep:679 epch:5.07 loss:0.009 grdn:0.224 lr:6.2e-06 updt_s:0.058 data_s:0.061
+Training:  42%|█████████████████████▌                             | 12699/30000 [25:41<31:15,  9.22step/s]INFO 2026-05-19 01:37:34 ot_train.py:488 step:13K smpl:406K ep:680 epch:5.07 loss:0.008 grdn:0.197 lr:6.2e-06 updt_s:0.060 data_s:0.059
+Training:  42%|█████████████████████▌                             | 12719/30000 [25:43<31:02,  9.28step/s]INFO 2026-05-19 01:37:36 ot_train.py:488 step:13K smpl:407K ep:681 epch:5.08 loss:0.010 grdn:0.209 lr:6.2e-06 updt_s:0.060 data_s:0.057
+Training:  42%|█████████████████████▋                             | 12739/30000 [25:46<31:15,  9.20step/s]INFO 2026-05-19 01:37:38 ot_train.py:488 step:13K smpl:408K ep:682 epch:5.09 loss:0.009 grdn:0.206 lr:6.2e-06 updt_s:0.060 data_s:0.060
+Training:  43%|█████████████████████▋                             | 12759/30000 [25:48<32:09,  8.94step/s]INFO 2026-05-19 01:37:41 ot_train.py:488 step:13K smpl:408K ep:683 epch:5.10 loss:0.010 grdn:0.218 lr:6.2e-06 updt_s:0.059 data_s:0.063
+Training:  43%|█████████████████████▋                             | 12779/30000 [25:51<31:17,  9.17step/s]INFO 2026-05-19 01:37:43 ot_train.py:488 step:13K smpl:409K ep:684 epch:5.11 loss:0.008 grdn:0.205 lr:6.2e-06 updt_s:0.059 data_s:0.062
+Training:  43%|█████████████████████▊                             | 12799/30000 [25:53<31:54,  8.99step/s]INFO 2026-05-19 01:37:46 ot_train.py:488 step:13K smpl:410K ep:685 epch:5.11 loss:0.008 grdn:0.201 lr:6.2e-06 updt_s:0.060 data_s:0.060
+Training:  43%|█████████████████████▊                             | 12819/30000 [25:55<31:36,  9.06step/s]INFO 2026-05-19 01:37:48 ot_train.py:488 step:13K smpl:410K ep:686 epch:5.12 loss:0.007 grdn:0.179 lr:6.2e-06 updt_s:0.059 data_s:0.061
+Training:  43%|█████████████████████▊                             | 12839/30000 [25:58<31:59,  8.94step/s]INFO 2026-05-19 01:37:51 ot_train.py:488 step:13K smpl:411K ep:687 epch:5.13 loss:0.009 grdn:0.208 lr:6.2e-06 updt_s:0.059 data_s:0.059
+Training:  43%|█████████████████████▊                             | 12859/30000 [26:00<31:47,  8.98step/s]INFO 2026-05-19 01:37:53 ot_train.py:488 step:13K smpl:412K ep:689 epch:5.14 loss:0.009 grdn:0.202 lr:6.1e-06 updt_s:0.059 data_s:0.058
+Training:  43%|█████████████████████▉                             | 12879/30000 [26:03<31:54,  8.94step/s]INFO 2026-05-19 01:37:55 ot_train.py:488 step:13K smpl:412K ep:690 epch:5.15 loss:0.010 grdn:0.234 lr:6.1e-06 updt_s:0.060 data_s:0.060
+Training:  43%|█████████████████████▉                             | 12899/30000 [26:05<31:25,  9.07step/s]INFO 2026-05-19 01:37:58 ot_train.py:488 step:13K smpl:413K ep:691 epch:5.15 loss:0.010 grdn:0.209 lr:6.1e-06 updt_s:0.059 data_s:0.062
+Training:  43%|█████████████████████▉                             | 12919/30000 [26:07<30:52,  9.22step/s]INFO 2026-05-19 01:38:00 ot_train.py:488 step:13K smpl:413K ep:692 epch:5.16 loss:0.008 grdn:0.178 lr:6.1e-06 updt_s:0.058 data_s:0.062
+Training:  43%|█████████████████████▉                             | 12939/30000 [26:10<31:14,  9.10step/s]INFO 2026-05-19 01:38:03 ot_train.py:488 step:13K smpl:414K ep:693 epch:5.17 loss:0.009 grdn:0.205 lr:6.1e-06 updt_s:0.059 data_s:0.062
+Training:  43%|██████████████████████                             | 12959/30000 [26:12<31:52,  8.91step/s]INFO 2026-05-19 01:38:05 ot_train.py:488 step:13K smpl:415K ep:694 epch:5.18 loss:0.009 grdn:0.217 lr:6.1e-06 updt_s:0.058 data_s:0.064
+Training:  43%|██████████████████████                             | 12979/30000 [26:15<31:18,  9.06step/s]INFO 2026-05-19 01:38:07 ot_train.py:488 step:13K smpl:415K ep:695 epch:5.19 loss:0.010 grdn:0.225 lr:6.1e-06 updt_s:0.059 data_s:0.061
+Training:  43%|██████████████████████                             | 12999/30000 [26:17<30:31,  9.28step/s]INFO 2026-05-19 01:38:10 ot_train.py:488 step:13K smpl:416K ep:696 epch:5.19 loss:0.010 grdn:0.215 lr:6.1e-06 updt_s:0.058 data_s:0.060
+Training:  43%|██████████████████████▏                            | 13019/30000 [26:19<30:25,  9.30step/s]INFO 2026-05-19 01:38:12 ot_train.py:488 step:13K smpl:417K ep:697 epch:5.20 loss:0.010 grdn:0.226 lr:6.1e-06 updt_s:0.057 data_s:0.059
+Training:  43%|██████████████████████▏                            | 13039/30000 [26:22<34:25,  8.21step/s]INFO 2026-05-19 01:38:15 ot_train.py:488 step:13K smpl:417K ep:698 epch:5.21 loss:0.010 grdn:0.218 lr:6.1e-06 updt_s:0.059 data_s:0.064
+Training:  44%|██████████████████████▏                            | 13059/30000 [26:24<35:12,  8.02step/s]INFO 2026-05-19 01:38:17 ot_train.py:488 step:13K smpl:418K ep:699 epch:5.22 loss:0.009 grdn:0.222 lr:6.0e-06 updt_s:0.059 data_s:0.061
+Training:  44%|██████████████████████▏                            | 13079/30000 [26:27<36:25,  7.74step/s]INFO 2026-05-19 01:38:19 ot_train.py:488 step:13K smpl:419K ep:700 epch:5.23 loss:0.009 grdn:0.203 lr:6.0e-06 updt_s:0.060 data_s:0.061
+Training:  44%|██████████████████████▎                            | 13099/30000 [26:29<36:38,  7.69step/s]INFO 2026-05-19 01:38:22 ot_train.py:488 step:13K smpl:419K ep:701 epch:5.23 loss:0.011 grdn:0.212 lr:6.0e-06 updt_s:0.058 data_s:0.062
+Training:  44%|██████████████████████▎                            | 13119/30000 [26:32<35:01,  8.03step/s]INFO 2026-05-19 01:38:24 ot_train.py:488 step:13K smpl:420K ep:702 epch:5.24 loss:0.009 grdn:0.208 lr:6.0e-06 updt_s:0.059 data_s:0.060
+Training:  44%|██████████████████████▎                            | 13139/30000 [26:34<34:30,  8.14step/s]INFO 2026-05-19 01:38:27 ot_train.py:488 step:13K smpl:420K ep:704 epch:5.25 loss:0.010 grdn:0.214 lr:6.0e-06 updt_s:0.058 data_s:0.060
+Training:  44%|██████████████████████▎                            | 13159/30000 [26:36<33:01,  8.50step/s]INFO 2026-05-19 01:38:29 ot_train.py:488 step:13K smpl:421K ep:705 epch:5.26 loss:0.010 grdn:0.228 lr:6.0e-06 updt_s:0.058 data_s:0.057
+Training:  44%|██████████████████████▍                            | 13179/30000 [26:39<30:43,  9.12step/s]INFO 2026-05-19 01:38:31 ot_train.py:488 step:13K smpl:422K ep:706 epch:5.27 loss:0.011 grdn:0.230 lr:6.0e-06 updt_s:0.058 data_s:0.058
+Training:  44%|██████████████████████▍                            | 13199/30000 [26:41<32:34,  8.60step/s]INFO 2026-05-19 01:38:34 ot_train.py:488 step:13K smpl:422K ep:707 epch:5.27 loss:0.010 grdn:0.241 lr:6.0e-06 updt_s:0.058 data_s:0.062
+Training:  44%|██████████████████████▍                            | 13219/30000 [26:43<33:30,  8.35step/s]INFO 2026-05-19 01:38:36 ot_train.py:488 step:13K smpl:423K ep:708 epch:5.28 loss:0.010 grdn:0.230 lr:6.0e-06 updt_s:0.058 data_s:0.060
+Training:  44%|██████████████████████▌                            | 13239/30000 [26:46<33:47,  8.27step/s]INFO 2026-05-19 01:38:38 ot_train.py:488 step:13K smpl:424K ep:709 epch:5.29 loss:0.009 grdn:0.201 lr:6.0e-06 updt_s:0.058 data_s:0.061
+Training:  44%|██████████████████████▌                            | 13259/30000 [26:48<32:19,  8.63step/s]INFO 2026-05-19 01:38:41 ot_train.py:488 step:13K smpl:424K ep:710 epch:5.30 loss:0.008 grdn:0.187 lr:5.9e-06 updt_s:0.058 data_s:0.060
+Training:  44%|██████████████████████▌                            | 13279/30000 [26:51<32:55,  8.46step/s]INFO 2026-05-19 01:38:43 ot_train.py:488 step:13K smpl:425K ep:711 epch:5.31 loss:0.009 grdn:0.217 lr:5.9e-06 updt_s:0.059 data_s:0.061
+Training:  44%|██████████████████████▌                            | 13299/30000 [26:53<33:27,  8.32step/s]INFO 2026-05-19 01:38:46 ot_train.py:488 step:13K smpl:426K ep:712 epch:5.31 loss:0.010 grdn:0.199 lr:5.9e-06 updt_s:0.059 data_s:0.061
+Training:  44%|██████████████████████▋                            | 13319/30000 [26:56<33:50,  8.22step/s]INFO 2026-05-19 01:38:48 ot_train.py:488 step:13K smpl:426K ep:713 epch:5.32 loss:0.010 grdn:0.212 lr:5.9e-06 updt_s:0.058 data_s:0.071
+Training:  44%|██████████████████████▋                            | 13339/30000 [26:58<30:38,  9.06step/s]INFO 2026-05-19 01:38:51 ot_train.py:488 step:13K smpl:427K ep:714 epch:5.33 loss:0.011 grdn:0.236 lr:5.9e-06 updt_s:0.059 data_s:0.060
+Training:  45%|██████████████████████▋                            | 13359/30000 [27:00<30:52,  8.98step/s]INFO 2026-05-19 01:38:53 ot_train.py:488 step:13K smpl:428K ep:715 epch:5.34 loss:0.011 grdn:0.233 lr:5.9e-06 updt_s:0.058 data_s:0.056
+Training:  45%|██████████████████████▋                            | 13379/30000 [27:03<33:15,  8.33step/s]INFO 2026-05-19 01:38:55 ot_train.py:488 step:13K smpl:428K ep:716 epch:5.35 loss:0.010 grdn:0.207 lr:5.9e-06 updt_s:0.058 data_s:0.062
+Training:  45%|██████████████████████▊                            | 13399/30000 [27:05<32:58,  8.39step/s]INFO 2026-05-19 01:38:58 ot_train.py:488 step:13K smpl:429K ep:717 epch:5.35 loss:0.007 grdn:0.169 lr:5.9e-06 updt_s:0.059 data_s:0.062
+Training:  45%|██████████████████████▊                            | 13419/30000 [27:08<32:46,  8.43step/s]INFO 2026-05-19 01:39:00 ot_train.py:488 step:13K smpl:429K ep:719 epch:5.36 loss:0.009 grdn:0.217 lr:5.9e-06 updt_s:0.058 data_s:0.062
+Training:  45%|██████████████████████▊                            | 13439/30000 [27:10<32:00,  8.62step/s]INFO 2026-05-19 01:39:03 ot_train.py:488 step:13K smpl:430K ep:720 epch:5.37 loss:0.010 grdn:0.216 lr:5.8e-06 updt_s:0.058 data_s:0.059
+Training:  45%|██████████████████████▉                            | 13459/30000 [27:12<30:43,  8.97step/s]INFO 2026-05-19 01:39:05 ot_train.py:488 step:13K smpl:431K ep:721 epch:5.38 loss:0.011 grdn:0.214 lr:5.8e-06 updt_s:0.059 data_s:0.057
+Training:  45%|██████████████████████▉                            | 13479/30000 [27:14<29:47,  9.24step/s]INFO 2026-05-19 01:39:07 ot_train.py:488 step:13K smpl:431K ep:722 epch:5.39 loss:0.009 grdn:0.229 lr:5.8e-06 updt_s:0.059 data_s:0.052
+Training:  45%|██████████████████████▉                            | 13499/30000 [27:17<30:05,  9.14step/s]INFO 2026-05-19 01:39:10 ot_train.py:488 step:14K smpl:432K ep:723 epch:5.39 loss:0.009 grdn:0.201 lr:5.8e-06 updt_s:0.058 data_s:0.058
+Training:  45%|██████████████████████▉                            | 13519/30000 [27:19<32:26,  8.47step/s]INFO 2026-05-19 01:39:12 ot_train.py:488 step:14K smpl:433K ep:724 epch:5.40 loss:0.009 grdn:0.203 lr:5.8e-06 updt_s:0.059 data_s:0.064
+Training:  45%|███████████████████████                            | 13539/30000 [27:22<31:09,  8.80step/s]INFO 2026-05-19 01:39:14 ot_train.py:488 step:14K smpl:433K ep:725 epch:5.41 loss:0.009 grdn:0.216 lr:5.8e-06 updt_s:0.059 data_s:0.062
+Training:  45%|███████████████████████                            | 13559/30000 [27:24<31:18,  8.75step/s]INFO 2026-05-19 01:39:17 ot_train.py:488 step:14K smpl:434K ep:726 epch:5.42 loss:0.011 grdn:0.252 lr:5.8e-06 updt_s:0.060 data_s:0.061
+Training:  45%|███████████████████████                            | 13579/30000 [27:27<30:40,  8.92step/s]INFO 2026-05-19 01:39:19 ot_train.py:488 step:14K smpl:435K ep:727 epch:5.43 loss:0.010 grdn:0.218 lr:5.8e-06 updt_s:0.059 data_s:0.062
+Training:  45%|███████████████████████                            | 13599/30000 [27:29<31:55,  8.56step/s]INFO 2026-05-19 01:39:22 ot_train.py:488 step:14K smpl:435K ep:728 epch:5.43 loss:0.010 grdn:0.244 lr:5.8e-06 updt_s:0.059 data_s:0.064
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+Training:  47%|███████████████████████▊                           | 13999/30000 [28:17<34:57,  7.63step/s]INFO 2026-05-19 01:40:10 ot_train.py:488 step:14K smpl:448K ep:750 epch:5.59 loss:0.011 grdn:0.243 lr:5.6e-06 updt_s:0.058 data_s:0.061
+Training:  47%|███████████████████████▊                           | 14019/30000 [28:20<33:12,  8.02step/s]INFO 2026-05-19 01:40:12 ot_train.py:488 step:14K smpl:449K ep:751 epch:5.60 loss:0.009 grdn:0.224 lr:5.5e-06 updt_s:0.058 data_s:0.058
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+Training:  47%|███████████████████████▉                           | 14059/30000 [28:25<35:03,  7.58step/s]INFO 2026-05-19 01:40:17 ot_train.py:488 step:14K smpl:450K ep:753 epch:5.62 loss:0.010 grdn:0.235 lr:5.5e-06 updt_s:0.059 data_s:0.062
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+Training:  47%|████████████████████████                           | 14119/30000 [28:32<34:37,  7.65step/s]INFO 2026-05-19 01:40:25 ot_train.py:488 step:14K smpl:452K ep:756 epch:5.64 loss:0.009 grdn:0.200 lr:5.5e-06 updt_s:0.059 data_s:0.063
+Training:  47%|████████████████████████                           | 14139/30000 [28:34<34:33,  7.65step/s]INFO 2026-05-19 01:40:27 ot_train.py:488 step:14K smpl:452K ep:757 epch:5.65 loss:0.009 grdn:0.186 lr:5.5e-06 updt_s:0.059 data_s:0.062
+Training:  47%|████████████████████████                           | 14159/30000 [28:37<33:08,  7.96step/s]INFO 2026-05-19 01:40:29 ot_train.py:488 step:14K smpl:453K ep:758 epch:5.66 loss:0.010 grdn:0.233 lr:5.5e-06 updt_s:0.058 data_s:0.059
+Training:  47%|████████████████████████                           | 14179/30000 [28:39<33:36,  7.84step/s]INFO 2026-05-19 01:40:32 ot_train.py:488 step:14K smpl:454K ep:759 epch:5.67 loss:0.010 grdn:0.211 lr:5.5e-06 updt_s:0.058 data_s:0.060
+Training:  47%|████████████████████████▏                          | 14199/30000 [28:41<33:32,  7.85step/s]INFO 2026-05-19 01:40:34 ot_train.py:488 step:14K smpl:454K ep:760 epch:5.67 loss:0.008 grdn:0.186 lr:5.5e-06 updt_s:0.058 data_s:0.059
+Training:  47%|████████████████████████▏                          | 14219/30000 [28:44<33:25,  7.87step/s]INFO 2026-05-19 01:40:36 ot_train.py:488 step:14K smpl:455K ep:761 epch:5.68 loss:0.009 grdn:0.198 lr:5.4e-06 updt_s:0.058 data_s:0.059
+Training:  47%|████████████████████████▏                          | 14239/30000 [28:46<33:20,  7.88step/s]INFO 2026-05-19 01:40:39 ot_train.py:488 step:14K smpl:456K ep:762 epch:5.69 loss:0.010 grdn:0.206 lr:5.4e-06 updt_s:0.058 data_s:0.061
+Training:  48%|████████████████████████▏                          | 14259/30000 [28:49<34:04,  7.70step/s]INFO 2026-05-19 01:40:41 ot_train.py:488 step:14K smpl:456K ep:764 epch:5.70 loss:0.010 grdn:0.198 lr:5.4e-06 updt_s:0.060 data_s:0.060
+Training:  48%|████████████████████████▎                          | 14279/30000 [28:51<33:36,  7.80step/s]INFO 2026-05-19 01:40:44 ot_train.py:488 step:14K smpl:457K ep:765 epch:5.71 loss:0.009 grdn:0.219 lr:5.4e-06 updt_s:0.058 data_s:0.062
+Training:  48%|████████████████████████▎                          | 14299/30000 [28:53<33:07,  7.90step/s]INFO 2026-05-19 01:40:46 ot_train.py:488 step:14K smpl:458K ep:766 epch:5.71 loss:0.008 grdn:0.200 lr:5.4e-06 updt_s:0.059 data_s:0.059
+Training:  48%|████████████████████████▎                          | 14319/30000 [28:56<34:07,  7.66step/s]INFO 2026-05-19 01:40:48 ot_train.py:488 step:14K smpl:458K ep:767 epch:5.72 loss:0.009 grdn:0.209 lr:5.4e-06 updt_s:0.059 data_s:0.058
+Training:  48%|████████████████████████▍                          | 14339/30000 [28:58<34:21,  7.60step/s]INFO 2026-05-19 01:40:51 ot_train.py:488 step:14K smpl:459K ep:768 epch:5.73 loss:0.010 grdn:0.232 lr:5.4e-06 updt_s:0.059 data_s:0.061
+Training:  48%|████████████████████████▍                          | 14359/30000 [29:01<34:44,  7.50step/s]INFO 2026-05-19 01:40:53 ot_train.py:488 step:14K smpl:460K ep:769 epch:5.74 loss:0.010 grdn:0.217 lr:5.4e-06 updt_s:0.061 data_s:0.061
+Training:  48%|████████████████████████▍                          | 14379/30000 [29:03<33:41,  7.73step/s]INFO 2026-05-19 01:40:56 ot_train.py:488 step:14K smpl:460K ep:770 epch:5.75 loss:0.010 grdn:0.220 lr:5.4e-06 updt_s:0.058 data_s:0.059
+Training:  48%|████████████████████████▍                          | 14399/30000 [29:05<33:10,  7.84step/s]INFO 2026-05-19 01:40:58 ot_train.py:488 step:14K smpl:461K ep:771 epch:5.75 loss:0.009 grdn:0.218 lr:5.3e-06 updt_s:0.059 data_s:0.059
+Training:  48%|████████████████████████▌                          | 14419/30000 [29:08<32:13,  8.06step/s]INFO 2026-05-19 01:41:00 ot_train.py:488 step:14K smpl:461K ep:772 epch:5.76 loss:0.009 grdn:0.202 lr:5.3e-06 updt_s:0.058 data_s:0.058
+Training:  48%|████████████████████████▌                          | 14439/30000 [29:10<33:23,  7.77step/s]INFO 2026-05-19 01:41:03 ot_train.py:488 step:14K smpl:462K ep:773 epch:5.77 loss:0.009 grdn:0.210 lr:5.3e-06 updt_s:0.059 data_s:0.061
+Training:  48%|████████████████████████▌                          | 14459/30000 [29:13<33:43,  7.68step/s]INFO 2026-05-19 01:41:05 ot_train.py:488 step:14K smpl:463K ep:774 epch:5.78 loss:0.010 grdn:0.216 lr:5.3e-06 updt_s:0.059 data_s:0.063
+Training:  48%|████████████████████████▌                          | 14479/30000 [29:15<34:06,  7.58step/s]INFO 2026-05-19 01:41:08 ot_train.py:488 step:14K smpl:463K ep:775 epch:5.79 loss:0.012 grdn:0.237 lr:5.3e-06 updt_s:0.059 data_s:0.061
+Training:  48%|████████████████████████▋                          | 14499/30000 [29:17<33:32,  7.70step/s]INFO 2026-05-19 01:41:10 ot_train.py:488 step:14K smpl:464K ep:776 epch:5.79 loss:0.009 grdn:0.198 lr:5.3e-06 updt_s:0.059 data_s:0.059
+Training:  48%|████████████████████████▋                          | 14519/30000 [29:20<33:29,  7.70step/s]INFO 2026-05-19 01:41:12 ot_train.py:488 step:15K smpl:465K ep:777 epch:5.80 loss:0.010 grdn:0.219 lr:5.3e-06 updt_s:0.058 data_s:0.060
+Training:  48%|████████████████████████▋                          | 14539/30000 [29:22<34:00,  7.58step/s]INFO 2026-05-19 01:41:15 ot_train.py:488 step:15K smpl:465K ep:778 epch:5.81 loss:0.009 grdn:0.211 lr:5.3e-06 updt_s:0.058 data_s:0.061
+Training:  49%|████████████████████████▊                          | 14559/30000 [29:24<33:21,  7.71step/s]INFO 2026-05-19 01:41:17 ot_train.py:488 step:15K smpl:466K ep:780 epch:5.82 loss:0.008 grdn:0.188 lr:5.3e-06 updt_s:0.058 data_s:0.059
+Training:  49%|████████████████████████▊                          | 14579/30000 [29:27<32:11,  7.98step/s]INFO 2026-05-19 01:41:19 ot_train.py:488 step:15K smpl:467K ep:781 epch:5.83 loss:0.009 grdn:0.224 lr:5.3e-06 updt_s:0.059 data_s:0.059
+Training:  49%|████████████████████████▊                          | 14599/30000 [29:29<33:40,  7.62step/s]INFO 2026-05-19 01:41:22 ot_train.py:488 step:15K smpl:467K ep:782 epch:5.83 loss:0.010 grdn:0.243 lr:5.2e-06 updt_s:0.059 data_s:0.059
+Training:  49%|████████████████████████▊                          | 14619/30000 [29:32<34:54,  7.34step/s]INFO 2026-05-19 01:41:24 ot_train.py:488 step:15K smpl:468K ep:783 epch:5.84 loss:0.008 grdn:0.202 lr:5.2e-06 updt_s:0.060 data_s:0.059
+Training:  49%|████████████████████████▉                          | 14639/30000 [29:34<36:14,  7.06step/s]INFO 2026-05-19 01:41:27 ot_train.py:488 step:15K smpl:468K ep:784 epch:5.85 loss:0.010 grdn:0.222 lr:5.2e-06 updt_s:0.057 data_s:0.069
+Training:  49%|████████████████████████▉                          | 14659/30000 [29:36<33:04,  7.73step/s]INFO 2026-05-19 01:41:29 ot_train.py:488 step:15K smpl:469K ep:785 epch:5.86 loss:0.009 grdn:0.247 lr:5.2e-06 updt_s:0.060 data_s:0.059
+Training:  49%|████████████████████████▉                          | 14679/30000 [29:39<32:57,  7.75step/s]INFO 2026-05-19 01:41:32 ot_train.py:488 step:15K smpl:470K ep:786 epch:5.87 loss:0.009 grdn:0.192 lr:5.2e-06 updt_s:0.060 data_s:0.060
+Training:  49%|████████████████████████▉                          | 14699/30000 [29:41<33:10,  7.69step/s]INFO 2026-05-19 01:41:34 ot_train.py:488 step:15K smpl:470K ep:787 epch:5.87 loss:0.008 grdn:0.205 lr:5.2e-06 updt_s:0.061 data_s:0.059
+Training:  49%|█████████████████████████                          | 14719/30000 [29:44<33:29,  7.60step/s]INFO 2026-05-19 01:41:36 ot_train.py:488 step:15K smpl:471K ep:788 epch:5.88 loss:0.009 grdn:0.208 lr:5.2e-06 updt_s:0.061 data_s:0.061
+Training:  49%|█████████████████████████                          | 14739/30000 [29:46<33:42,  7.55step/s]INFO 2026-05-19 01:41:39 ot_train.py:488 step:15K smpl:472K ep:789 epch:5.89 loss:0.007 grdn:0.180 lr:5.2e-06 updt_s:0.069 data_s:0.052
+Training:  49%|█████████████████████████                          | 14759/30000 [29:49<32:35,  7.79step/s]INFO 2026-05-19 01:41:41 ot_train.py:488 step:15K smpl:472K ep:790 epch:5.90 loss:0.010 grdn:0.212 lr:5.2e-06 updt_s:0.060 data_s:0.061
+Training:  49%|█████████████████████████                          | 14779/30000 [29:51<32:29,  7.81step/s]INFO 2026-05-19 01:41:44 ot_train.py:488 step:15K smpl:473K ep:791 epch:5.91 loss:0.009 grdn:0.212 lr:5.1e-06 updt_s:0.059 data_s:0.061
+Training:  49%|█████████████████████████▏                         | 14799/30000 [29:53<31:58,  7.92step/s]INFO 2026-05-19 01:41:46 ot_train.py:488 step:15K smpl:474K ep:792 epch:5.91 loss:0.010 grdn:0.238 lr:5.1e-06 updt_s:0.058 data_s:0.058
+Training:  49%|█████████████████████████▏                         | 14819/30000 [29:56<32:55,  7.68step/s]INFO 2026-05-19 01:41:49 ot_train.py:488 step:15K smpl:474K ep:793 epch:5.92 loss:0.009 grdn:0.198 lr:5.1e-06 updt_s:0.058 data_s:0.060
+Training:  49%|█████████████████████████▏                         | 14839/30000 [29:58<30:40,  8.24step/s]INFO 2026-05-19 01:41:51 ot_train.py:488 step:15K smpl:475K ep:795 epch:5.93 loss:0.009 grdn:0.203 lr:5.1e-06 updt_s:0.058 data_s:0.059
+Training:  50%|█████████████████████████▎                         | 14859/30000 [30:01<29:10,  8.65step/s]INFO 2026-05-19 01:41:54 ot_train.py:488 step:15K smpl:476K ep:796 epch:5.94 loss:0.010 grdn:0.269 lr:5.1e-06 updt_s:0.060 data_s:0.085
+Training:  50%|█████████████████████████▎                         | 14879/30000 [30:03<28:44,  8.77step/s]INFO 2026-05-19 01:41:56 ot_train.py:488 step:15K smpl:476K ep:797 epch:5.95 loss:0.008 grdn:0.234 lr:5.1e-06 updt_s:0.059 data_s:0.051
+Training:  50%|█████████████████████████▎                         | 14899/30000 [30:06<28:09,  8.94step/s]INFO 2026-05-19 01:41:58 ot_train.py:488 step:15K smpl:477K ep:798 epch:5.95 loss:0.009 grdn:0.211 lr:5.1e-06 updt_s:0.060 data_s:0.063
+Training:  50%|█████████████████████████▎                         | 14919/30000 [30:08<28:08,  8.93step/s]INFO 2026-05-19 01:42:01 ot_train.py:488 step:15K smpl:477K ep:799 epch:5.96 loss:0.009 grdn:0.236 lr:5.1e-06 updt_s:0.058 data_s:0.065
+Training:  50%|█████████████████████████▍                         | 14939/30000 [30:11<28:26,  8.83step/s]INFO 2026-05-19 01:42:03 ot_train.py:488 step:15K smpl:478K ep:800 epch:5.97 loss:0.009 grdn:0.219 lr:5.1e-06 updt_s:0.059 data_s:0.063
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+Training:  50%|█████████████████████████▍                         | 14979/30000 [30:16<28:08,  8.90step/s]INFO 2026-05-19 01:42:08 ot_train.py:488 step:15K smpl:479K ep:802 epch:5.99 loss:0.010 grdn:0.209 lr:5.0e-06 updt_s:0.058 data_s:0.062
+Training:  50%|█████████████████████████▍                         | 14999/30000 [30:18<27:20,  9.14step/s]INFO 2026-05-19 01:42:11 ot_train.py:488 step:15K smpl:480K ep:803 epch:5.99 loss:0.010 grdn:0.220 lr:5.0e-06 updt_s:0.058 data_s:0.063
+INFO 2026-05-19 01:42:11 ot_train.py:502 Checkpoint policy after step 15000
+Training:  50%|█████████████████████████▌                         | 15019/30000 [30:26<27:14,  9.16step/s]INFO 2026-05-19 01:42:19 ot_train.py:488 step:15K smpl:481K ep:804 epch:6.00 loss:0.008 grdn:0.206 lr:5.0e-06 updt_s:0.061 data_s:0.006
+Training:  50%|█████████████████████████▌                         | 15039/30000 [30:28<27:34,  9.04step/s]INFO 2026-05-19 01:42:21 ot_train.py:488 step:15K smpl:481K ep:805 epch:6.01 loss:0.008 grdn:0.198 lr:5.0e-06 updt_s:0.059 data_s:0.047
+Training:  50%|█████████████████████████▌                         | 15059/30000 [30:31<32:23,  7.69step/s]INFO 2026-05-19 01:42:24 ot_train.py:488 step:15K smpl:482K ep:806 epch:6.02 loss:0.009 grdn:0.209 lr:5.0e-06 updt_s:0.058 data_s:0.070
+Training:  50%|█████████████████████████▋                         | 15079/30000 [30:33<30:25,  8.18step/s]INFO 2026-05-19 01:42:26 ot_train.py:488 step:15K smpl:483K ep:807 epch:6.03 loss:0.010 grdn:0.227 lr:5.0e-06 updt_s:0.058 data_s:0.065
+Training:  50%|█████████████████████████▋                         | 15099/30000 [30:36<29:27,  8.43step/s]INFO 2026-05-19 01:42:29 ot_train.py:488 step:15K smpl:483K ep:808 epch:6.03 loss:0.010 grdn:0.220 lr:5.0e-06 updt_s:0.060 data_s:0.061
+Training:  50%|█████████████████████████▋                         | 15119/30000 [30:38<29:34,  8.38step/s]INFO 2026-05-19 01:42:31 ot_train.py:488 step:15K smpl:484K ep:810 epch:6.04 loss:0.008 grdn:0.194 lr:5.0e-06 updt_s:0.058 data_s:0.067
+Training:  50%|█████████████████████████▋                         | 15139/30000 [30:41<28:18,  8.75step/s]INFO 2026-05-19 01:42:34 ot_train.py:488 step:15K smpl:484K ep:811 epch:6.05 loss:0.009 grdn:0.198 lr:5.0e-06 updt_s:0.059 data_s:0.063
+Training:  51%|█████████████████████████▊                         | 15159/30000 [30:43<27:46,  8.91step/s]INFO 2026-05-19 01:42:36 ot_train.py:488 step:15K smpl:485K ep:812 epch:6.06 loss:0.009 grdn:0.210 lr:4.9e-06 updt_s:0.059 data_s:0.067
+Training:  51%|█████████████████████████▊                         | 15179/30000 [30:46<27:41,  8.92step/s]INFO 2026-05-19 01:42:39 ot_train.py:488 step:15K smpl:486K ep:813 epch:6.07 loss:0.011 grdn:0.212 lr:4.9e-06 updt_s:0.060 data_s:0.062
+Training:  51%|█████████████████████████▊                         | 15199/30000 [30:48<28:19,  8.71step/s]INFO 2026-05-19 01:42:41 ot_train.py:488 step:15K smpl:486K ep:814 epch:6.07 loss:0.010 grdn:0.218 lr:4.9e-06 updt_s:0.060 data_s:0.062
+Training:  51%|█████████████████████████▊                         | 15219/30000 [30:51<27:59,  8.80step/s]INFO 2026-05-19 01:42:43 ot_train.py:488 step:15K smpl:487K ep:815 epch:6.08 loss:0.008 grdn:0.203 lr:4.9e-06 updt_s:0.060 data_s:0.062
+Training:  51%|█████████████████████████▉                         | 15239/30000 [30:53<27:54,  8.82step/s]INFO 2026-05-19 01:42:46 ot_train.py:488 step:15K smpl:488K ep:816 epch:6.09 loss:0.009 grdn:0.212 lr:4.9e-06 updt_s:0.058 data_s:0.064
+Training:  51%|█████████████████████████▉                         | 15259/30000 [30:56<28:56,  8.49step/s]INFO 2026-05-19 01:42:48 ot_train.py:488 step:15K smpl:488K ep:817 epch:6.10 loss:0.010 grdn:0.203 lr:4.9e-06 updt_s:0.059 data_s:0.065
+Training:  51%|█████████████████████████▉                         | 15279/30000 [30:58<29:56,  8.20step/s]INFO 2026-05-19 01:42:51 ot_train.py:488 step:15K smpl:489K ep:818 epch:6.11 loss:0.010 grdn:0.214 lr:4.9e-06 updt_s:0.060 data_s:0.074
+Training:  51%|██████████████████████████                         | 15299/30000 [31:01<29:27,  8.32step/s]INFO 2026-05-19 01:42:54 ot_train.py:488 step:15K smpl:490K ep:819 epch:6.11 loss:0.008 grdn:0.202 lr:4.9e-06 updt_s:0.059 data_s:0.063
+Training:  51%|██████████████████████████                         | 15319/30000 [31:03<28:59,  8.44step/s]INFO 2026-05-19 01:42:56 ot_train.py:488 step:15K smpl:490K ep:820 epch:6.12 loss:0.010 grdn:0.250 lr:4.9e-06 updt_s:0.059 data_s:0.062
+Training:  51%|██████████████████████████                         | 15339/30000 [31:06<28:35,  8.54step/s]INFO 2026-05-19 01:42:59 ot_train.py:488 step:15K smpl:491K ep:821 epch:6.13 loss:0.009 grdn:0.215 lr:4.9e-06 updt_s:0.060 data_s:0.069
+Training:  51%|██████████████████████████                         | 15359/30000 [31:08<28:07,  8.68step/s]INFO 2026-05-19 01:43:01 ot_train.py:488 step:15K smpl:492K ep:822 epch:6.14 loss:0.011 grdn:0.241 lr:4.8e-06 updt_s:0.061 data_s:0.064
+Training:  51%|██████████████████████████▏                        | 15379/30000 [31:11<27:11,  8.96step/s]INFO 2026-05-19 01:43:04 ot_train.py:488 step:15K smpl:492K ep:823 epch:6.15 loss:0.009 grdn:0.228 lr:4.8e-06 updt_s:0.059 data_s:0.060
+Training:  51%|██████████████████████████▏                        | 15399/30000 [31:13<26:55,  9.04step/s]INFO 2026-05-19 01:43:06 ot_train.py:488 step:15K smpl:493K ep:825 epch:6.15 loss:0.008 grdn:0.202 lr:4.8e-06 updt_s:0.059 data_s:0.061
+Training:  51%|██████████████████████████▏                        | 15419/30000 [31:16<26:55,  9.02step/s]INFO 2026-05-19 01:43:08 ot_train.py:488 step:15K smpl:493K ep:826 epch:6.16 loss:0.011 grdn:0.226 lr:4.8e-06 updt_s:0.058 data_s:0.064
+Training:  51%|██████████████████████████▏                        | 15439/30000 [31:18<27:23,  8.86step/s]INFO 2026-05-19 01:43:11 ot_train.py:488 step:15K smpl:494K ep:827 epch:6.17 loss:0.010 grdn:0.249 lr:4.8e-06 updt_s:0.059 data_s:0.061
+Training:  52%|██████████████████████████▎                        | 15459/30000 [31:21<26:48,  9.04step/s]INFO 2026-05-19 01:43:13 ot_train.py:488 step:15K smpl:495K ep:828 epch:6.18 loss:0.009 grdn:0.216 lr:4.8e-06 updt_s:0.061 data_s:0.060
+Training:  52%|██████████████████████████▎                        | 15479/30000 [31:23<27:31,  8.79step/s]INFO 2026-05-19 01:43:16 ot_train.py:488 step:15K smpl:495K ep:829 epch:6.19 loss:0.010 grdn:0.233 lr:4.8e-06 updt_s:0.061 data_s:0.060
+Training:  52%|██████████████████████████▎                        | 15499/30000 [31:25<27:24,  8.82step/s]INFO 2026-05-19 01:43:18 ot_train.py:488 step:16K smpl:496K ep:830 epch:6.19 loss:0.011 grdn:0.241 lr:4.8e-06 updt_s:0.062 data_s:0.059
+Training:  52%|██████████████████████████▍                        | 15519/30000 [31:28<27:23,  8.81step/s]INFO 2026-05-19 01:43:21 ot_train.py:488 step:16K smpl:497K ep:831 epch:6.20 loss:0.010 grdn:0.244 lr:4.8e-06 updt_s:0.060 data_s:0.071
+Training:  52%|██████████████████████████▍                        | 15539/30000 [31:31<26:39,  9.04step/s]INFO 2026-05-19 01:43:23 ot_train.py:488 step:16K smpl:497K ep:832 epch:6.21 loss:0.010 grdn:0.227 lr:4.7e-06 updt_s:0.058 data_s:0.062
+Training:  52%|██████████████████████████▍                        | 15559/30000 [31:33<26:39,  9.03step/s]INFO 2026-05-19 01:43:26 ot_train.py:488 step:16K smpl:498K ep:833 epch:6.22 loss:0.011 grdn:0.218 lr:4.7e-06 updt_s:0.059 data_s:0.061
+Training:  52%|██████████████████████████▍                        | 15579/30000 [31:35<27:32,  8.73step/s]INFO 2026-05-19 01:43:28 ot_train.py:488 step:16K smpl:499K ep:834 epch:6.23 loss:0.007 grdn:0.192 lr:4.7e-06 updt_s:0.059 data_s:0.064
+Training:  52%|██████████████████████████▌                        | 15599/30000 [31:38<27:07,  8.85step/s]INFO 2026-05-19 01:43:31 ot_train.py:488 step:16K smpl:499K ep:835 epch:6.23 loss:0.010 grdn:0.225 lr:4.7e-06 updt_s:0.059 data_s:0.062
+Training:  52%|██████████████████████████▌                        | 15619/30000 [31:40<27:18,  8.78step/s]INFO 2026-05-19 01:43:33 ot_train.py:488 step:16K smpl:500K ep:836 epch:6.24 loss:0.009 grdn:0.204 lr:4.7e-06 updt_s:0.059 data_s:0.062
+Training:  52%|██████████████████████████▌                        | 15639/30000 [31:43<27:12,  8.80step/s]INFO 2026-05-19 01:43:35 ot_train.py:488 step:16K smpl:500K ep:837 epch:6.25 loss:0.008 grdn:0.198 lr:4.7e-06 updt_s:0.060 data_s:0.062
+Training:  52%|██████████████████████████▌                        | 15659/30000 [31:45<27:02,  8.84step/s]INFO 2026-05-19 01:43:38 ot_train.py:488 step:16K smpl:501K ep:838 epch:6.26 loss:0.010 grdn:0.226 lr:4.7e-06 updt_s:0.061 data_s:0.063
+Training:  52%|██████████████████████████▋                        | 15679/30000 [31:48<26:55,  8.86step/s]INFO 2026-05-19 01:43:40 ot_train.py:488 step:16K smpl:502K ep:840 epch:6.27 loss:0.009 grdn:0.219 lr:4.7e-06 updt_s:0.058 data_s:0.060
+Training:  52%|██████████████████████████▋                        | 15699/30000 [31:50<26:55,  8.85step/s]INFO 2026-05-19 01:43:43 ot_train.py:488 step:16K smpl:502K ep:841 epch:6.27 loss:0.009 grdn:0.204 lr:4.7e-06 updt_s:0.058 data_s:0.062
+Training:  52%|██████████████████████████▋                        | 15719/30000 [31:52<26:06,  9.12step/s]INFO 2026-05-19 01:43:45 ot_train.py:488 step:16K smpl:503K ep:842 epch:6.28 loss:0.009 grdn:0.189 lr:4.7e-06 updt_s:0.058 data_s:0.060
+Training:  52%|██████████████████████████▊                        | 15739/30000 [31:55<26:14,  9.06step/s]INFO 2026-05-19 01:43:48 ot_train.py:488 step:16K smpl:504K ep:843 epch:6.29 loss:0.008 grdn:0.214 lr:4.6e-06 updt_s:0.059 data_s:0.061
+Training:  53%|██████████████████████████▊                        | 15759/30000 [31:57<26:23,  9.00step/s]INFO 2026-05-19 01:43:50 ot_train.py:488 step:16K smpl:504K ep:844 epch:6.30 loss:0.009 grdn:0.205 lr:4.6e-06 updt_s:0.058 data_s:0.063
+Training:  53%|██████████████████████████▊                        | 15779/30000 [32:00<24:46,  9.57step/s]INFO 2026-05-19 01:43:52 ot_train.py:488 step:16K smpl:505K ep:845 epch:6.31 loss:0.010 grdn:0.209 lr:4.6e-06 updt_s:0.059 data_s:0.057
+Training:  53%|██████████████████████████▊                        | 15799/30000 [32:02<26:08,  9.05step/s]INFO 2026-05-19 01:43:55 ot_train.py:488 step:16K smpl:506K ep:846 epch:6.31 loss:0.009 grdn:0.200 lr:4.6e-06 updt_s:0.058 data_s:0.060
+Training:  53%|██████████████████████████▉                        | 15819/30000 [32:04<25:28,  9.28step/s]INFO 2026-05-19 01:43:57 ot_train.py:488 step:16K smpl:506K ep:847 epch:6.32 loss:0.008 grdn:0.194 lr:4.6e-06 updt_s:0.059 data_s:0.057
+Training:  53%|██████████████████████████▉                        | 15839/30000 [32:07<26:03,  9.06step/s]INFO 2026-05-19 01:44:00 ot_train.py:488 step:16K smpl:507K ep:848 epch:6.33 loss:0.009 grdn:0.215 lr:4.6e-06 updt_s:0.059 data_s:0.063
+Training:  53%|██████████████████████████▉                        | 15859/30000 [32:09<26:57,  8.74step/s]INFO 2026-05-19 01:44:02 ot_train.py:488 step:16K smpl:508K ep:849 epch:6.34 loss:0.009 grdn:0.213 lr:4.6e-06 updt_s:0.058 data_s:0.064
+Training:  53%|██████████████████████████▉                        | 15879/30000 [32:12<27:21,  8.60step/s]INFO 2026-05-19 01:44:04 ot_train.py:488 step:16K smpl:508K ep:850 epch:6.35 loss:0.012 grdn:0.237 lr:4.6e-06 updt_s:0.058 data_s:0.065
+Training:  53%|███████████████████████████                        | 15899/30000 [32:14<25:03,  9.38step/s]INFO 2026-05-19 01:44:07 ot_train.py:488 step:16K smpl:509K ep:851 epch:6.35 loss:0.010 grdn:0.239 lr:4.6e-06 updt_s:0.059 data_s:0.059
+Training:  53%|███████████████████████████                        | 15919/30000 [32:17<27:20,  8.58step/s]INFO 2026-05-19 01:44:09 ot_train.py:488 step:16K smpl:509K ep:852 epch:6.36 loss:0.010 grdn:0.247 lr:4.5e-06 updt_s:0.060 data_s:0.064
+Training:  53%|███████████████████████████                        | 15939/30000 [32:19<27:40,  8.47step/s]INFO 2026-05-19 01:44:12 ot_train.py:488 step:16K smpl:510K ep:853 epch:6.37 loss:0.010 grdn:0.208 lr:4.5e-06 updt_s:0.058 data_s:0.060
+Training:  53%|███████████████████████████▏                       | 15959/30000 [32:21<29:30,  7.93step/s]INFO 2026-05-19 01:44:14 ot_train.py:488 step:16K smpl:511K ep:855 epch:6.38 loss:0.011 grdn:0.228 lr:4.5e-06 updt_s:0.058 data_s:0.063
+Training:  53%|███████████████████████████▏                       | 15979/30000 [32:24<27:59,  8.35step/s]INFO 2026-05-19 01:44:17 ot_train.py:488 step:16K smpl:511K ep:856 epch:6.39 loss:0.010 grdn:0.239 lr:4.5e-06 updt_s:0.058 data_s:0.061
+Training:  53%|███████████████████████████▏                       | 15999/30000 [32:26<28:50,  8.09step/s]INFO 2026-05-19 01:44:19 ot_train.py:488 step:16K smpl:512K ep:857 epch:6.39 loss:0.010 grdn:0.216 lr:4.5e-06 updt_s:0.058 data_s:0.065
+Training:  53%|███████████████████████████▏                       | 16019/30000 [32:29<28:27,  8.19step/s]INFO 2026-05-19 01:44:21 ot_train.py:488 step:16K smpl:513K ep:858 epch:6.40 loss:0.009 grdn:0.214 lr:4.5e-06 updt_s:0.059 data_s:0.061
+Training:  53%|███████████████████████████▎                       | 16039/30000 [32:31<27:41,  8.40step/s]INFO 2026-05-19 01:44:24 ot_train.py:488 step:16K smpl:513K ep:859 epch:6.41 loss:0.009 grdn:0.223 lr:4.5e-06 updt_s:0.059 data_s:0.062
+Training:  54%|███████████████████████████▎                       | 16059/30000 [32:34<27:20,  8.50step/s]INFO 2026-05-19 01:44:26 ot_train.py:488 step:16K smpl:514K ep:860 epch:6.42 loss:0.010 grdn:0.196 lr:4.5e-06 updt_s:0.059 data_s:0.073
+Training:  54%|███████████████████████████▎                       | 16079/30000 [32:36<25:10,  9.21step/s]INFO 2026-05-19 01:44:29 ot_train.py:488 step:16K smpl:515K ep:861 epch:6.43 loss:0.010 grdn:0.219 lr:4.5e-06 updt_s:0.059 data_s:0.061
+Training:  54%|███████████████████████████▎                       | 16099/30000 [32:39<26:00,  8.91step/s]INFO 2026-05-19 01:44:31 ot_train.py:488 step:16K smpl:515K ep:862 epch:6.43 loss:0.011 grdn:0.256 lr:4.5e-06 updt_s:0.059 data_s:0.061
+Training:  54%|███████████████████████████▍                       | 16119/30000 [32:41<25:07,  9.21step/s]INFO 2026-05-19 01:44:34 ot_train.py:488 step:16K smpl:516K ep:863 epch:6.44 loss:0.009 grdn:0.206 lr:4.4e-06 updt_s:0.058 data_s:0.061
+Training:  54%|███████████████████████████▍                       | 16139/30000 [32:43<25:32,  9.04step/s]INFO 2026-05-19 01:44:36 ot_train.py:488 step:16K smpl:516K ep:864 epch:6.45 loss:0.009 grdn:0.205 lr:4.4e-06 updt_s:0.058 data_s:0.061
+Training:  54%|███████████████████████████▍                       | 16159/30000 [32:46<26:50,  8.59step/s]INFO 2026-05-19 01:44:38 ot_train.py:488 step:16K smpl:517K ep:865 epch:6.46 loss:0.008 grdn:0.200 lr:4.4e-06 updt_s:0.058 data_s:0.063
+Training:  54%|███████████████████████████▌                       | 16179/30000 [32:48<25:36,  8.99step/s]INFO 2026-05-19 01:44:41 ot_train.py:488 step:16K smpl:518K ep:866 epch:6.46 loss:0.009 grdn:0.209 lr:4.4e-06 updt_s:0.059 data_s:0.059
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+Training:  57%|████████████████████████████▊                      | 16959/30000 [34:21<23:11,  9.37step/s]INFO 2026-05-19 01:46:14 ot_train.py:488 step:17K smpl:543K ep:908 epch:6.78 loss:0.010 grdn:0.232 lr:4.0e-06 updt_s:0.059 data_s:0.060
+Training:  57%|████████████████████████████▊                      | 16979/30000 [34:24<23:39,  9.17step/s]INFO 2026-05-19 01:46:17 ot_train.py:488 step:17K smpl:543K ep:909 epch:6.78 loss:0.008 grdn:0.196 lr:4.0e-06 updt_s:0.059 data_s:0.060
+Training:  57%|████████████████████████████▉                      | 16999/30000 [34:26<23:51,  9.08step/s]INFO 2026-05-19 01:46:19 ot_train.py:488 step:17K smpl:544K ep:910 epch:6.79 loss:0.009 grdn:0.196 lr:4.0e-06 updt_s:0.059 data_s:0.062
+Training:  57%|████████████████████████████▉                      | 17019/30000 [34:29<24:24,  8.86step/s]INFO 2026-05-19 01:46:22 ot_train.py:488 step:17K smpl:545K ep:911 epch:6.80 loss:0.011 grdn:0.219 lr:4.0e-06 updt_s:0.060 data_s:0.068
+Training:  57%|████████████████████████████▉                      | 17039/30000 [34:31<23:08,  9.34step/s]INFO 2026-05-19 01:46:24 ot_train.py:488 step:17K smpl:545K ep:912 epch:6.81 loss:0.009 grdn:0.185 lr:4.0e-06 updt_s:0.060 data_s:0.056
+Training:  57%|█████████████████████████████                      | 17059/30000 [34:34<24:00,  8.98step/s]INFO 2026-05-19 01:46:26 ot_train.py:488 step:17K smpl:546K ep:913 epch:6.82 loss:0.007 grdn:0.194 lr:4.0e-06 updt_s:0.060 data_s:0.060
+Training:  57%|█████████████████████████████                      | 17079/30000 [34:36<23:17,  9.24step/s]INFO 2026-05-19 01:46:29 ot_train.py:488 step:17K smpl:547K ep:914 epch:6.82 loss:0.008 grdn:0.196 lr:3.9e-06 updt_s:0.059 data_s:0.059
+Training:  57%|█████████████████████████████                      | 17099/30000 [34:38<23:43,  9.06step/s]INFO 2026-05-19 01:46:31 ot_train.py:488 step:17K smpl:547K ep:916 epch:6.83 loss:0.011 grdn:0.229 lr:3.9e-06 updt_s:0.060 data_s:0.059
+Training:  57%|█████████████████████████████                      | 17119/30000 [34:41<24:19,  8.83step/s]INFO 2026-05-19 01:46:34 ot_train.py:488 step:17K smpl:548K ep:917 epch:6.84 loss:0.009 grdn:0.224 lr:3.9e-06 updt_s:0.061 data_s:0.058
+Training:  57%|█████████████████████████████▏                     | 17139/30000 [34:43<23:44,  9.03step/s]INFO 2026-05-19 01:46:36 ot_train.py:488 step:17K smpl:548K ep:918 epch:6.85 loss:0.011 grdn:0.222 lr:3.9e-06 updt_s:0.061 data_s:0.057
+Training:  57%|█████████████████████████████▏                     | 17159/30000 [34:46<23:02,  9.29step/s]INFO 2026-05-19 01:46:38 ot_train.py:488 step:17K smpl:549K ep:919 epch:6.86 loss:0.008 grdn:0.197 lr:3.9e-06 updt_s:0.060 data_s:0.059
+Training:  57%|█████████████████████████████▏                     | 17179/30000 [34:48<23:16,  9.18step/s]INFO 2026-05-19 01:46:41 ot_train.py:488 step:17K smpl:550K ep:920 epch:6.86 loss:0.009 grdn:0.220 lr:3.9e-06 updt_s:0.060 data_s:0.057
+Training:  57%|█████████████████████████████▏                     | 17199/30000 [34:50<23:42,  9.00step/s]INFO 2026-05-19 01:46:43 ot_train.py:488 step:17K smpl:550K ep:921 epch:6.87 loss:0.009 grdn:0.222 lr:3.9e-06 updt_s:0.059 data_s:0.061
+Training:  57%|█████████████████████████████▎                     | 17219/30000 [34:53<23:32,  9.05step/s]INFO 2026-05-19 01:46:45 ot_train.py:488 step:17K smpl:551K ep:922 epch:6.88 loss:0.011 grdn:0.215 lr:3.9e-06 updt_s:0.060 data_s:0.056
+Training:  57%|█████████████████████████████▎                     | 17239/30000 [34:55<23:31,  9.04step/s]INFO 2026-05-19 01:46:48 ot_train.py:488 step:17K smpl:552K ep:923 epch:6.89 loss:0.010 grdn:0.214 lr:3.9e-06 updt_s:0.061 data_s:0.060
+Training:  58%|█████████████████████████████▎                     | 17259/30000 [34:58<23:15,  9.13step/s]INFO 2026-05-19 01:46:50 ot_train.py:488 step:17K smpl:552K ep:924 epch:6.90 loss:0.009 grdn:0.210 lr:3.9e-06 updt_s:0.058 data_s:0.060
+Training:  58%|█████████████████████████████▎                     | 17279/30000 [35:00<23:14,  9.12step/s]INFO 2026-05-19 01:46:53 ot_train.py:488 step:17K smpl:553K ep:925 epch:6.90 loss:0.010 grdn:0.241 lr:3.8e-06 updt_s:0.060 data_s:0.059
+Training:  58%|█████████████████████████████▍                     | 17299/30000 [35:02<23:06,  9.16step/s]INFO 2026-05-19 01:46:55 ot_train.py:488 step:17K smpl:554K ep:926 epch:6.91 loss:0.008 grdn:0.185 lr:3.8e-06 updt_s:0.060 data_s:0.059
+Training:  58%|█████████████████████████████▍                     | 17319/30000 [35:05<36:22,  5.81step/s]INFO 2026-05-19 01:46:58 ot_train.py:488 step:17K smpl:554K ep:927 epch:6.92 loss:0.010 grdn:0.227 lr:3.8e-06 updt_s:0.058 data_s:0.074
+Training:  58%|█████████████████████████████▍                     | 17339/30000 [35:07<27:37,  7.64step/s]INFO 2026-05-19 01:47:00 ot_train.py:488 step:17K smpl:555K ep:928 epch:6.93 loss:0.010 grdn:0.225 lr:3.8e-06 updt_s:0.059 data_s:0.060
+Training:  58%|█████████████████████████████▌                     | 17359/30000 [35:10<27:39,  7.62step/s]INFO 2026-05-19 01:47:03 ot_train.py:488 step:17K smpl:556K ep:929 epch:6.94 loss:0.008 grdn:0.199 lr:3.8e-06 updt_s:0.059 data_s:0.061
+Training:  58%|█████████████████████████████▌                     | 17379/30000 [35:12<27:49,  7.56step/s]INFO 2026-05-19 01:47:05 ot_train.py:488 step:17K smpl:556K ep:931 epch:6.94 loss:0.009 grdn:0.206 lr:3.8e-06 updt_s:0.059 data_s:0.061
+Training:  58%|█████████████████████████████▌                     | 17399/30000 [35:15<28:07,  7.47step/s]INFO 2026-05-19 01:47:07 ot_train.py:488 step:17K smpl:557K ep:932 epch:6.95 loss:0.008 grdn:0.203 lr:3.8e-06 updt_s:0.061 data_s:0.061
+Training:  58%|█████████████████████████████▌                     | 17419/30000 [35:17<26:58,  7.77step/s]INFO 2026-05-19 01:47:10 ot_train.py:488 step:17K smpl:557K ep:933 epch:6.96 loss:0.009 grdn:0.217 lr:3.8e-06 updt_s:0.060 data_s:0.058
+Training:  58%|█████████████████████████████▋                     | 17439/30000 [35:19<27:40,  7.56step/s]INFO 2026-05-19 01:47:12 ot_train.py:488 step:17K smpl:558K ep:934 epch:6.97 loss:0.007 grdn:0.195 lr:3.8e-06 updt_s:0.060 data_s:0.060
+Training:  58%|█████████████████████████████▋                     | 17459/30000 [35:22<27:06,  7.71step/s]INFO 2026-05-19 01:47:15 ot_train.py:488 step:17K smpl:559K ep:935 epch:6.98 loss:0.009 grdn:0.202 lr:3.8e-06 updt_s:0.058 data_s:0.063
+Training:  58%|█████████████████████████████▋                     | 17479/30000 [35:24<26:21,  7.92step/s]INFO 2026-05-19 01:47:17 ot_train.py:488 step:17K smpl:559K ep:936 epch:6.98 loss:0.009 grdn:0.198 lr:3.7e-06 updt_s:0.059 data_s:0.058
+Training:  58%|█████████████████████████████▋                     | 17499/30000 [35:27<28:16,  7.37step/s]INFO 2026-05-19 01:47:19 ot_train.py:488 step:18K smpl:560K ep:937 epch:6.99 loss:0.009 grdn:0.219 lr:3.7e-06 updt_s:0.060 data_s:0.060
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+Training:  58%|█████████████████████████████▊                     | 17539/30000 [35:31<27:15,  7.62step/s]INFO 2026-05-19 01:47:24 ot_train.py:488 step:18K smpl:561K ep:939 epch:7.01 loss:0.011 grdn:0.234 lr:3.7e-06 updt_s:0.058 data_s:0.061
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+Training:  59%|█████████████████████████████▉                     | 17579/30000 [35:36<27:21,  7.57step/s]INFO 2026-05-19 01:47:29 ot_train.py:488 step:18K smpl:563K ep:941 epch:7.02 loss:0.010 grdn:0.210 lr:3.7e-06 updt_s:0.058 data_s:0.062
+Training:  59%|█████████████████████████████▉                     | 17599/30000 [35:39<27:40,  7.47step/s]INFO 2026-05-19 01:47:31 ot_train.py:488 step:18K smpl:563K ep:942 epch:7.03 loss:0.009 grdn:0.209 lr:3.7e-06 updt_s:0.060 data_s:0.061
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+Training:  59%|██████████████████████████████▏                    | 17779/30000 [36:01<27:15,  7.47step/s]INFO 2026-05-19 01:47:53 ot_train.py:488 step:18K smpl:569K ep:952 epch:7.10 loss:0.008 grdn:0.228 lr:3.6e-06 updt_s:0.060 data_s:0.061
+Training:  59%|██████████████████████████████▎                    | 17799/30000 [36:03<26:09,  7.77step/s]INFO 2026-05-19 01:47:56 ot_train.py:488 step:18K smpl:570K ep:953 epch:7.11 loss:0.009 grdn:0.224 lr:3.6e-06 updt_s:0.058 data_s:0.061
+Training:  59%|██████████████████████████████▎                    | 17819/30000 [36:05<26:58,  7.52step/s]INFO 2026-05-19 01:47:58 ot_train.py:488 step:18K smpl:570K ep:954 epch:7.12 loss:0.008 grdn:0.218 lr:3.6e-06 updt_s:0.058 data_s:0.062
+Training:  59%|██████████████████████████████▎                    | 17839/30000 [36:08<26:42,  7.59step/s]INFO 2026-05-19 01:48:00 ot_train.py:488 step:18K smpl:571K ep:955 epch:7.13 loss:0.010 grdn:0.219 lr:3.6e-06 updt_s:0.059 data_s:0.063
+Training:  60%|██████████████████████████████▎                    | 17859/30000 [36:10<26:22,  7.67step/s]INFO 2026-05-19 01:48:03 ot_train.py:488 step:18K smpl:572K ep:956 epch:7.14 loss:0.009 grdn:0.207 lr:3.5e-06 updt_s:0.058 data_s:0.062
+Training:  60%|██████████████████████████████▍                    | 17879/30000 [36:13<25:27,  7.94step/s]INFO 2026-05-19 01:48:05 ot_train.py:488 step:18K smpl:572K ep:957 epch:7.14 loss:0.009 grdn:0.232 lr:3.5e-06 updt_s:0.059 data_s:0.059
+Training:  60%|██████████████████████████████▍                    | 17899/30000 [36:15<26:02,  7.74step/s]INFO 2026-05-19 01:48:08 ot_train.py:488 step:18K smpl:573K ep:958 epch:7.15 loss:0.010 grdn:0.221 lr:3.5e-06 updt_s:0.059 data_s:0.060
+Training:  60%|██████████████████████████████▍                    | 17919/30000 [36:17<27:36,  7.29step/s]INFO 2026-05-19 01:48:10 ot_train.py:488 step:18K smpl:573K ep:959 epch:7.16 loss:0.011 grdn:0.229 lr:3.5e-06 updt_s:0.058 data_s:0.064
+Training:  60%|██████████████████████████████▍                    | 17939/30000 [36:20<26:38,  7.54step/s]INFO 2026-05-19 01:48:13 ot_train.py:488 step:18K smpl:574K ep:961 epch:7.17 loss:0.010 grdn:0.209 lr:3.5e-06 updt_s:0.062 data_s:0.060
+Training:  60%|██████████████████████████████▌                    | 17959/30000 [36:22<26:38,  7.53step/s]INFO 2026-05-19 01:48:15 ot_train.py:488 step:18K smpl:575K ep:962 epch:7.18 loss:0.009 grdn:0.193 lr:3.5e-06 updt_s:0.061 data_s:0.061
+Training:  60%|██████████████████████████████▌                    | 17979/30000 [36:25<25:53,  7.74step/s]INFO 2026-05-19 01:48:17 ot_train.py:488 step:18K smpl:575K ep:963 epch:7.18 loss:0.009 grdn:0.233 lr:3.5e-06 updt_s:0.061 data_s:0.058
+Training:  60%|██████████████████████████████▌                    | 17999/30000 [36:27<27:13,  7.35step/s]INFO 2026-05-19 01:48:20 ot_train.py:488 step:18K smpl:576K ep:964 epch:7.19 loss:0.009 grdn:0.223 lr:3.5e-06 updt_s:0.059 data_s:0.064
+Training:  60%|██████████████████████████████▋                    | 18019/30000 [36:30<26:59,  7.40step/s]INFO 2026-05-19 01:48:22 ot_train.py:488 step:18K smpl:577K ep:965 epch:7.20 loss:0.009 grdn:0.217 lr:3.5e-06 updt_s:0.059 data_s:0.065
+Training:  60%|██████████████████████████████▋                    | 18039/30000 [36:32<27:30,  7.25step/s]INFO 2026-05-19 01:48:25 ot_train.py:488 step:18K smpl:577K ep:966 epch:7.21 loss:0.009 grdn:0.205 lr:3.5e-06 updt_s:0.060 data_s:0.065
+Training:  60%|██████████████████████████████▋                    | 18059/30000 [36:35<27:06,  7.34step/s]INFO 2026-05-19 01:48:27 ot_train.py:488 step:18K smpl:578K ep:967 epch:7.22 loss:0.009 grdn:0.219 lr:3.4e-06 updt_s:0.061 data_s:0.062
+Training:  60%|██████████████████████████████▋                    | 18079/30000 [36:37<26:25,  7.52step/s]INFO 2026-05-19 01:48:30 ot_train.py:488 step:18K smpl:579K ep:968 epch:7.22 loss:0.008 grdn:0.196 lr:3.4e-06 updt_s:0.060 data_s:0.063
+Training:  60%|██████████████████████████████▊                    | 18099/30000 [36:40<26:29,  7.49step/s]INFO 2026-05-19 01:48:32 ot_train.py:488 step:18K smpl:579K ep:969 epch:7.23 loss:0.009 grdn:0.213 lr:3.4e-06 updt_s:0.061 data_s:0.061
+Training:  60%|██████████████████████████████▊                    | 18119/30000 [36:42<26:04,  7.59step/s]INFO 2026-05-19 01:48:35 ot_train.py:488 step:18K smpl:580K ep:970 epch:7.24 loss:0.010 grdn:0.243 lr:3.4e-06 updt_s:0.061 data_s:0.061
+Training:  60%|██████████████████████████████▊                    | 18139/30000 [36:45<25:36,  7.72step/s]INFO 2026-05-19 01:48:37 ot_train.py:488 step:18K smpl:580K ep:971 epch:7.25 loss:0.010 grdn:0.235 lr:3.4e-06 updt_s:0.060 data_s:0.060
+Training:  61%|██████████████████████████████▊                    | 18159/30000 [36:47<26:25,  7.47step/s]INFO 2026-05-19 01:48:40 ot_train.py:488 step:18K smpl:581K ep:972 epch:7.26 loss:0.008 grdn:0.233 lr:3.4e-06 updt_s:0.061 data_s:0.062
+Training:  61%|██████████████████████████████▉                    | 18179/30000 [36:50<27:21,  7.20step/s]INFO 2026-05-19 01:48:42 ot_train.py:488 step:18K smpl:582K ep:973 epch:7.26 loss:0.009 grdn:0.238 lr:3.4e-06 updt_s:0.062 data_s:0.063
+Training:  61%|██████████████████████████████▉                    | 18199/30000 [36:52<25:21,  7.76step/s]INFO 2026-05-19 01:48:45 ot_train.py:488 step:18K smpl:582K ep:974 epch:7.27 loss:0.009 grdn:0.230 lr:3.4e-06 updt_s:0.061 data_s:0.059
+Training:  61%|██████████████████████████████▉                    | 18219/30000 [36:55<30:14,  6.49step/s]INFO 2026-05-19 01:48:47 ot_train.py:488 step:18K smpl:583K ep:976 epch:7.28 loss:0.008 grdn:0.221 lr:3.4e-06 updt_s:0.059 data_s:0.069
+Training:  61%|███████████████████████████████                    | 18239/30000 [36:57<26:06,  7.51step/s]INFO 2026-05-19 01:48:50 ot_train.py:488 step:18K smpl:584K ep:977 epch:7.29 loss:0.011 grdn:0.240 lr:3.4e-06 updt_s:0.060 data_s:0.061
+Training:  61%|███████████████████████████████                    | 18259/30000 [36:59<25:54,  7.56step/s]INFO 2026-05-19 01:48:52 ot_train.py:488 step:18K smpl:584K ep:978 epch:7.30 loss:0.012 grdn:0.256 lr:3.4e-06 updt_s:0.059 data_s:0.063
+Training:  61%|███████████████████████████████                    | 18279/30000 [37:02<25:55,  7.54step/s]INFO 2026-05-19 01:48:55 ot_train.py:488 step:18K smpl:585K ep:979 epch:7.30 loss:0.011 grdn:0.222 lr:3.3e-06 updt_s:0.058 data_s:0.064
+Training:  61%|███████████████████████████████                    | 18299/30000 [37:04<25:49,  7.55step/s]INFO 2026-05-19 01:48:57 ot_train.py:488 step:18K smpl:586K ep:980 epch:7.31 loss:0.009 grdn:0.213 lr:3.3e-06 updt_s:0.060 data_s:0.061
+Training:  61%|███████████████████████████████▏                   | 18319/30000 [37:07<25:45,  7.56step/s]INFO 2026-05-19 01:48:59 ot_train.py:488 step:18K smpl:586K ep:981 epch:7.32 loss:0.008 grdn:0.186 lr:3.3e-06 updt_s:0.060 data_s:0.061
+Training:  61%|███████████████████████████████▏                   | 18339/30000 [37:09<26:23,  7.36step/s]INFO 2026-05-19 01:49:02 ot_train.py:488 step:18K smpl:587K ep:982 epch:7.33 loss:0.009 grdn:0.197 lr:3.3e-06 updt_s:0.058 data_s:0.064
+Training:  61%|███████████████████████████████▏                   | 18359/30000 [37:12<25:25,  7.63step/s]INFO 2026-05-19 01:49:04 ot_train.py:488 step:18K smpl:588K ep:983 epch:7.34 loss:0.010 grdn:0.242 lr:3.3e-06 updt_s:0.058 data_s:0.060
+Training:  61%|███████████████████████████████▏                   | 18379/30000 [37:14<24:09,  8.01step/s]INFO 2026-05-19 01:49:07 ot_train.py:488 step:18K smpl:588K ep:984 epch:7.34 loss:0.008 grdn:0.206 lr:3.3e-06 updt_s:0.061 data_s:0.057
+Training:  61%|███████████████████████████████▎                   | 18399/30000 [37:16<25:59,  7.44step/s]INFO 2026-05-19 01:49:09 ot_train.py:488 step:18K smpl:589K ep:985 epch:7.35 loss:0.009 grdn:0.222 lr:3.3e-06 updt_s:0.059 data_s:0.062
+Training:  61%|███████████████████████████████▎                   | 18419/30000 [37:19<25:25,  7.59step/s]INFO 2026-05-19 01:49:11 ot_train.py:488 step:18K smpl:589K ep:986 epch:7.36 loss:0.009 grdn:0.189 lr:3.3e-06 updt_s:0.060 data_s:0.059
+Training:  61%|███████████████████████████████▎                   | 18439/30000 [37:21<25:52,  7.45step/s]INFO 2026-05-19 01:49:14 ot_train.py:488 step:18K smpl:590K ep:987 epch:7.37 loss:0.009 grdn:0.221 lr:3.3e-06 updt_s:0.060 data_s:0.060
+Training:  62%|███████████████████████████████▍                   | 18459/30000 [37:24<25:47,  7.46step/s]INFO 2026-05-19 01:49:16 ot_train.py:488 step:18K smpl:591K ep:988 epch:7.38 loss:0.009 grdn:0.209 lr:3.3e-06 updt_s:0.059 data_s:0.063
+Training:  62%|███████████████████████████████▍                   | 18479/30000 [37:26<26:15,  7.31step/s]INFO 2026-05-19 01:49:19 ot_train.py:488 step:18K smpl:591K ep:989 epch:7.38 loss:0.010 grdn:0.212 lr:3.2e-06 updt_s:0.057 data_s:0.066
+Training:  62%|███████████████████████████████▍                   | 18499/30000 [37:28<24:27,  7.84step/s]INFO 2026-05-19 01:49:21 ot_train.py:488 step:18K smpl:592K ep:991 epch:7.39 loss:0.009 grdn:0.218 lr:3.2e-06 updt_s:0.059 data_s:0.055
+Training:  62%|███████████████████████████████▍                   | 18519/30000 [37:31<24:13,  7.90step/s]INFO 2026-05-19 01:49:23 ot_train.py:488 step:19K smpl:593K ep:992 epch:7.40 loss:0.011 grdn:0.232 lr:3.2e-06 updt_s:0.058 data_s:0.057
+Training:  62%|███████████████████████████████▌                   | 18539/30000 [37:33<24:53,  7.67step/s]INFO 2026-05-19 01:49:26 ot_train.py:488 step:19K smpl:593K ep:993 epch:7.41 loss:0.009 grdn:0.204 lr:3.2e-06 updt_s:0.059 data_s:0.059
+Training:  62%|███████████████████████████████▌                   | 18559/30000 [37:35<25:17,  7.54step/s]INFO 2026-05-19 01:49:28 ot_train.py:488 step:19K smpl:594K ep:994 epch:7.42 loss:0.009 grdn:0.222 lr:3.2e-06 updt_s:0.058 data_s:0.060
+Training:  62%|███████████████████████████████▌                   | 18579/30000 [37:38<25:15,  7.53step/s]INFO 2026-05-19 01:49:31 ot_train.py:488 step:19K smpl:595K ep:995 epch:7.42 loss:0.009 grdn:0.216 lr:3.2e-06 updt_s:0.059 data_s:0.062
+Training:  62%|███████████████████████████████▌                   | 18599/30000 [37:40<21:30,  8.83step/s]INFO 2026-05-19 01:49:33 ot_train.py:488 step:19K smpl:595K ep:996 epch:7.43 loss:0.010 grdn:0.231 lr:3.2e-06 updt_s:0.059 data_s:0.059
+Training:  62%|███████████████████████████████▋                   | 18619/30000 [37:43<21:15,  8.92step/s]INFO 2026-05-19 01:49:35 ot_train.py:488 step:19K smpl:596K ep:997 epch:7.44 loss:0.010 grdn:0.228 lr:3.2e-06 updt_s:0.060 data_s:0.061
+Training:  62%|███████████████████████████████▋                   | 18639/30000 [37:45<22:26,  8.44step/s]INFO 2026-05-19 01:49:38 ot_train.py:488 step:19K smpl:596K ep:998 epch:7.45 loss:0.010 grdn:0.218 lr:3.2e-06 updt_s:0.059 data_s:0.064
+Training:  62%|███████████████████████████████▋                   | 18659/30000 [37:48<21:11,  8.92step/s]INFO 2026-05-19 01:49:40 ot_train.py:488 step:19K smpl:597K ep:999 epch:7.46 loss:0.009 grdn:0.214 lr:3.2e-06 updt_s:0.059 data_s:0.058
+Training:  62%|███████████████████████████████▊                   | 18679/30000 [37:50<21:53,  8.62step/s]INFO 2026-05-19 01:49:43 ot_train.py:488 step:19K smpl:598K ep:1K epch:7.46 loss:0.009 grdn:0.220 lr:3.1e-06 updt_s:0.059 data_s:0.062
+Training:  62%|███████████████████████████████▊                   | 18699/30000 [37:53<26:42,  7.05step/s]INFO 2026-05-19 01:49:45 ot_train.py:488 step:19K smpl:598K ep:1K epch:7.47 loss:0.011 grdn:0.246 lr:3.1e-06 updt_s:0.059 data_s:0.077
+Training:  62%|███████████████████████████████▊                   | 18719/30000 [37:55<24:06,  7.80step/s]INFO 2026-05-19 01:49:48 ot_train.py:488 step:19K smpl:599K ep:1K epch:7.48 loss:0.010 grdn:0.234 lr:3.1e-06 updt_s:0.062 data_s:0.059
+Training:  62%|███████████████████████████████▊                   | 18739/30000 [37:58<24:17,  7.73step/s]INFO 2026-05-19 01:49:50 ot_train.py:488 step:19K smpl:600K ep:1K epch:7.49 loss:0.011 grdn:0.217 lr:3.1e-06 updt_s:0.061 data_s:0.058
+Training:  63%|███████████████████████████████▉                   | 18759/30000 [38:00<23:34,  7.95step/s]INFO 2026-05-19 01:49:53 ot_train.py:488 step:19K smpl:600K ep:1K epch:7.50 loss:0.011 grdn:0.254 lr:3.1e-06 updt_s:0.060 data_s:0.059
+Training:  63%|███████████████████████████████▉                   | 18779/30000 [38:02<24:47,  7.55step/s]INFO 2026-05-19 01:49:55 ot_train.py:488 step:19K smpl:601K ep:1K epch:7.50 loss:0.008 grdn:0.217 lr:3.1e-06 updt_s:0.059 data_s:0.061
+Training:  63%|███████████████████████████████▉                   | 18799/30000 [38:05<23:26,  7.96step/s]INFO 2026-05-19 01:49:57 ot_train.py:488 step:19K smpl:602K ep:1K epch:7.51 loss:0.009 grdn:0.216 lr:3.1e-06 updt_s:0.059 data_s:0.058
+Training:  63%|███████████████████████████████▉                   | 18819/30000 [38:07<23:26,  7.95step/s]INFO 2026-05-19 01:50:00 ot_train.py:488 step:19K smpl:602K ep:1K epch:7.52 loss:0.010 grdn:0.235 lr:3.1e-06 updt_s:0.059 data_s:0.058
+Training:  63%|████████████████████████████████                   | 18839/30000 [38:09<22:40,  8.20step/s]INFO 2026-05-19 01:50:02 ot_train.py:488 step:19K smpl:603K ep:1K epch:7.53 loss:0.009 grdn:0.206 lr:3.1e-06 updt_s:0.059 data_s:0.056
+Training:  63%|████████████████████████████████                   | 18859/30000 [38:12<22:59,  8.08step/s]INFO 2026-05-19 01:50:04 ot_train.py:488 step:19K smpl:604K ep:1K epch:7.54 loss:0.011 grdn:0.220 lr:3.1e-06 updt_s:0.059 data_s:0.056
+Training:  63%|████████████████████████████████                   | 18880/30000 [38:14<19:22,  9.56step/s]INFO 2026-05-19 01:50:07 ot_train.py:488 step:19K smpl:604K ep:1K epch:7.54 loss:0.010 grdn:0.234 lr:3.0e-06 updt_s:0.060 data_s:0.055
+Training:  63%|████████████████████████████████▏                  | 18899/30000 [38:16<21:57,  8.43step/s]INFO 2026-05-19 01:50:09 ot_train.py:488 step:19K smpl:605K ep:1K epch:7.55 loss:0.010 grdn:0.220 lr:3.0e-06 updt_s:0.058 data_s:0.057
+Training:  63%|████████████████████████████████▏                  | 18919/30000 [38:19<22:46,  8.11step/s]INFO 2026-05-19 01:50:11 ot_train.py:488 step:19K smpl:605K ep:1K epch:7.56 loss:0.008 grdn:0.184 lr:3.0e-06 updt_s:0.059 data_s:0.057
+Training:  63%|████████████████████████████████▏                  | 18939/30000 [38:21<23:52,  7.72step/s]INFO 2026-05-19 01:50:14 ot_train.py:488 step:19K smpl:606K ep:1K epch:7.57 loss:0.010 grdn:0.233 lr:3.0e-06 updt_s:0.059 data_s:0.060
+Training:  63%|████████████████████████████████▏                  | 18959/30000 [38:23<22:49,  8.06step/s]INFO 2026-05-19 01:50:16 ot_train.py:488 step:19K smpl:607K ep:1K epch:7.58 loss:0.008 grdn:0.229 lr:3.0e-06 updt_s:0.059 data_s:0.058
+Training:  63%|████████████████████████████████▎                  | 18979/30000 [38:26<22:50,  8.04step/s]INFO 2026-05-19 01:50:19 ot_train.py:488 step:19K smpl:607K ep:1K epch:7.58 loss:0.010 grdn:0.221 lr:3.0e-06 updt_s:0.058 data_s:0.061
+Training:  63%|████████████████████████████████▎                  | 18999/30000 [38:28<23:26,  7.82step/s]INFO 2026-05-19 01:50:21 ot_train.py:488 step:19K smpl:608K ep:1K epch:7.59 loss:0.010 grdn:0.235 lr:3.0e-06 updt_s:0.059 data_s:0.058
+Training:  63%|████████████████████████████████▎                  | 19019/30000 [38:31<23:41,  7.73step/s]INFO 2026-05-19 01:50:23 ot_train.py:488 step:19K smpl:609K ep:1K epch:7.60 loss:0.009 grdn:0.211 lr:3.0e-06 updt_s:0.061 data_s:0.057
+Training:  63%|████████████████████████████████▎                  | 19039/30000 [38:33<23:20,  7.83step/s]INFO 2026-05-19 01:50:26 ot_train.py:488 step:19K smpl:609K ep:1K epch:7.61 loss:0.009 grdn:0.215 lr:3.0e-06 updt_s:0.059 data_s:0.058
+Training:  64%|████████████████████████████████▍                  | 19059/30000 [38:35<24:14,  7.52step/s]INFO 2026-05-19 01:50:28 ot_train.py:488 step:19K smpl:610K ep:1K epch:7.62 loss:0.010 grdn:0.237 lr:3.0e-06 updt_s:0.061 data_s:0.060
+Training:  64%|████████████████████████████████▍                  | 19079/30000 [38:38<24:05,  7.56step/s]INFO 2026-05-19 01:50:31 ot_train.py:488 step:19K smpl:611K ep:1K epch:7.62 loss:0.010 grdn:0.242 lr:3.0e-06 updt_s:0.058 data_s:0.062
+Training:  64%|████████████████████████████████▍                  | 19099/30000 [38:40<23:25,  7.76step/s]INFO 2026-05-19 01:50:33 ot_train.py:488 step:19K smpl:611K ep:1K epch:7.63 loss:0.010 grdn:0.250 lr:2.9e-06 updt_s:0.058 data_s:0.060
+Training:  64%|████████████████████████████████▌                  | 19119/30000 [38:43<24:16,  7.47step/s]INFO 2026-05-19 01:50:35 ot_train.py:488 step:19K smpl:612K ep:1K epch:7.64 loss:0.008 grdn:0.194 lr:2.9e-06 updt_s:0.059 data_s:0.063
+Training:  64%|████████████████████████████████▌                  | 19139/30000 [38:45<23:10,  7.81step/s]INFO 2026-05-19 01:50:38 ot_train.py:488 step:19K smpl:612K ep:1K epch:7.65 loss:0.008 grdn:0.216 lr:2.9e-06 updt_s:0.060 data_s:0.060
+Training:  64%|████████████████████████████████▌                  | 19159/30000 [38:48<23:52,  7.57step/s]INFO 2026-05-19 01:50:40 ot_train.py:488 step:19K smpl:613K ep:1K epch:7.66 loss:0.010 grdn:0.214 lr:2.9e-06 updt_s:0.058 data_s:0.062
+Training:  64%|████████████████████████████████▌                  | 19179/30000 [38:50<23:37,  7.63step/s]INFO 2026-05-19 01:50:43 ot_train.py:488 step:19K smpl:614K ep:1K epch:7.66 loss:0.009 grdn:0.203 lr:2.9e-06 updt_s:0.058 data_s:0.063
+Training:  64%|████████████████████████████████▋                  | 19199/30000 [38:52<23:33,  7.64step/s]INFO 2026-05-19 01:50:45 ot_train.py:488 step:19K smpl:614K ep:1K epch:7.67 loss:0.010 grdn:0.212 lr:2.9e-06 updt_s:0.061 data_s:0.059
+Training:  64%|████████████████████████████████▋                  | 19219/30000 [38:55<23:43,  7.57step/s]INFO 2026-05-19 01:50:47 ot_train.py:488 step:19K smpl:615K ep:1K epch:7.68 loss:0.010 grdn:0.237 lr:2.9e-06 updt_s:0.061 data_s:0.059
+Training:  64%|████████████████████████████████▋                  | 19239/30000 [38:57<22:56,  7.82step/s]INFO 2026-05-19 01:50:50 ot_train.py:488 step:19K smpl:616K ep:1K epch:7.69 loss:0.009 grdn:0.204 lr:2.9e-06 updt_s:0.059 data_s:0.060
+Training:  64%|████████████████████████████████▋                  | 19259/30000 [39:00<23:37,  7.57step/s]INFO 2026-05-19 01:50:52 ot_train.py:488 step:19K smpl:616K ep:1K epch:7.70 loss:0.009 grdn:0.201 lr:2.9e-06 updt_s:0.061 data_s:0.062
+Training:  64%|████████████████████████████████▊                  | 19279/30000 [39:02<22:54,  7.80step/s]INFO 2026-05-19 01:50:55 ot_train.py:488 step:19K smpl:617K ep:1K epch:7.70 loss:0.010 grdn:0.226 lr:2.9e-06 updt_s:0.061 data_s:0.059
+Training:  64%|████████████████████████████████▊                  | 19299/30000 [39:04<23:24,  7.62step/s]INFO 2026-05-19 01:50:57 ot_train.py:488 step:19K smpl:618K ep:1K epch:7.71 loss:0.009 grdn:0.205 lr:2.8e-06 updt_s:0.060 data_s:0.060
+Training:  64%|████████████████████████████████▊                  | 19319/30000 [39:07<22:56,  7.76step/s]INFO 2026-05-19 01:51:00 ot_train.py:488 step:19K smpl:618K ep:1K epch:7.72 loss:0.010 grdn:0.245 lr:2.8e-06 updt_s:0.060 data_s:0.061
+Training:  64%|████████████████████████████████▉                  | 19339/30000 [39:09<23:12,  7.66step/s]INFO 2026-05-19 01:51:02 ot_train.py:488 step:19K smpl:619K ep:1K epch:7.73 loss:0.010 grdn:0.230 lr:2.8e-06 updt_s:0.060 data_s:0.060
+Training:  65%|████████████████████████████████▉                  | 19359/30000 [39:12<22:26,  7.90step/s]INFO 2026-05-19 01:51:04 ot_train.py:488 step:19K smpl:620K ep:1K epch:7.74 loss:0.010 grdn:0.221 lr:2.8e-06 updt_s:0.059 data_s:0.058
+Training:  65%|████████████████████████████████▉                  | 19379/30000 [39:14<23:10,  7.64step/s]INFO 2026-05-19 01:51:07 ot_train.py:488 step:19K smpl:620K ep:1K epch:7.74 loss:0.010 grdn:0.226 lr:2.8e-06 updt_s:0.059 data_s:0.059
+Training:  65%|████████████████████████████████▉                  | 19399/30000 [39:16<22:38,  7.80step/s]INFO 2026-05-19 01:51:09 ot_train.py:488 step:19K smpl:621K ep:1K epch:7.75 loss:0.008 grdn:0.196 lr:2.8e-06 updt_s:0.058 data_s:0.060
+Training:  65%|█████████████████████████████████                  | 19419/30000 [39:19<22:14,  7.93step/s]INFO 2026-05-19 01:51:12 ot_train.py:488 step:19K smpl:621K ep:1K epch:7.76 loss:0.010 grdn:0.221 lr:2.8e-06 updt_s:0.058 data_s:0.061
+Training:  65%|█████████████████████████████████                  | 19439/30000 [39:21<23:55,  7.36step/s]INFO 2026-05-19 01:51:14 ot_train.py:488 step:19K smpl:622K ep:1K epch:7.77 loss:0.009 grdn:0.214 lr:2.8e-06 updt_s:0.058 data_s:0.064
+Training:  65%|█████████████████████████████████                  | 19459/30000 [39:24<22:29,  7.81step/s]INFO 2026-05-19 01:51:16 ot_train.py:488 step:19K smpl:623K ep:1K epch:7.78 loss:0.009 grdn:0.201 lr:2.8e-06 updt_s:0.059 data_s:0.060
+Training:  65%|█████████████████████████████████                  | 19479/30000 [39:26<22:34,  7.77step/s]INFO 2026-05-19 01:51:19 ot_train.py:488 step:19K smpl:623K ep:1K epch:7.78 loss:0.008 grdn:0.208 lr:2.8e-06 updt_s:0.059 data_s:0.060
+Training:  65%|█████████████████████████████████▏                 | 19499/30000 [39:28<22:52,  7.65step/s]INFO 2026-05-19 01:51:21 ot_train.py:488 step:20K smpl:624K ep:1K epch:7.79 loss:0.009 grdn:0.205 lr:2.8e-06 updt_s:0.058 data_s:0.060
+Training:  65%|█████████████████████████████████▏                 | 19519/30000 [39:31<22:40,  7.70step/s]INFO 2026-05-19 01:51:24 ot_train.py:488 step:20K smpl:625K ep:1K epch:7.80 loss:0.009 grdn:0.207 lr:2.7e-06 updt_s:0.059 data_s:0.061
+Training:  65%|█████████████████████████████████▏                 | 19539/30000 [39:33<22:37,  7.71step/s]INFO 2026-05-19 01:51:26 ot_train.py:488 step:20K smpl:625K ep:1K epch:7.81 loss:0.010 grdn:0.221 lr:2.7e-06 updt_s:0.057 data_s:0.063
+Training:  65%|█████████████████████████████████▎                 | 19559/30000 [39:36<22:51,  7.62step/s]INFO 2026-05-19 01:51:28 ot_train.py:488 step:20K smpl:626K ep:1K epch:7.82 loss:0.012 grdn:0.237 lr:2.7e-06 updt_s:0.059 data_s:0.061
+Training:  65%|█████████████████████████████████▎                 | 19579/30000 [39:38<22:10,  7.83step/s]INFO 2026-05-19 01:51:31 ot_train.py:488 step:20K smpl:627K ep:1K epch:7.82 loss:0.009 grdn:0.206 lr:2.7e-06 updt_s:0.058 data_s:0.060
+Training:  65%|█████████████████████████████████▎                 | 19599/30000 [39:41<22:52,  7.58step/s]INFO 2026-05-19 01:51:33 ot_train.py:488 step:20K smpl:627K ep:1K epch:7.83 loss:0.007 grdn:0.196 lr:2.7e-06 updt_s:0.058 data_s:0.064
+Training:  65%|█████████████████████████████████▎                 | 19619/30000 [39:43<22:11,  7.80step/s]INFO 2026-05-19 01:51:36 ot_train.py:488 step:20K smpl:628K ep:1K epch:7.84 loss:0.009 grdn:0.224 lr:2.7e-06 updt_s:0.059 data_s:0.060
+Training:  65%|█████████████████████████████████▍                 | 19639/30000 [39:45<22:25,  7.70step/s]INFO 2026-05-19 01:51:38 ot_train.py:488 step:20K smpl:628K ep:1K epch:7.85 loss:0.009 grdn:0.210 lr:2.7e-06 updt_s:0.058 data_s:0.066
+Training:  66%|█████████████████████████████████▍                 | 19659/30000 [39:48<22:21,  7.71step/s]INFO 2026-05-19 01:51:41 ot_train.py:488 step:20K smpl:629K ep:1K epch:7.86 loss:0.008 grdn:0.203 lr:2.7e-06 updt_s:0.058 data_s:0.060
+Training:  66%|█████████████████████████████████▍                 | 19679/30000 [39:50<21:59,  7.82step/s]INFO 2026-05-19 01:51:43 ot_train.py:488 step:20K smpl:630K ep:1K epch:7.86 loss:0.009 grdn:0.204 lr:2.7e-06 updt_s:0.059 data_s:0.059
+Training:  66%|█████████████████████████████████▍                 | 19699/30000 [39:53<22:05,  7.77step/s]INFO 2026-05-19 01:51:45 ot_train.py:488 step:20K smpl:630K ep:1K epch:7.87 loss:0.010 grdn:0.224 lr:2.7e-06 updt_s:0.059 data_s:0.057
+Training:  66%|█████████████████████████████████▌                 | 19719/30000 [39:55<22:12,  7.72step/s]INFO 2026-05-19 01:51:48 ot_train.py:488 step:20K smpl:631K ep:1K epch:7.88 loss:0.009 grdn:0.206 lr:2.6e-06 updt_s:0.061 data_s:0.056
+Training:  66%|█████████████████████████████████▌                 | 19739/30000 [39:57<21:08,  8.09step/s]INFO 2026-05-19 01:51:50 ot_train.py:488 step:20K smpl:632K ep:1K epch:7.89 loss:0.010 grdn:0.222 lr:2.6e-06 updt_s:0.058 data_s:0.055
+Training:  66%|█████████████████████████████████▌                 | 19759/30000 [40:00<21:59,  7.76step/s]INFO 2026-05-19 01:51:52 ot_train.py:488 step:20K smpl:632K ep:1K epch:7.90 loss:0.009 grdn:0.203 lr:2.6e-06 updt_s:0.061 data_s:0.058
+Training:  66%|█████████████████████████████████▌                 | 19779/30000 [40:02<20:14,  8.41step/s]INFO 2026-05-19 01:51:55 ot_train.py:488 step:20K smpl:633K ep:1K epch:7.90 loss:0.010 grdn:0.236 lr:2.6e-06 updt_s:0.061 data_s:0.053
+Training:  66%|█████████████████████████████████▋                 | 19799/30000 [40:04<22:24,  7.59step/s]INFO 2026-05-19 01:51:57 ot_train.py:488 step:20K smpl:634K ep:1K epch:7.91 loss:0.009 grdn:0.210 lr:2.6e-06 updt_s:0.059 data_s:0.079
+Training:  66%|█████████████████████████████████▋                 | 19819/30000 [40:07<24:31,  6.92step/s]INFO 2026-05-19 01:52:00 ot_train.py:488 step:20K smpl:634K ep:1K epch:7.92 loss:0.010 grdn:0.203 lr:2.6e-06 updt_s:0.059 data_s:0.080
+Training:  66%|█████████████████████████████████▋                 | 19839/30000 [40:10<18:17,  9.25step/s]INFO 2026-05-19 01:52:02 ot_train.py:488 step:20K smpl:635K ep:1K epch:7.93 loss:0.010 grdn:0.233 lr:2.6e-06 updt_s:0.061 data_s:0.054
+Training:  66%|█████████████████████████████████▊                 | 19859/30000 [40:12<18:17,  9.24step/s]INFO 2026-05-19 01:52:05 ot_train.py:488 step:20K smpl:636K ep:1K epch:7.94 loss:0.011 grdn:0.212 lr:2.6e-06 updt_s:0.061 data_s:0.054
+Training:  66%|█████████████████████████████████▊                 | 19879/30000 [40:14<17:13,  9.79step/s]INFO 2026-05-19 01:52:07 ot_train.py:488 step:20K smpl:636K ep:1K epch:7.94 loss:0.009 grdn:0.219 lr:2.6e-06 updt_s:0.060 data_s:0.050
+Training:  66%|█████████████████████████████████▊                 | 19899/30000 [40:17<19:03,  8.83step/s]INFO 2026-05-19 01:52:09 ot_train.py:488 step:20K smpl:637K ep:1K epch:7.95 loss:0.009 grdn:0.224 lr:2.6e-06 updt_s:0.059 data_s:0.061
+Training:  66%|█████████████████████████████████▊                 | 19919/30000 [40:19<18:51,  8.91step/s]INFO 2026-05-19 01:52:12 ot_train.py:488 step:20K smpl:637K ep:1K epch:7.96 loss:0.008 grdn:0.197 lr:2.6e-06 updt_s:0.060 data_s:0.061
+Training:  66%|█████████████████████████████████▉                 | 19939/30000 [40:21<19:56,  8.41step/s]INFO 2026-05-19 01:52:14 ot_train.py:488 step:20K smpl:638K ep:1K epch:7.97 loss:0.010 grdn:0.237 lr:2.5e-06 updt_s:0.063 data_s:0.064
+Training:  67%|█████████████████████████████████▉                 | 19959/30000 [40:24<19:00,  8.80step/s]INFO 2026-05-19 01:52:17 ot_train.py:488 step:20K smpl:639K ep:1K epch:7.98 loss:0.008 grdn:0.189 lr:2.5e-06 updt_s:0.059 data_s:0.062
+Training:  67%|█████████████████████████████████▉                 | 19979/30000 [40:26<18:19,  9.11step/s]INFO 2026-05-19 01:52:19 ot_train.py:488 step:20K smpl:639K ep:1K epch:7.98 loss:0.009 grdn:0.207 lr:2.5e-06 updt_s:0.060 data_s:0.063
+Training:  67%|█████████████████████████████████▉                 | 19999/30000 [40:29<18:59,  8.77step/s]INFO 2026-05-19 01:52:22 ot_train.py:488 step:20K smpl:640K ep:1K epch:7.99 loss:0.009 grdn:0.219 lr:2.5e-06 updt_s:0.061 data_s:0.058
+INFO 2026-05-19 01:52:22 ot_train.py:502 Checkpoint policy after step 20000
+Training:  67%|██████████████████████████████████                 | 20019/30000 [40:37<17:55,  9.28step/s]INFO 2026-05-19 01:52:30 ot_train.py:488 step:20K smpl:641K ep:1K epch:8.00 loss:0.008 grdn:0.205 lr:2.5e-06 updt_s:0.061 data_s:0.005
+Training:  67%|██████████████████████████████████                 | 20039/30000 [40:39<20:37,  8.05step/s]INFO 2026-05-19 01:52:32 ot_train.py:488 step:20K smpl:641K ep:1K epch:8.01 loss:0.011 grdn:0.258 lr:2.5e-06 updt_s:0.059 data_s:0.046
+Training:  67%|██████████████████████████████████                 | 20059/30000 [40:42<21:43,  7.63step/s]INFO 2026-05-19 01:52:34 ot_train.py:488 step:20K smpl:642K ep:1K epch:8.02 loss:0.010 grdn:0.248 lr:2.5e-06 updt_s:0.059 data_s:0.067
+Training:  67%|██████████████████████████████████▏                | 20079/30000 [40:44<21:54,  7.55step/s]INFO 2026-05-19 01:52:37 ot_train.py:488 step:20K smpl:643K ep:1K epch:8.02 loss:0.008 grdn:0.206 lr:2.5e-06 updt_s:0.059 data_s:0.064
+Training:  67%|██████████████████████████████████▏                | 20100/30000 [40:47<22:13,  7.42step/s]INFO 2026-05-19 01:52:39 ot_train.py:488 step:20K smpl:643K ep:1K epch:8.03 loss:0.010 grdn:0.230 lr:2.5e-06 updt_s:0.059 data_s:0.066
+Training:  67%|██████████████████████████████████▏                | 20120/30000 [40:49<21:22,  7.70step/s]INFO 2026-05-19 01:52:42 ot_train.py:488 step:20K smpl:644K ep:1K epch:8.04 loss:0.010 grdn:0.236 lr:2.5e-06 updt_s:0.058 data_s:0.063
+Training:  67%|██████████████████████████████████▏                | 20140/30000 [40:52<21:41,  7.58step/s]INFO 2026-05-19 01:52:44 ot_train.py:488 step:20K smpl:644K ep:1K epch:8.05 loss:0.011 grdn:0.216 lr:2.5e-06 updt_s:0.058 data_s:0.064
+Training:  67%|██████████████████████████████████▎                | 20160/30000 [40:54<21:14,  7.72step/s]INFO 2026-05-19 01:52:47 ot_train.py:488 step:20K smpl:645K ep:1K epch:8.06 loss:0.010 grdn:0.217 lr:2.4e-06 updt_s:0.060 data_s:0.063
+Training:  67%|██████████████████████████████████▎                | 20180/30000 [40:56<21:21,  7.66step/s]INFO 2026-05-19 01:52:49 ot_train.py:488 step:20K smpl:646K ep:1K epch:8.06 loss:0.009 grdn:0.218 lr:2.4e-06 updt_s:0.058 data_s:0.065
+Training:  67%|██████████████████████████████████▎                | 20200/30000 [40:59<21:25,  7.63step/s]INFO 2026-05-19 01:52:52 ot_train.py:488 step:20K smpl:646K ep:1K epch:8.07 loss:0.010 grdn:0.228 lr:2.4e-06 updt_s:0.060 data_s:0.064
+Training:  67%|██████████████████████████████████▎                | 20220/30000 [41:01<20:25,  7.98step/s]INFO 2026-05-19 01:52:54 ot_train.py:488 step:20K smpl:647K ep:1K epch:8.08 loss:0.011 grdn:0.231 lr:2.4e-06 updt_s:0.061 data_s:0.062
+Training:  67%|██████████████████████████████████▍                | 20240/30000 [41:04<21:34,  7.54step/s]INFO 2026-05-19 01:52:57 ot_train.py:488 step:20K smpl:648K ep:1K epch:8.09 loss:0.010 grdn:0.217 lr:2.4e-06 updt_s:0.059 data_s:0.072
+Training:  68%|██████████████████████████████████▍                | 20259/30000 [41:06<18:08,  8.95step/s]INFO 2026-05-19 01:52:59 ot_train.py:488 step:20K smpl:648K ep:1K epch:8.10 loss:0.011 grdn:0.244 lr:2.4e-06 updt_s:0.058 data_s:0.058
+Training:  68%|██████████████████████████████████▍                | 20279/30000 [41:09<19:40,  8.23step/s]INFO 2026-05-19 01:53:02 ot_train.py:488 step:20K smpl:649K ep:1K epch:8.10 loss:0.009 grdn:0.226 lr:2.4e-06 updt_s:0.058 data_s:0.067
+Training:  68%|██████████████████████████████████▌                | 20299/30000 [41:11<17:54,  9.03step/s]INFO 2026-05-19 01:53:04 ot_train.py:488 step:20K smpl:650K ep:1K epch:8.11 loss:0.009 grdn:0.203 lr:2.4e-06 updt_s:0.058 data_s:0.061
+Training:  68%|██████████████████████████████████▌                | 20319/30000 [41:14<18:13,  8.85step/s]INFO 2026-05-19 01:53:06 ot_train.py:488 step:20K smpl:650K ep:1K epch:8.12 loss:0.010 grdn:0.226 lr:2.4e-06 updt_s:0.059 data_s:0.062
+Training:  68%|██████████████████████████████████▌                | 20339/30000 [41:16<17:57,  8.97step/s]INFO 2026-05-19 01:53:09 ot_train.py:488 step:20K smpl:651K ep:1K epch:8.13 loss:0.009 grdn:0.218 lr:2.4e-06 updt_s:0.060 data_s:0.069
+Training:  68%|██████████████████████████████████▌                | 20359/30000 [41:19<17:52,  8.99step/s]INFO 2026-05-19 01:53:11 ot_train.py:488 step:20K smpl:652K ep:1K epch:8.14 loss:0.008 grdn:0.233 lr:2.4e-06 updt_s:0.060 data_s:0.061
+Training:  68%|██████████████████████████████████▋                | 20379/30000 [41:21<17:42,  9.05step/s]INFO 2026-05-19 01:53:14 ot_train.py:488 step:20K smpl:652K ep:1K epch:8.14 loss:0.010 grdn:0.233 lr:2.3e-06 updt_s:0.059 data_s:0.062
+Training:  68%|██████████████████████████████████▋                | 20399/30000 [41:24<18:06,  8.84step/s]INFO 2026-05-19 01:53:16 ot_train.py:488 step:20K smpl:653K ep:1K epch:8.15 loss:0.010 grdn:0.206 lr:2.3e-06 updt_s:0.060 data_s:0.061
+Training:  68%|██████████████████████████████████▋                | 20419/30000 [41:26<17:25,  9.17step/s]INFO 2026-05-19 01:53:19 ot_train.py:488 step:20K smpl:653K ep:1K epch:8.16 loss:0.009 grdn:0.197 lr:2.3e-06 updt_s:0.060 data_s:0.062
+Training:  68%|██████████████████████████████████▋                | 20439/30000 [41:29<17:40,  9.02step/s]INFO 2026-05-19 01:53:21 ot_train.py:488 step:20K smpl:654K ep:1K epch:8.17 loss:0.009 grdn:0.217 lr:2.3e-06 updt_s:0.059 data_s:0.060
+Training:  68%|██████████████████████████████████▊                | 20459/30000 [41:31<18:03,  8.81step/s]INFO 2026-05-19 01:53:24 ot_train.py:488 step:20K smpl:655K ep:1K epch:8.18 loss:0.009 grdn:0.211 lr:2.3e-06 updt_s:0.059 data_s:0.064
+Training:  68%|██████████████████████████████████▊                | 20479/30000 [41:33<18:00,  8.81step/s]INFO 2026-05-19 01:53:26 ot_train.py:488 step:20K smpl:655K ep:1K epch:8.18 loss:0.009 grdn:0.216 lr:2.3e-06 updt_s:0.058 data_s:0.063
+Training:  68%|██████████████████████████████████▊                | 20499/30000 [41:36<18:05,  8.75step/s]INFO 2026-05-19 01:53:29 ot_train.py:488 step:20K smpl:656K ep:1K epch:8.19 loss:0.010 grdn:0.239 lr:2.3e-06 updt_s:0.058 data_s:0.063
+Training:  68%|██████████████████████████████████▉                | 20519/30000 [41:38<17:23,  9.08step/s]INFO 2026-05-19 01:53:31 ot_train.py:488 step:21K smpl:657K ep:1K epch:8.20 loss:0.008 grdn:0.205 lr:2.3e-06 updt_s:0.058 data_s:0.061
+Training:  68%|██████████████████████████████████▉                | 20539/30000 [41:41<17:44,  8.88step/s]INFO 2026-05-19 01:53:33 ot_train.py:488 step:21K smpl:657K ep:1K epch:8.21 loss:0.008 grdn:0.194 lr:2.3e-06 updt_s:0.059 data_s:0.062
+Training:  69%|██████████████████████████████████▉                | 20559/30000 [41:43<17:24,  9.04step/s]INFO 2026-05-19 01:53:36 ot_train.py:488 step:21K smpl:658K ep:1K epch:8.22 loss:0.009 grdn:0.229 lr:2.3e-06 updt_s:0.058 data_s:0.060
+Training:  69%|██████████████████████████████████▉                | 20579/30000 [41:46<16:59,  9.24step/s]INFO 2026-05-19 01:53:38 ot_train.py:488 step:21K smpl:659K ep:1K epch:8.22 loss:0.008 grdn:0.196 lr:2.3e-06 updt_s:0.059 data_s:0.061
+Training:  69%|███████████████████████████████████                | 20599/30000 [41:48<16:59,  9.22step/s]INFO 2026-05-19 01:53:41 ot_train.py:488 step:21K smpl:659K ep:1K epch:8.23 loss:0.009 grdn:0.219 lr:2.3e-06 updt_s:0.059 data_s:0.059
+Training:  69%|███████████████████████████████████                | 20619/30000 [41:51<17:39,  8.85step/s]INFO 2026-05-19 01:53:43 ot_train.py:488 step:21K smpl:660K ep:1K epch:8.24 loss:0.010 grdn:0.221 lr:2.2e-06 updt_s:0.061 data_s:0.074
+Training:  69%|███████████████████████████████████                | 20639/30000 [41:53<17:29,  8.92step/s]INFO 2026-05-19 01:53:46 ot_train.py:488 step:21K smpl:660K ep:1K epch:8.25 loss:0.009 grdn:0.215 lr:2.2e-06 updt_s:0.060 data_s:0.060
+Training:  69%|███████████████████████████████████                | 20659/30000 [41:56<17:20,  8.98step/s]INFO 2026-05-19 01:53:48 ot_train.py:488 step:21K smpl:661K ep:1K epch:8.26 loss:0.010 grdn:0.225 lr:2.2e-06 updt_s:0.061 data_s:0.060
+Training:  69%|███████████████████████████████████▏               | 20679/30000 [41:58<17:04,  9.10step/s]INFO 2026-05-19 01:53:51 ot_train.py:488 step:21K smpl:662K ep:1K epch:8.26 loss:0.010 grdn:0.215 lr:2.2e-06 updt_s:0.059 data_s:0.058
+Training:  69%|███████████████████████████████████▏               | 20699/30000 [42:00<16:53,  9.17step/s]INFO 2026-05-19 01:53:53 ot_train.py:488 step:21K smpl:662K ep:1K epch:8.27 loss:0.009 grdn:0.219 lr:2.2e-06 updt_s:0.059 data_s:0.059
+Training:  69%|███████████████████████████████████▏               | 20719/30000 [42:03<16:41,  9.26step/s]INFO 2026-05-19 01:53:55 ot_train.py:488 step:21K smpl:663K ep:1K epch:8.28 loss:0.008 grdn:0.204 lr:2.2e-06 updt_s:0.059 data_s:0.060
+Training:  69%|███████████████████████████████████▎               | 20739/30000 [42:05<16:47,  9.19step/s]INFO 2026-05-19 01:53:58 ot_train.py:488 step:21K smpl:664K ep:1K epch:8.29 loss:0.010 grdn:0.231 lr:2.2e-06 updt_s:0.058 data_s:0.061
+Training:  69%|███████████████████████████████████▎               | 20759/30000 [42:07<17:12,  8.95step/s]INFO 2026-05-19 01:54:00 ot_train.py:488 step:21K smpl:664K ep:1K epch:8.29 loss:0.009 grdn:0.201 lr:2.2e-06 updt_s:0.059 data_s:0.060
+Training:  69%|███████████████████████████████████▎               | 20779/30000 [42:10<16:58,  9.05step/s]INFO 2026-05-19 01:54:03 ot_train.py:488 step:21K smpl:665K ep:1K epch:8.30 loss:0.008 grdn:0.203 lr:2.2e-06 updt_s:0.060 data_s:0.059
+Training:  69%|███████████████████████████████████▎               | 20799/30000 [42:12<17:24,  8.81step/s]INFO 2026-05-19 01:54:05 ot_train.py:488 step:21K smpl:666K ep:1K epch:8.31 loss:0.008 grdn:0.215 lr:2.2e-06 updt_s:0.059 data_s:0.063
+Training:  69%|███████████████████████████████████▍               | 20819/30000 [42:15<16:24,  9.32step/s]INFO 2026-05-19 01:54:07 ot_train.py:488 step:21K smpl:666K ep:1K epch:8.32 loss:0.010 grdn:0.217 lr:2.2e-06 updt_s:0.058 data_s:0.056
+Training:  69%|███████████████████████████████████▍               | 20839/30000 [42:17<16:49,  9.08step/s]INFO 2026-05-19 01:54:10 ot_train.py:488 step:21K smpl:667K ep:1K epch:8.33 loss:0.011 grdn:0.276 lr:2.1e-06 updt_s:0.060 data_s:0.061
+Training:  70%|███████████████████████████████████▍               | 20859/30000 [42:19<17:20,  8.79step/s]INFO 2026-05-19 01:54:12 ot_train.py:488 step:21K smpl:668K ep:1K epch:8.33 loss:0.010 grdn:0.229 lr:2.1e-06 updt_s:0.059 data_s:0.062
+Training:  70%|███████████████████████████████████▍               | 20879/30000 [42:22<17:23,  8.74step/s]INFO 2026-05-19 01:54:15 ot_train.py:488 step:21K smpl:668K ep:1K epch:8.34 loss:0.011 grdn:0.234 lr:2.1e-06 updt_s:0.060 data_s:0.061
+Training:  70%|███████████████████████████████████▌               | 20899/30000 [42:24<16:46,  9.04step/s]INFO 2026-05-19 01:54:17 ot_train.py:488 step:21K smpl:669K ep:1K epch:8.35 loss:0.010 grdn:0.229 lr:2.1e-06 updt_s:0.060 data_s:0.060
+Training:  70%|███████████████████████████████████▌               | 20919/30000 [42:27<16:53,  8.96step/s]INFO 2026-05-19 01:54:19 ot_train.py:488 step:21K smpl:669K ep:1K epch:8.36 loss:0.009 grdn:0.219 lr:2.1e-06 updt_s:0.060 data_s:0.061
+Training:  70%|███████████████████████████████████▌               | 20939/30000 [42:29<16:29,  9.16step/s]INFO 2026-05-19 01:54:22 ot_train.py:488 step:21K smpl:670K ep:1K epch:8.37 loss:0.010 grdn:0.230 lr:2.1e-06 updt_s:0.060 data_s:0.058
+Training:  70%|███████████████████████████████████▋               | 20959/30000 [42:32<16:52,  8.93step/s]INFO 2026-05-19 01:54:24 ot_train.py:488 step:21K smpl:671K ep:1K epch:8.37 loss:0.009 grdn:0.218 lr:2.1e-06 updt_s:0.061 data_s:0.060
+Training:  70%|███████████████████████████████████▋               | 20979/30000 [42:34<17:57,  8.37step/s]INFO 2026-05-19 01:54:27 ot_train.py:488 step:21K smpl:671K ep:1K epch:8.38 loss:0.009 grdn:0.210 lr:2.1e-06 updt_s:0.059 data_s:0.071
+Training:  70%|███████████████████████████████████▋               | 20999/30000 [42:37<16:34,  9.05step/s]INFO 2026-05-19 01:54:29 ot_train.py:488 step:21K smpl:672K ep:1K epch:8.39 loss:0.009 grdn:0.221 lr:2.1e-06 updt_s:0.058 data_s:0.062
+Training:  70%|███████████████████████████████████▋               | 21019/30000 [42:39<16:33,  9.04step/s]INFO 2026-05-19 01:54:32 ot_train.py:488 step:21K smpl:673K ep:1K epch:8.40 loss:0.009 grdn:0.210 lr:2.1e-06 updt_s:0.059 data_s:0.060
+Training:  70%|███████████████████████████████████▊               | 21039/30000 [42:41<16:41,  8.95step/s]INFO 2026-05-19 01:54:34 ot_train.py:488 step:21K smpl:673K ep:1K epch:8.41 loss:0.011 grdn:0.238 lr:2.1e-06 updt_s:0.058 data_s:0.063
+Training:  70%|███████████████████████████████████▊               | 21059/30000 [42:44<16:10,  9.21step/s]INFO 2026-05-19 01:54:37 ot_train.py:488 step:21K smpl:674K ep:1K epch:8.41 loss:0.009 grdn:0.197 lr:2.1e-06 updt_s:0.059 data_s:0.061
+Training:  70%|███████████████████████████████████▊               | 21079/30000 [42:46<16:32,  8.99step/s]INFO 2026-05-19 01:54:39 ot_train.py:488 step:21K smpl:675K ep:1K epch:8.42 loss:0.009 grdn:0.210 lr:2.0e-06 updt_s:0.058 data_s:0.063
+Training:  70%|███████████████████████████████████▊               | 21099/30000 [42:49<16:21,  9.06step/s]INFO 2026-05-19 01:54:41 ot_train.py:488 step:21K smpl:675K ep:1K epch:8.43 loss:0.010 grdn:0.231 lr:2.0e-06 updt_s:0.060 data_s:0.060
+Training:  70%|███████████████████████████████████▉               | 21119/30000 [42:51<15:49,  9.35step/s]INFO 2026-05-19 01:54:44 ot_train.py:488 step:21K smpl:676K ep:1K epch:8.44 loss:0.009 grdn:0.213 lr:2.0e-06 updt_s:0.059 data_s:0.059
+Training:  70%|███████████████████████████████████▉               | 21139/30000 [42:54<16:27,  8.97step/s]INFO 2026-05-19 01:54:46 ot_train.py:488 step:21K smpl:676K ep:1K epch:8.45 loss:0.010 grdn:0.232 lr:2.0e-06 updt_s:0.059 data_s:0.063
+Training:  71%|███████████████████████████████████▉               | 21159/30000 [42:56<16:11,  9.10step/s]INFO 2026-05-19 01:54:49 ot_train.py:488 step:21K smpl:677K ep:1K epch:8.45 loss:0.010 grdn:0.260 lr:2.0e-06 updt_s:0.058 data_s:0.061
+Training:  71%|████████████████████████████████████               | 21179/30000 [42:58<16:49,  8.74step/s]INFO 2026-05-19 01:54:51 ot_train.py:488 step:21K smpl:678K ep:1K epch:8.46 loss:0.009 grdn:0.215 lr:2.0e-06 updt_s:0.058 data_s:0.064
+Training:  71%|████████████████████████████████████               | 21199/30000 [43:01<15:58,  9.18step/s]INFO 2026-05-19 01:54:53 ot_train.py:488 step:21K smpl:678K ep:1K epch:8.47 loss:0.008 grdn:0.204 lr:2.0e-06 updt_s:0.059 data_s:0.058
+Training:  71%|████████████████████████████████████               | 21219/30000 [43:03<16:19,  8.97step/s]INFO 2026-05-19 01:54:56 ot_train.py:488 step:21K smpl:679K ep:1K epch:8.48 loss:0.008 grdn:0.218 lr:2.0e-06 updt_s:0.059 data_s:0.061
+Training:  71%|████████████████████████████████████               | 21239/30000 [43:06<15:59,  9.13step/s]INFO 2026-05-19 01:54:58 ot_train.py:488 step:21K smpl:680K ep:1K epch:8.49 loss:0.010 grdn:0.224 lr:2.0e-06 updt_s:0.059 data_s:0.060
+Training:  71%|████████████████████████████████████▏              | 21259/30000 [43:08<16:11,  9.00step/s]INFO 2026-05-19 01:55:01 ot_train.py:488 step:21K smpl:680K ep:1K epch:8.49 loss:0.009 grdn:0.220 lr:2.0e-06 updt_s:0.059 data_s:0.061
+Training:  71%|████████████████████████████████████▏              | 21279/30000 [43:10<16:00,  9.08step/s]INFO 2026-05-19 01:55:03 ot_train.py:488 step:21K smpl:681K ep:1K epch:8.50 loss:0.009 grdn:0.224 lr:2.0e-06 updt_s:0.060 data_s:0.058
+Training:  71%|████████████████████████████████████▏              | 21299/30000 [43:13<15:45,  9.20step/s]INFO 2026-05-19 01:55:05 ot_train.py:488 step:21K smpl:682K ep:1K epch:8.51 loss:0.011 grdn:0.241 lr:2.0e-06 updt_s:0.058 data_s:0.060
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+Training:  71%|████████████████████████████████████▎              | 21339/30000 [43:17<15:29,  9.32step/s]INFO 2026-05-19 01:55:10 ot_train.py:488 step:21K smpl:683K ep:1K epch:8.53 loss:0.010 grdn:0.230 lr:1.9e-06 updt_s:0.058 data_s:0.059
+Training:  71%|████████████████████████████████████▎              | 21359/30000 [43:20<16:03,  8.96step/s]INFO 2026-05-19 01:55:13 ot_train.py:488 step:21K smpl:684K ep:1K epch:8.53 loss:0.009 grdn:0.212 lr:1.9e-06 updt_s:0.058 data_s:0.069
+Training:  71%|████████████████████████████████████▎              | 21379/30000 [43:22<15:48,  9.09step/s]INFO 2026-05-19 01:55:15 ot_train.py:488 step:21K smpl:684K ep:1K epch:8.54 loss:0.009 grdn:0.229 lr:1.9e-06 updt_s:0.059 data_s:0.059
+Training:  71%|████████████████████████████████████▍              | 21399/30000 [43:25<16:08,  8.88step/s]INFO 2026-05-19 01:55:18 ot_train.py:488 step:21K smpl:685K ep:1K epch:8.55 loss:0.010 grdn:0.229 lr:1.9e-06 updt_s:0.058 data_s:0.075
+Training:  71%|████████████████████████████████████▍              | 21419/30000 [43:27<15:38,  9.14step/s]INFO 2026-05-19 01:55:20 ot_train.py:488 step:21K smpl:685K ep:1K epch:8.56 loss:0.009 grdn:0.231 lr:1.9e-06 updt_s:0.059 data_s:0.060
+Training:  71%|████████████████████████████████████▍              | 21439/30000 [43:30<18:21,  7.77step/s]INFO 2026-05-19 01:55:23 ot_train.py:488 step:21K smpl:686K ep:1K epch:8.57 loss:0.010 grdn:0.220 lr:1.9e-06 updt_s:0.059 data_s:0.068
+Training:  72%|████████████████████████████████████▍              | 21459/30000 [43:32<15:27,  9.21step/s]INFO 2026-05-19 01:55:25 ot_train.py:488 step:21K smpl:687K ep:1K epch:8.57 loss:0.008 grdn:0.209 lr:1.9e-06 updt_s:0.058 data_s:0.051
+Training:  72%|████████████████████████████████████▌              | 21479/30000 [43:35<15:48,  8.99step/s]INFO 2026-05-19 01:55:27 ot_train.py:488 step:21K smpl:687K ep:1K epch:8.58 loss:0.010 grdn:0.222 lr:1.9e-06 updt_s:0.058 data_s:0.061
+Training:  72%|████████████████████████████████████▌              | 21499/30000 [43:37<15:19,  9.24step/s]INFO 2026-05-19 01:55:30 ot_train.py:488 step:22K smpl:688K ep:1K epch:8.59 loss:0.009 grdn:0.254 lr:1.9e-06 updt_s:0.059 data_s:0.057
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+Training:  72%|████████████████████████████████████▌              | 21539/30000 [43:42<15:16,  9.23step/s]INFO 2026-05-19 01:55:34 ot_train.py:488 step:22K smpl:689K ep:1K epch:8.61 loss:0.009 grdn:0.237 lr:1.9e-06 updt_s:0.058 data_s:0.059
+Training:  72%|████████████████████████████████████▋              | 21559/30000 [43:44<15:18,  9.19step/s]INFO 2026-05-19 01:55:37 ot_train.py:488 step:22K smpl:690K ep:1K epch:8.61 loss:0.010 grdn:0.217 lr:1.8e-06 updt_s:0.058 data_s:0.058
+Training:  72%|████████████████████████████████████▋              | 21579/30000 [43:46<15:13,  9.22step/s]INFO 2026-05-19 01:55:39 ot_train.py:488 step:22K smpl:691K ep:1K epch:8.62 loss:0.009 grdn:0.205 lr:1.8e-06 updt_s:0.058 data_s:0.060
+Training:  72%|████████████████████████████████████▋              | 21599/30000 [43:49<15:18,  9.15step/s]INFO 2026-05-19 01:55:41 ot_train.py:488 step:22K smpl:691K ep:1K epch:8.63 loss:0.008 grdn:0.205 lr:1.8e-06 updt_s:0.058 data_s:0.060
+Training:  72%|████████████████████████████████████▊              | 21619/30000 [43:51<15:18,  9.12step/s]INFO 2026-05-19 01:55:44 ot_train.py:488 step:22K smpl:692K ep:1K epch:8.64 loss:0.008 grdn:0.216 lr:1.8e-06 updt_s:0.058 data_s:0.058
+Training:  72%|████████████████████████████████████▊              | 21639/30000 [43:53<15:15,  9.13step/s]INFO 2026-05-19 01:55:46 ot_train.py:488 step:22K smpl:692K ep:1K epch:8.65 loss:0.009 grdn:0.220 lr:1.8e-06 updt_s:0.058 data_s:0.058
+Training:  72%|████████████████████████████████████▊              | 21659/30000 [43:56<15:20,  9.06step/s]INFO 2026-05-19 01:55:49 ot_train.py:488 step:22K smpl:693K ep:1K epch:8.65 loss:0.012 grdn:0.263 lr:1.8e-06 updt_s:0.060 data_s:0.057
+Training:  72%|████████████████████████████████████▊              | 21679/30000 [43:58<15:21,  9.03step/s]INFO 2026-05-19 01:55:51 ot_train.py:488 step:22K smpl:694K ep:1K epch:8.66 loss:0.008 grdn:0.205 lr:1.8e-06 updt_s:0.059 data_s:0.060
+Training:  72%|████████████████████████████████████▉              | 21699/30000 [44:01<15:27,  8.95step/s]INFO 2026-05-19 01:55:53 ot_train.py:488 step:22K smpl:694K ep:1K epch:8.67 loss:0.009 grdn:0.233 lr:1.8e-06 updt_s:0.059 data_s:0.061
+Training:  72%|████████████████████████████████████▉              | 21719/30000 [44:03<15:23,  8.97step/s]INFO 2026-05-19 01:55:56 ot_train.py:488 step:22K smpl:695K ep:1K epch:8.68 loss:0.009 grdn:0.221 lr:1.8e-06 updt_s:0.061 data_s:0.059
+Training:  72%|████████████████████████████████████▉              | 21739/30000 [44:06<15:24,  8.94step/s]INFO 2026-05-19 01:55:58 ot_train.py:488 step:22K smpl:696K ep:1K epch:8.69 loss:0.009 grdn:0.206 lr:1.8e-06 updt_s:0.058 data_s:0.067
+Training:  73%|████████████████████████████████████▉              | 21759/30000 [44:08<14:51,  9.24step/s]INFO 2026-05-19 01:56:01 ot_train.py:488 step:22K smpl:696K ep:1K epch:8.69 loss:0.009 grdn:0.219 lr:1.8e-06 updt_s:0.058 data_s:0.058
+Training:  73%|█████████████████████████████████████              | 21779/30000 [44:10<14:48,  9.26step/s]INFO 2026-05-19 01:56:03 ot_train.py:488 step:22K smpl:697K ep:1K epch:8.70 loss:0.010 grdn:0.220 lr:1.8e-06 updt_s:0.058 data_s:0.060
+Training:  73%|█████████████████████████████████████              | 21799/30000 [44:13<14:30,  9.42step/s]INFO 2026-05-19 01:56:05 ot_train.py:488 step:22K smpl:698K ep:1K epch:8.71 loss:0.010 grdn:0.222 lr:1.7e-06 updt_s:0.058 data_s:0.056
+Training:  73%|█████████████████████████████████████              | 21819/30000 [44:15<14:42,  9.27step/s]INFO 2026-05-19 01:56:08 ot_train.py:488 step:22K smpl:698K ep:1K epch:8.72 loss:0.010 grdn:0.263 lr:1.7e-06 updt_s:0.058 data_s:0.058
+Training:  73%|█████████████████████████████████████▏             | 21839/30000 [44:17<14:15,  9.54step/s]INFO 2026-05-19 01:56:10 ot_train.py:488 step:22K smpl:699K ep:1K epch:8.73 loss:0.009 grdn:0.214 lr:1.7e-06 updt_s:0.058 data_s:0.057
+Training:  73%|█████████████████████████████████████▏             | 21859/30000 [44:20<14:57,  9.07step/s]INFO 2026-05-19 01:56:12 ot_train.py:488 step:22K smpl:700K ep:1K epch:8.73 loss:0.010 grdn:0.203 lr:1.7e-06 updt_s:0.058 data_s:0.060
+Training:  73%|█████████████████████████████████████▏             | 21879/30000 [44:22<15:00,  9.02step/s]INFO 2026-05-19 01:56:15 ot_train.py:488 step:22K smpl:700K ep:1K epch:8.74 loss:0.009 grdn:0.207 lr:1.7e-06 updt_s:0.059 data_s:0.059
+Training:  73%|█████████████████████████████████████▏             | 21899/30000 [44:24<14:54,  9.06step/s]INFO 2026-05-19 01:56:17 ot_train.py:488 step:22K smpl:701K ep:1K epch:8.75 loss:0.010 grdn:0.217 lr:1.7e-06 updt_s:0.059 data_s:0.059
+Training:  73%|█████████████████████████████████████▎             | 21919/30000 [44:27<14:58,  9.00step/s]INFO 2026-05-19 01:56:19 ot_train.py:488 step:22K smpl:701K ep:1K epch:8.76 loss:0.009 grdn:0.229 lr:1.7e-06 updt_s:0.059 data_s:0.059
+Training:  73%|█████████████████████████████████████▎             | 21939/30000 [44:29<15:07,  8.88step/s]INFO 2026-05-19 01:56:22 ot_train.py:488 step:22K smpl:702K ep:1K epch:8.77 loss:0.008 grdn:0.194 lr:1.7e-06 updt_s:0.061 data_s:0.059
+Training:  73%|█████████████████████████████████████▎             | 21959/30000 [44:32<14:55,  8.98step/s]INFO 2026-05-19 01:56:24 ot_train.py:488 step:22K smpl:703K ep:1K epch:8.77 loss:0.012 grdn:0.233 lr:1.7e-06 updt_s:0.061 data_s:0.058
+Training:  73%|█████████████████████████████████████▎             | 21979/30000 [44:34<14:52,  8.99step/s]INFO 2026-05-19 01:56:27 ot_train.py:488 step:22K smpl:703K ep:1K epch:8.78 loss:0.008 grdn:0.200 lr:1.7e-06 updt_s:0.059 data_s:0.062
+Training:  73%|█████████████████████████████████████▍             | 21999/30000 [44:36<14:26,  9.23step/s]INFO 2026-05-19 01:56:29 ot_train.py:488 step:22K smpl:704K ep:1K epch:8.79 loss:0.010 grdn:0.250 lr:1.7e-06 updt_s:0.060 data_s:0.059
+Training:  73%|█████████████████████████████████████▍             | 22019/30000 [44:39<14:58,  8.89step/s]INFO 2026-05-19 01:56:32 ot_train.py:488 step:22K smpl:705K ep:1K epch:8.80 loss:0.009 grdn:0.219 lr:1.7e-06 updt_s:0.060 data_s:0.059
+Training:  73%|█████████████████████████████████████▍             | 22039/30000 [44:41<14:52,  8.92step/s]INFO 2026-05-19 01:56:34 ot_train.py:488 step:22K smpl:705K ep:1K epch:8.81 loss:0.008 grdn:0.212 lr:1.7e-06 updt_s:0.059 data_s:0.060
+Training:  74%|█████████████████████████████████████▌             | 22059/30000 [44:44<14:06,  9.39step/s]INFO 2026-05-19 01:56:36 ot_train.py:488 step:22K smpl:706K ep:1K epch:8.81 loss:0.009 grdn:0.196 lr:1.6e-06 updt_s:0.060 data_s:0.056
+Training:  74%|█████████████████████████████████████▌             | 22079/30000 [44:46<14:28,  9.12step/s]INFO 2026-05-19 01:56:39 ot_train.py:488 step:22K smpl:707K ep:1K epch:8.82 loss:0.009 grdn:0.210 lr:1.6e-06 updt_s:0.061 data_s:0.058
+Training:  74%|█████████████████████████████████████▌             | 22099/30000 [44:48<14:50,  8.87step/s]INFO 2026-05-19 01:56:41 ot_train.py:488 step:22K smpl:707K ep:1K epch:8.83 loss:0.010 grdn:0.246 lr:1.6e-06 updt_s:0.062 data_s:0.058
+Training:  74%|█████████████████████████████████████▌             | 22119/30000 [44:51<14:35,  9.01step/s]INFO 2026-05-19 01:56:43 ot_train.py:488 step:22K smpl:708K ep:1K epch:8.84 loss:0.010 grdn:0.226 lr:1.6e-06 updt_s:0.060 data_s:0.059
+Training:  74%|█████████████████████████████████████▋             | 22139/30000 [44:53<14:41,  8.92step/s]INFO 2026-05-19 01:56:46 ot_train.py:488 step:22K smpl:708K ep:1K epch:8.85 loss:0.010 grdn:0.212 lr:1.6e-06 updt_s:0.060 data_s:0.059
+Training:  74%|█████████████████████████████████████▋             | 22159/30000 [44:56<14:52,  8.78step/s]INFO 2026-05-19 01:56:48 ot_train.py:488 step:22K smpl:709K ep:1K epch:8.85 loss:0.010 grdn:0.233 lr:1.6e-06 updt_s:0.060 data_s:0.061
+Training:  74%|█████████████████████████████████████▋             | 22179/30000 [44:58<14:21,  9.08step/s]INFO 2026-05-19 01:56:51 ot_train.py:488 step:22K smpl:710K ep:1K epch:8.86 loss:0.009 grdn:0.235 lr:1.6e-06 updt_s:0.061 data_s:0.057
+Training:  74%|█████████████████████████████████████▋             | 22199/30000 [45:00<14:40,  8.86step/s]INFO 2026-05-19 01:56:53 ot_train.py:488 step:22K smpl:710K ep:1K epch:8.87 loss:0.009 grdn:0.224 lr:1.6e-06 updt_s:0.060 data_s:0.061
+Training:  74%|█████████████████████████████████████▊             | 22219/30000 [45:03<13:56,  9.30step/s]INFO 2026-05-19 01:56:55 ot_train.py:488 step:22K smpl:711K ep:1K epch:8.88 loss:0.008 grdn:0.206 lr:1.6e-06 updt_s:0.059 data_s:0.055
+Training:  74%|█████████████████████████████████████▊             | 22239/30000 [45:05<14:25,  8.97step/s]INFO 2026-05-19 01:56:58 ot_train.py:488 step:22K smpl:712K ep:1K epch:8.89 loss:0.010 grdn:0.229 lr:1.6e-06 updt_s:0.058 data_s:0.062
+Training:  74%|█████████████████████████████████████▊             | 22259/30000 [45:08<12:52, 10.02step/s]INFO 2026-05-19 01:57:00 ot_train.py:488 step:22K smpl:712K ep:1K epch:8.89 loss:0.009 grdn:0.201 lr:1.6e-06 updt_s:0.058 data_s:0.056
+Training:  74%|█████████████████████████████████████▊             | 22279/30000 [45:10<17:18,  7.43step/s]INFO 2026-05-19 01:57:03 ot_train.py:488 step:22K smpl:713K ep:1K epch:8.90 loss:0.009 grdn:0.220 lr:1.6e-06 updt_s:0.057 data_s:0.085
+Training:  74%|█████████████████████████████████████▉             | 22299/30000 [45:13<16:25,  7.81step/s]INFO 2026-05-19 01:57:05 ot_train.py:488 step:22K smpl:714K ep:1K epch:8.91 loss:0.008 grdn:0.200 lr:1.6e-06 updt_s:0.059 data_s:0.056
+Training:  74%|█████████████████████████████████████▉             | 22319/30000 [45:15<15:49,  8.09step/s]INFO 2026-05-19 01:57:08 ot_train.py:488 step:22K smpl:714K ep:1K epch:8.92 loss:0.009 grdn:0.210 lr:1.5e-06 updt_s:0.060 data_s:0.060
+Training:  74%|█████████████████████████████████████▉             | 22339/30000 [45:17<16:42,  7.64step/s]INFO 2026-05-19 01:57:10 ot_train.py:488 step:22K smpl:715K ep:1K epch:8.93 loss:0.009 grdn:0.211 lr:1.5e-06 updt_s:0.059 data_s:0.060
+Training:  75%|██████████████████████████████████████             | 22359/30000 [45:20<16:23,  7.77step/s]INFO 2026-05-19 01:57:13 ot_train.py:488 step:22K smpl:716K ep:1K epch:8.93 loss:0.010 grdn:0.223 lr:1.5e-06 updt_s:0.059 data_s:0.060
+Training:  75%|██████████████████████████████████████             | 22379/30000 [45:22<16:22,  7.76step/s]INFO 2026-05-19 01:57:15 ot_train.py:488 step:22K smpl:716K ep:1K epch:8.94 loss:0.009 grdn:0.251 lr:1.5e-06 updt_s:0.059 data_s:0.061
+Training:  75%|██████████████████████████████████████             | 22399/30000 [45:25<16:29,  7.68step/s]INFO 2026-05-19 01:57:18 ot_train.py:488 step:22K smpl:717K ep:1K epch:8.95 loss:0.010 grdn:0.221 lr:1.5e-06 updt_s:0.059 data_s:0.069
+Training:  75%|██████████████████████████████████████             | 22419/30000 [45:27<16:11,  7.81step/s]INFO 2026-05-19 01:57:20 ot_train.py:488 step:22K smpl:717K ep:1K epch:8.96 loss:0.010 grdn:0.253 lr:1.5e-06 updt_s:0.060 data_s:0.058
+Training:  75%|██████████████████████████████████████▏            | 22439/30000 [45:30<16:09,  7.80step/s]INFO 2026-05-19 01:57:22 ot_train.py:488 step:22K smpl:718K ep:1K epch:8.97 loss:0.007 grdn:0.204 lr:1.5e-06 updt_s:0.060 data_s:0.058
+Training:  75%|██████████████████████████████████████▏            | 22459/30000 [45:32<16:01,  7.84step/s]INFO 2026-05-19 01:57:25 ot_train.py:488 step:22K smpl:719K ep:1K epch:8.97 loss:0.010 grdn:0.221 lr:1.5e-06 updt_s:0.061 data_s:0.059
+Training:  75%|██████████████████████████████████████▏            | 22479/30000 [45:34<16:04,  7.79step/s]INFO 2026-05-19 01:57:27 ot_train.py:488 step:22K smpl:719K ep:1K epch:8.98 loss:0.008 grdn:0.209 lr:1.5e-06 updt_s:0.059 data_s:0.059
+Training:  75%|██████████████████████████████████████▏            | 22499/30000 [45:37<16:13,  7.70step/s]INFO 2026-05-19 01:57:29 ot_train.py:488 step:22K smpl:720K ep:1K epch:8.99 loss:0.009 grdn:0.213 lr:1.5e-06 updt_s:0.059 data_s:0.060
+Training:  75%|██████████████████████████████████████▎            | 22519/30000 [45:39<16:23,  7.61step/s]INFO 2026-05-19 01:57:32 ot_train.py:488 step:23K smpl:721K ep:1K epch:9.00 loss:0.007 grdn:0.193 lr:1.5e-06 updt_s:0.060 data_s:0.059
+Training:  75%|██████████████████████████████████████▎            | 22539/30000 [45:42<16:56,  7.34step/s]INFO 2026-05-19 01:57:34 ot_train.py:488 step:23K smpl:721K ep:1K epch:9.01 loss:0.009 grdn:0.211 lr:1.5e-06 updt_s:0.061 data_s:0.060
+Training:  75%|██████████████████████████████████████▎            | 22559/30000 [45:44<16:00,  7.75step/s]INFO 2026-05-19 01:57:37 ot_train.py:488 step:23K smpl:722K ep:1K epch:9.01 loss:0.008 grdn:0.182 lr:1.5e-06 updt_s:0.060 data_s:0.057
+Training:  75%|██████████████████████████████████████▍            | 22579/30000 [45:46<17:19,  7.14step/s]INFO 2026-05-19 01:57:39 ot_train.py:488 step:23K smpl:723K ep:1K epch:9.02 loss:0.008 grdn:0.196 lr:1.4e-06 updt_s:0.060 data_s:0.065
+Training:  75%|██████████████████████████████████████▍            | 22599/30000 [45:49<16:26,  7.50step/s]INFO 2026-05-19 01:57:42 ot_train.py:488 step:23K smpl:723K ep:1K epch:9.03 loss:0.009 grdn:0.205 lr:1.4e-06 updt_s:0.059 data_s:0.063
+Training:  75%|██████████████████████████████████████▍            | 22619/30000 [45:51<16:09,  7.61step/s]INFO 2026-05-19 01:57:44 ot_train.py:488 step:23K smpl:724K ep:1K epch:9.04 loss:0.010 grdn:0.240 lr:1.4e-06 updt_s:0.058 data_s:0.064
+Training:  75%|██████████████████████████████████████▍            | 22639/30000 [45:54<15:31,  7.91step/s]INFO 2026-05-19 01:57:46 ot_train.py:488 step:23K smpl:724K ep:1K epch:9.05 loss:0.010 grdn:0.226 lr:1.4e-06 updt_s:0.059 data_s:0.059
+Training:  76%|██████████████████████████████████████▌            | 22659/30000 [45:56<15:35,  7.84step/s]INFO 2026-05-19 01:57:49 ot_train.py:488 step:23K smpl:725K ep:1K epch:9.05 loss:0.009 grdn:0.205 lr:1.4e-06 updt_s:0.059 data_s:0.058
+Training:  76%|██████████████████████████████████████▌            | 22679/30000 [45:59<15:39,  7.80step/s]INFO 2026-05-19 01:57:51 ot_train.py:488 step:23K smpl:726K ep:1K epch:9.06 loss:0.010 grdn:0.215 lr:1.4e-06 updt_s:0.059 data_s:0.060
+Training:  76%|██████████████████████████████████████▌            | 22699/30000 [46:01<16:12,  7.51step/s]INFO 2026-05-19 01:57:54 ot_train.py:488 step:23K smpl:726K ep:1K epch:9.07 loss:0.012 grdn:0.231 lr:1.4e-06 updt_s:0.059 data_s:0.062
+Training:  76%|██████████████████████████████████████▌            | 22719/30000 [46:03<15:38,  7.75step/s]INFO 2026-05-19 01:57:56 ot_train.py:488 step:23K smpl:727K ep:1K epch:9.08 loss:0.009 grdn:0.254 lr:1.4e-06 updt_s:0.059 data_s:0.059
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+Training:  76%|██████████████████████████████████████▋            | 22759/30000 [46:08<15:52,  7.60step/s]INFO 2026-05-19 01:58:01 ot_train.py:488 step:23K smpl:728K ep:1K epch:9.09 loss:0.010 grdn:0.235 lr:1.4e-06 updt_s:0.060 data_s:0.058
+Training:  76%|██████████████████████████████████████▋            | 22779/30000 [46:10<15:54,  7.57step/s]INFO 2026-05-19 01:58:03 ot_train.py:488 step:23K smpl:729K ep:1K epch:9.10 loss:0.010 grdn:0.220 lr:1.4e-06 updt_s:0.060 data_s:0.060
+Training:  76%|██████████████████████████████████████▊            | 22799/30000 [46:13<16:09,  7.43step/s]INFO 2026-05-19 01:58:06 ot_train.py:488 step:23K smpl:730K ep:1K epch:9.11 loss:0.010 grdn:0.231 lr:1.4e-06 updt_s:0.061 data_s:0.062
+Training:  76%|██████████████████████████████████████▊            | 22819/30000 [46:15<15:39,  7.65step/s]INFO 2026-05-19 01:58:08 ot_train.py:488 step:23K smpl:730K ep:1K epch:9.12 loss:0.009 grdn:0.201 lr:1.4e-06 updt_s:0.064 data_s:0.059
+Training:  76%|██████████████████████████████████████▊            | 22839/30000 [46:18<15:57,  7.48step/s]INFO 2026-05-19 01:58:11 ot_train.py:488 step:23K smpl:731K ep:1K epch:9.13 loss:0.009 grdn:0.230 lr:1.4e-06 updt_s:0.062 data_s:0.060
+Training:  76%|██████████████████████████████████████▊            | 22859/30000 [46:20<15:39,  7.60step/s]INFO 2026-05-19 01:58:13 ot_train.py:488 step:23K smpl:732K ep:1K epch:9.13 loss:0.009 grdn:0.221 lr:1.3e-06 updt_s:0.060 data_s:0.062
+Training:  76%|██████████████████████████████████████▉            | 22879/30000 [46:23<15:26,  7.69step/s]INFO 2026-05-19 01:58:16 ot_train.py:488 step:23K smpl:732K ep:1K epch:9.14 loss:0.009 grdn:0.218 lr:1.3e-06 updt_s:0.062 data_s:0.059
+Training:  76%|██████████████████████████████████████▉            | 22899/30000 [46:25<15:47,  7.49step/s]INFO 2026-05-19 01:58:18 ot_train.py:488 step:23K smpl:733K ep:1K epch:9.15 loss:0.008 grdn:0.191 lr:1.3e-06 updt_s:0.060 data_s:0.064
+Training:  76%|██████████████████████████████████████▉            | 22919/30000 [46:28<15:18,  7.71step/s]INFO 2026-05-19 01:58:20 ot_train.py:488 step:23K smpl:733K ep:1K epch:9.16 loss:0.009 grdn:0.231 lr:1.3e-06 updt_s:0.060 data_s:0.061
+Training:  76%|██████████████████████████████████████▉            | 22939/30000 [46:30<14:41,  8.01step/s]INFO 2026-05-19 01:58:23 ot_train.py:488 step:23K smpl:734K ep:1K epch:9.17 loss:0.008 grdn:0.206 lr:1.3e-06 updt_s:0.058 data_s:0.060
+Training:  77%|███████████████████████████████████████            | 22959/30000 [46:32<15:01,  7.81step/s]INFO 2026-05-19 01:58:25 ot_train.py:488 step:23K smpl:735K ep:1K epch:9.17 loss:0.009 grdn:0.201 lr:1.3e-06 updt_s:0.059 data_s:0.058
+Training:  77%|███████████████████████████████████████            | 22979/30000 [46:35<14:59,  7.81step/s]INFO 2026-05-19 01:58:28 ot_train.py:488 step:23K smpl:735K ep:1K epch:9.18 loss:0.009 grdn:0.216 lr:1.3e-06 updt_s:0.060 data_s:0.060
+Training:  77%|███████████████████████████████████████            | 22999/30000 [46:37<15:11,  7.68step/s]INFO 2026-05-19 01:58:30 ot_train.py:488 step:23K smpl:736K ep:1K epch:9.19 loss:0.008 grdn:0.203 lr:1.3e-06 updt_s:0.059 data_s:0.062
+Training:  77%|███████████████████████████████████████▏           | 23019/30000 [46:40<14:56,  7.79step/s]INFO 2026-05-19 01:58:32 ot_train.py:488 step:23K smpl:737K ep:1K epch:9.20 loss:0.010 grdn:0.224 lr:1.3e-06 updt_s:0.060 data_s:0.057
+Training:  77%|███████████████████████████████████████▏           | 23039/30000 [46:42<15:14,  7.61step/s]INFO 2026-05-19 01:58:35 ot_train.py:488 step:23K smpl:737K ep:1K epch:9.21 loss:0.007 grdn:0.183 lr:1.3e-06 updt_s:0.060 data_s:0.062
+Training:  77%|███████████████████████████████████████▏           | 23059/30000 [46:44<14:56,  7.74step/s]INFO 2026-05-19 01:58:37 ot_train.py:488 step:23K smpl:738K ep:1K epch:9.21 loss:0.009 grdn:0.218 lr:1.3e-06 updt_s:0.060 data_s:0.057
+Training:  77%|███████████████████████████████████████▏           | 23079/30000 [46:47<14:56,  7.72step/s]INFO 2026-05-19 01:58:40 ot_train.py:488 step:23K smpl:739K ep:1K epch:9.22 loss:0.009 grdn:0.218 lr:1.3e-06 updt_s:0.060 data_s:0.058
+Training:  77%|███████████████████████████████████████▎           | 23099/30000 [46:49<15:10,  7.58step/s]INFO 2026-05-19 01:58:42 ot_train.py:488 step:23K smpl:739K ep:1K epch:9.23 loss:0.009 grdn:0.228 lr:1.3e-06 updt_s:0.059 data_s:0.063
+Training:  77%|███████████████████████████████████████▎           | 23119/30000 [46:52<14:49,  7.74step/s]INFO 2026-05-19 01:58:44 ot_train.py:488 step:23K smpl:740K ep:1K epch:9.24 loss:0.010 grdn:0.221 lr:1.3e-06 updt_s:0.059 data_s:0.058
+Training:  77%|███████████████████████████████████████▎           | 23139/30000 [46:54<15:02,  7.60step/s]INFO 2026-05-19 01:58:47 ot_train.py:488 step:23K smpl:740K ep:1K epch:9.25 loss:0.009 grdn:0.225 lr:1.2e-06 updt_s:0.060 data_s:0.060
+Training:  77%|███████████████████████████████████████▎           | 23159/30000 [46:56<14:18,  7.97step/s]INFO 2026-05-19 01:58:49 ot_train.py:488 step:23K smpl:741K ep:1K epch:9.25 loss:0.009 grdn:0.229 lr:1.2e-06 updt_s:0.059 data_s:0.058
+Training:  77%|███████████████████████████████████████▍           | 23179/30000 [46:59<15:08,  7.51step/s]INFO 2026-05-19 01:58:52 ot_train.py:488 step:23K smpl:742K ep:1K epch:9.26 loss:0.008 grdn:0.216 lr:1.2e-06 updt_s:0.060 data_s:0.063
+Training:  77%|███████████████████████████████████████▍           | 23199/30000 [47:01<14:31,  7.81step/s]INFO 2026-05-19 01:58:54 ot_train.py:488 step:23K smpl:742K ep:1K epch:9.27 loss:0.009 grdn:0.213 lr:1.2e-06 updt_s:0.060 data_s:0.059
+Training:  77%|███████████████████████████████████████▍           | 23219/30000 [47:04<14:21,  7.87step/s]INFO 2026-05-19 01:58:56 ot_train.py:488 step:23K smpl:743K ep:1K epch:9.28 loss:0.008 grdn:0.219 lr:1.2e-06 updt_s:0.060 data_s:0.058
+Training:  77%|███████████████████████████████████████▌           | 23240/30000 [47:06<14:16,  7.89step/s]INFO 2026-05-19 01:58:59 ot_train.py:488 step:23K smpl:744K ep:1K epch:9.29 loss:0.011 grdn:0.220 lr:1.2e-06 updt_s:0.065 data_s:0.055
+Training:  78%|███████████████████████████████████████▌           | 23260/30000 [47:09<15:13,  7.38step/s]INFO 2026-05-19 01:59:01 ot_train.py:488 step:23K smpl:744K ep:1K epch:9.29 loss:0.010 grdn:0.220 lr:1.2e-06 updt_s:0.058 data_s:0.065
+Training:  78%|███████████████████████████████████████▌           | 23280/30000 [47:11<14:57,  7.49step/s]INFO 2026-05-19 01:59:04 ot_train.py:488 step:23K smpl:745K ep:1K epch:9.30 loss:0.008 grdn:0.204 lr:1.2e-06 updt_s:0.060 data_s:0.064
+Training:  78%|███████████████████████████████████████▌           | 23300/30000 [47:14<14:40,  7.61step/s]INFO 2026-05-19 01:59:06 ot_train.py:488 step:23K smpl:746K ep:1K epch:9.31 loss:0.009 grdn:0.221 lr:1.2e-06 updt_s:0.060 data_s:0.059
+Training:  78%|███████████████████████████████████████▋           | 23320/30000 [47:16<14:23,  7.74step/s]INFO 2026-05-19 01:59:09 ot_train.py:488 step:23K smpl:746K ep:1K epch:9.32 loss:0.009 grdn:0.207 lr:1.2e-06 updt_s:0.059 data_s:0.059
+Training:  78%|███████████████████████████████████████▋           | 23340/30000 [47:18<14:34,  7.62step/s]INFO 2026-05-19 01:59:11 ot_train.py:488 step:23K smpl:747K ep:1K epch:9.33 loss:0.010 grdn:0.219 lr:1.2e-06 updt_s:0.059 data_s:0.065
+Training:  78%|███████████████████████████████████████▋           | 23360/30000 [47:21<14:08,  7.82step/s]INFO 2026-05-19 01:59:13 ot_train.py:488 step:23K smpl:748K ep:1K epch:9.33 loss:0.008 grdn:0.217 lr:1.2e-06 updt_s:0.058 data_s:0.060
+Training:  78%|███████████████████████████████████████▋           | 23380/30000 [47:23<14:18,  7.71step/s]INFO 2026-05-19 01:59:16 ot_train.py:488 step:23K smpl:748K ep:1K epch:9.34 loss:0.009 grdn:0.207 lr:1.2e-06 updt_s:0.058 data_s:0.061
+Training:  78%|███████████████████████████████████████▊           | 23400/30000 [47:26<14:34,  7.55step/s]INFO 2026-05-19 01:59:18 ot_train.py:488 step:23K smpl:749K ep:1K epch:9.35 loss:0.009 grdn:0.205 lr:1.2e-06 updt_s:0.060 data_s:0.061
+Training:  78%|███████████████████████████████████████▊           | 23420/30000 [47:28<14:09,  7.75step/s]INFO 2026-05-19 01:59:21 ot_train.py:488 step:23K smpl:749K ep:1K epch:9.36 loss:0.008 grdn:0.198 lr:1.2e-06 updt_s:0.058 data_s:0.060
+Training:  78%|███████████████████████████████████████▊           | 23440/30000 [47:30<13:56,  7.84step/s]INFO 2026-05-19 01:59:23 ot_train.py:488 step:23K smpl:750K ep:1K epch:9.37 loss:0.009 grdn:0.197 lr:1.1e-06 updt_s:0.058 data_s:0.058
+Training:  78%|███████████████████████████████████████▉           | 23460/30000 [47:33<13:33,  8.04step/s]INFO 2026-05-19 01:59:25 ot_train.py:488 step:23K smpl:751K ep:1K epch:9.37 loss:0.009 grdn:0.206 lr:1.1e-06 updt_s:0.060 data_s:0.058
+Training:  78%|███████████████████████████████████████▉           | 23480/30000 [47:35<13:53,  7.83step/s]INFO 2026-05-19 01:59:28 ot_train.py:488 step:23K smpl:751K ep:1K epch:9.38 loss:0.009 grdn:0.220 lr:1.1e-06 updt_s:0.063 data_s:0.056
+Training:  78%|███████████████████████████████████████▉           | 23500/30000 [47:38<14:06,  7.68step/s]INFO 2026-05-19 01:59:30 ot_train.py:488 step:24K smpl:752K ep:1K epch:9.39 loss:0.009 grdn:0.207 lr:1.1e-06 updt_s:0.061 data_s:0.059
+Training:  78%|███████████████████████████████████████▉           | 23520/30000 [47:40<13:57,  7.74step/s]INFO 2026-05-19 01:59:33 ot_train.py:488 step:24K smpl:753K ep:1K epch:9.40 loss:0.010 grdn:0.219 lr:1.1e-06 updt_s:0.061 data_s:0.060
+Training:  78%|████████████████████████████████████████           | 23540/30000 [47:42<14:02,  7.67step/s]INFO 2026-05-19 01:59:35 ot_train.py:488 step:24K smpl:753K ep:1K epch:9.41 loss:0.008 grdn:0.206 lr:1.1e-06 updt_s:0.061 data_s:0.060
+Training:  79%|████████████████████████████████████████           | 23560/30000 [47:45<13:09,  8.16step/s]INFO 2026-05-19 01:59:37 ot_train.py:488 step:24K smpl:754K ep:1K epch:9.41 loss:0.008 grdn:0.202 lr:1.1e-06 updt_s:0.061 data_s:0.054
+Training:  79%|████████████████████████████████████████           | 23580/30000 [47:47<14:08,  7.56step/s]INFO 2026-05-19 01:59:40 ot_train.py:488 step:24K smpl:755K ep:1K epch:9.42 loss:0.010 grdn:0.244 lr:1.1e-06 updt_s:0.059 data_s:0.061
+Training:  79%|████████████████████████████████████████           | 23600/30000 [47:50<13:56,  7.65step/s]INFO 2026-05-19 01:59:42 ot_train.py:488 step:24K smpl:755K ep:1K epch:9.43 loss:0.011 grdn:0.257 lr:1.1e-06 updt_s:0.060 data_s:0.060
+Training:  79%|████████████████████████████████████████▏          | 23620/30000 [47:52<14:47,  7.18step/s]INFO 2026-05-19 01:59:45 ot_train.py:488 step:24K smpl:756K ep:1K epch:9.44 loss:0.009 grdn:0.199 lr:1.1e-06 updt_s:0.059 data_s:0.069
+Training:  79%|████████████████████████████████████████▏          | 23640/30000 [47:55<13:36,  7.79step/s]INFO 2026-05-19 01:59:47 ot_train.py:488 step:24K smpl:756K ep:1K epch:9.45 loss:0.009 grdn:0.207 lr:1.1e-06 updt_s:0.060 data_s:0.059
+Training:  79%|████████████████████████████████████████▏          | 23660/30000 [47:57<13:34,  7.79step/s]INFO 2026-05-19 01:59:50 ot_train.py:488 step:24K smpl:757K ep:1K epch:9.45 loss:0.008 grdn:0.212 lr:1.1e-06 updt_s:0.060 data_s:0.058
+Training:  79%|████████████████████████████████████████▎          | 23680/30000 [47:59<13:54,  7.57step/s]INFO 2026-05-19 01:59:52 ot_train.py:488 step:24K smpl:758K ep:1K epch:9.46 loss:0.010 grdn:0.226 lr:1.1e-06 updt_s:0.060 data_s:0.058
+Training:  79%|████████████████████████████████████████▎          | 23700/30000 [48:02<13:36,  7.72step/s]INFO 2026-05-19 01:59:54 ot_train.py:488 step:24K smpl:758K ep:1K epch:9.47 loss:0.009 grdn:0.222 lr:1.1e-06 updt_s:0.060 data_s:0.058
+Training:  79%|████████████████████████████████████████▎          | 23720/30000 [48:04<13:30,  7.75step/s]INFO 2026-05-19 01:59:57 ot_train.py:488 step:24K smpl:759K ep:1K epch:9.48 loss:0.009 grdn:0.223 lr:1.1e-06 updt_s:0.060 data_s:0.057
+Training:  79%|████████████████████████████████████████▎          | 23740/30000 [48:06<13:22,  7.80step/s]INFO 2026-05-19 01:59:59 ot_train.py:488 step:24K smpl:760K ep:1K epch:9.49 loss:0.007 grdn:0.184 lr:1.0e-06 updt_s:0.061 data_s:0.056
+Training:  79%|████████████████████████████████████████▍          | 23760/30000 [48:09<13:35,  7.65step/s]INFO 2026-05-19 02:00:01 ot_train.py:488 step:24K smpl:760K ep:1K epch:9.49 loss:0.009 grdn:0.218 lr:1.0e-06 updt_s:0.060 data_s:0.059
+Training:  79%|████████████████████████████████████████▍          | 23780/30000 [48:11<13:31,  7.66step/s]INFO 2026-05-19 02:00:04 ot_train.py:488 step:24K smpl:761K ep:1K epch:9.50 loss:0.009 grdn:0.225 lr:1.0e-06 updt_s:0.061 data_s:0.059
+Training:  79%|████████████████████████████████████████▍          | 23800/30000 [48:14<13:00,  7.95step/s]INFO 2026-05-19 02:00:06 ot_train.py:488 step:24K smpl:762K ep:1K epch:9.51 loss:0.008 grdn:0.217 lr:1.0e-06 updt_s:0.059 data_s:0.058
+Training:  79%|████████████████████████████████████████▍          | 23820/30000 [48:16<13:03,  7.89step/s]INFO 2026-05-19 02:00:09 ot_train.py:488 step:24K smpl:762K ep:1K epch:9.52 loss:0.008 grdn:0.211 lr:1.0e-06 updt_s:0.059 data_s:0.059
+Training:  79%|████████████████████████████████████████▌          | 23840/30000 [48:18<13:22,  7.67step/s]INFO 2026-05-19 02:00:11 ot_train.py:488 step:24K smpl:763K ep:1K epch:9.53 loss:0.011 grdn:0.219 lr:1.0e-06 updt_s:0.058 data_s:0.060
+Training:  80%|████████████████████████████████████████▌          | 23860/30000 [48:21<13:03,  7.84step/s]INFO 2026-05-19 02:00:13 ot_train.py:488 step:24K smpl:764K ep:1K epch:9.53 loss:0.010 grdn:0.225 lr:1.0e-06 updt_s:0.058 data_s:0.060
+Training:  80%|████████████████████████████████████████▌          | 23880/30000 [48:23<12:46,  7.98step/s]INFO 2026-05-19 02:00:16 ot_train.py:488 step:24K smpl:764K ep:1K epch:9.54 loss:0.008 grdn:0.225 lr:1.0e-06 updt_s:0.058 data_s:0.058
+Training:  80%|████████████████████████████████████████▋          | 23900/30000 [48:25<13:02,  7.80step/s]INFO 2026-05-19 02:00:18 ot_train.py:488 step:24K smpl:765K ep:1K epch:9.55 loss:0.011 grdn:0.235 lr:1.0e-06 updt_s:0.060 data_s:0.059
+Training:  80%|████████████████████████████████████████▋          | 23920/30000 [48:28<12:47,  7.92step/s]INFO 2026-05-19 02:00:20 ot_train.py:488 step:24K smpl:765K ep:1K epch:9.56 loss:0.009 grdn:0.208 lr:9.9e-07 updt_s:0.060 data_s:0.057
+Training:  80%|████████████████████████████████████████▋          | 23940/30000 [48:30<12:59,  7.77step/s]INFO 2026-05-19 02:00:23 ot_train.py:488 step:24K smpl:766K ep:1K epch:9.57 loss:0.011 grdn:0.258 lr:9.8e-07 updt_s:0.059 data_s:0.058
+Training:  80%|████████████████████████████████████████▋          | 23960/30000 [48:33<13:26,  7.49step/s]INFO 2026-05-19 02:00:25 ot_train.py:488 step:24K smpl:767K ep:1K epch:9.57 loss:0.009 grdn:0.218 lr:9.8e-07 updt_s:0.060 data_s:0.059
+Training:  80%|████████████████████████████████████████▊          | 23980/30000 [48:35<12:45,  7.86step/s]INFO 2026-05-19 02:00:28 ot_train.py:488 step:24K smpl:767K ep:1K epch:9.58 loss:0.010 grdn:0.218 lr:9.7e-07 updt_s:0.061 data_s:0.056
+Training:  80%|████████████████████████████████████████▊          | 24000/30000 [48:37<12:40,  7.89step/s]INFO 2026-05-19 02:00:30 ot_train.py:488 step:24K smpl:768K ep:1K epch:9.59 loss:0.010 grdn:0.217 lr:9.6e-07 updt_s:0.058 data_s:0.061
+Training:  80%|████████████████████████████████████████▊          | 24020/30000 [48:40<13:46,  7.24step/s]INFO 2026-05-19 02:00:33 ot_train.py:488 step:24K smpl:769K ep:1K epch:9.60 loss:0.007 grdn:0.197 lr:9.6e-07 updt_s:0.059 data_s:0.079
+Training:  80%|████████████████████████████████████████▊          | 24040/30000 [48:42<12:45,  7.78step/s]INFO 2026-05-19 02:00:35 ot_train.py:488 step:24K smpl:769K ep:1K epch:9.61 loss:0.010 grdn:0.215 lr:9.5e-07 updt_s:0.061 data_s:0.057
+Training:  80%|████████████████████████████████████████▉          | 24060/30000 [48:45<12:33,  7.88step/s]INFO 2026-05-19 02:00:37 ot_train.py:488 step:24K smpl:770K ep:1K epch:9.61 loss:0.008 grdn:0.200 lr:9.5e-07 updt_s:0.060 data_s:0.057
+Training:  80%|████████████████████████████████████████▉          | 24080/30000 [48:47<15:46,  6.26step/s]INFO 2026-05-19 02:00:40 ot_train.py:488 step:24K smpl:771K ep:1K epch:9.62 loss:0.009 grdn:0.226 lr:9.4e-07 updt_s:0.059 data_s:0.069
+Training:  80%|████████████████████████████████████████▉          | 24100/30000 [48:50<12:53,  7.62step/s]INFO 2026-05-19 02:00:42 ot_train.py:488 step:24K smpl:771K ep:1K epch:9.63 loss:0.009 grdn:0.197 lr:9.3e-07 updt_s:0.058 data_s:0.059
+Training:  80%|█████████████████████████████████████████          | 24120/30000 [48:52<12:16,  7.98step/s]INFO 2026-05-19 02:00:45 ot_train.py:488 step:24K smpl:772K ep:1K epch:9.64 loss:0.010 grdn:0.227 lr:9.3e-07 updt_s:0.059 data_s:0.058
+Training:  80%|█████████████████████████████████████████          | 24140/30000 [48:55<12:19,  7.93step/s]INFO 2026-05-19 02:00:47 ot_train.py:488 step:24K smpl:772K ep:1K epch:9.65 loss:0.009 grdn:0.223 lr:9.2e-07 updt_s:0.059 data_s:0.059
+Training:  81%|█████████████████████████████████████████          | 24160/30000 [48:57<12:49,  7.59step/s]INFO 2026-05-19 02:00:50 ot_train.py:488 step:24K smpl:773K ep:1K epch:9.65 loss:0.008 grdn:0.197 lr:9.1e-07 updt_s:0.060 data_s:0.061
+Training:  81%|█████████████████████████████████████████          | 24180/30000 [48:59<12:29,  7.77step/s]INFO 2026-05-19 02:00:52 ot_train.py:488 step:24K smpl:774K ep:1K epch:9.66 loss:0.010 grdn:0.232 lr:9.1e-07 updt_s:0.060 data_s:0.059
+Training:  81%|█████████████████████████████████████████▏         | 24200/30000 [49:02<12:40,  7.63step/s]INFO 2026-05-19 02:00:54 ot_train.py:488 step:24K smpl:774K ep:1K epch:9.67 loss:0.009 grdn:0.206 lr:9.0e-07 updt_s:0.059 data_s:0.059
+Training:  81%|█████████████████████████████████████████▏         | 24220/30000 [49:04<11:53,  8.10step/s]INFO 2026-05-19 02:00:57 ot_train.py:488 step:24K smpl:775K ep:1K epch:9.68 loss:0.009 grdn:0.227 lr:9.0e-07 updt_s:0.058 data_s:0.055
+Training:  81%|█████████████████████████████████████████▏         | 24240/30000 [49:06<12:17,  7.81step/s]INFO 2026-05-19 02:00:59 ot_train.py:488 step:24K smpl:776K ep:1K epch:9.69 loss:0.009 grdn:0.204 lr:8.9e-07 updt_s:0.058 data_s:0.059
+Training:  81%|█████████████████████████████████████████▏         | 24260/30000 [49:09<11:43,  8.16step/s]INFO 2026-05-19 02:01:01 ot_train.py:488 step:24K smpl:776K ep:1K epch:9.69 loss:0.010 grdn:0.253 lr:8.8e-07 updt_s:0.058 data_s:0.057
+Training:  81%|█████████████████████████████████████████▎         | 24280/30000 [49:11<11:57,  7.98step/s]INFO 2026-05-19 02:01:04 ot_train.py:488 step:24K smpl:777K ep:1K epch:9.70 loss:0.010 grdn:0.251 lr:8.8e-07 updt_s:0.058 data_s:0.058
+Training:  81%|█████████████████████████████████████████▎         | 24300/30000 [49:13<12:05,  7.85step/s]INFO 2026-05-19 02:01:06 ot_train.py:488 step:24K smpl:778K ep:1K epch:9.71 loss:0.009 grdn:0.240 lr:8.7e-07 updt_s:0.058 data_s:0.059
+Training:  81%|█████████████████████████████████████████▎         | 24320/30000 [49:16<12:14,  7.73step/s]INFO 2026-05-19 02:01:08 ot_train.py:488 step:24K smpl:778K ep:1K epch:9.72 loss:0.008 grdn:0.184 lr:8.7e-07 updt_s:0.058 data_s:0.060
+Training:  81%|█████████████████████████████████████████▍         | 24340/30000 [49:18<11:48,  7.99step/s]INFO 2026-05-19 02:01:11 ot_train.py:488 step:24K smpl:779K ep:1K epch:9.73 loss:0.009 grdn:0.204 lr:8.6e-07 updt_s:0.058 data_s:0.058
+Training:  81%|█████████████████████████████████████████▍         | 24360/30000 [49:20<12:13,  7.69step/s]INFO 2026-05-19 02:01:13 ot_train.py:488 step:24K smpl:780K ep:1K epch:9.73 loss:0.008 grdn:0.212 lr:8.6e-07 updt_s:0.058 data_s:0.060
+Training:  81%|█████████████████████████████████████████▍         | 24380/30000 [49:23<12:37,  7.42step/s]INFO 2026-05-19 02:01:16 ot_train.py:488 step:24K smpl:780K ep:1K epch:9.74 loss:0.011 grdn:0.263 lr:8.5e-07 updt_s:0.059 data_s:0.063
+Training:  81%|█████████████████████████████████████████▍         | 24400/30000 [49:25<12:18,  7.58step/s]INFO 2026-05-19 02:01:18 ot_train.py:488 step:24K smpl:781K ep:1K epch:9.75 loss:0.009 grdn:0.223 lr:8.4e-07 updt_s:0.059 data_s:0.061
+Training:  81%|█████████████████████████████████████████▌         | 24420/30000 [49:28<11:58,  7.77step/s]INFO 2026-05-19 02:01:20 ot_train.py:488 step:24K smpl:781K ep:1K epch:9.76 loss:0.008 grdn:0.224 lr:8.4e-07 updt_s:0.059 data_s:0.058
+Training:  81%|█████████████████████████████████████████▌         | 24440/30000 [49:30<11:57,  7.75step/s]INFO 2026-05-19 02:01:23 ot_train.py:488 step:24K smpl:782K ep:1K epch:9.77 loss:0.009 grdn:0.239 lr:8.3e-07 updt_s:0.058 data_s:0.060
+Training:  82%|█████████████████████████████████████████▌         | 24460/30000 [49:33<12:10,  7.58step/s]INFO 2026-05-19 02:01:25 ot_train.py:488 step:24K smpl:783K ep:1K epch:9.77 loss:0.009 grdn:0.192 lr:8.3e-07 updt_s:0.059 data_s:0.060
+Training:  82%|█████████████████████████████████████████▌         | 24480/30000 [49:35<12:04,  7.62step/s]INFO 2026-05-19 02:01:28 ot_train.py:488 step:24K smpl:783K ep:1K epch:9.78 loss:0.008 grdn:0.204 lr:8.2e-07 updt_s:0.060 data_s:0.061
+Training:  82%|█████████████████████████████████████████▋         | 24500/30000 [49:37<11:56,  7.68step/s]INFO 2026-05-19 02:01:30 ot_train.py:488 step:24K smpl:784K ep:1K epch:9.79 loss:0.010 grdn:0.223 lr:8.1e-07 updt_s:0.059 data_s:0.062
+Training:  82%|█████████████████████████████████████████▋         | 24520/30000 [49:40<11:56,  7.65step/s]INFO 2026-05-19 02:01:32 ot_train.py:488 step:25K smpl:785K ep:1K epch:9.80 loss:0.010 grdn:0.222 lr:8.1e-07 updt_s:0.059 data_s:0.060
+Training:  82%|█████████████████████████████████████████▋         | 24540/30000 [49:42<11:55,  7.63step/s]INFO 2026-05-19 02:01:35 ot_train.py:488 step:25K smpl:785K ep:1K epch:9.81 loss:0.009 grdn:0.202 lr:8.0e-07 updt_s:0.060 data_s:0.058
+Training:  82%|█████████████████████████████████████████▊         | 24560/30000 [49:45<11:58,  7.58step/s]INFO 2026-05-19 02:01:37 ot_train.py:488 step:25K smpl:786K ep:1K epch:9.81 loss:0.008 grdn:0.207 lr:8.0e-07 updt_s:0.059 data_s:0.063
+Training:  82%|█████████████████████████████████████████▊         | 24580/30000 [49:47<11:38,  7.76step/s]INFO 2026-05-19 02:01:40 ot_train.py:488 step:25K smpl:787K ep:1K epch:9.82 loss:0.009 grdn:0.210 lr:7.9e-07 updt_s:0.060 data_s:0.061
+Training:  82%|█████████████████████████████████████████▊         | 24600/30000 [49:50<12:08,  7.41step/s]INFO 2026-05-19 02:01:42 ot_train.py:488 step:25K smpl:787K ep:1K epch:9.83 loss:0.010 grdn:0.221 lr:7.9e-07 updt_s:0.059 data_s:0.063
+Training:  82%|█████████████████████████████████████████▊         | 24620/30000 [49:52<11:33,  7.76step/s]INFO 2026-05-19 02:01:44 ot_train.py:488 step:25K smpl:788K ep:1K epch:9.84 loss:0.009 grdn:0.215 lr:7.8e-07 updt_s:0.058 data_s:0.058
+Training:  82%|█████████████████████████████████████████▉         | 24640/30000 [49:54<11:31,  7.75step/s]INFO 2026-05-19 02:01:47 ot_train.py:488 step:25K smpl:788K ep:1K epch:9.85 loss:0.011 grdn:0.222 lr:7.7e-07 updt_s:0.059 data_s:0.059
+Training:  82%|█████████████████████████████████████████▉         | 24660/30000 [49:57<10:58,  8.11step/s]INFO 2026-05-19 02:01:49 ot_train.py:488 step:25K smpl:789K ep:1K epch:9.85 loss:0.009 grdn:0.209 lr:7.7e-07 updt_s:0.059 data_s:0.055
+Training:  82%|█████████████████████████████████████████▉         | 24680/30000 [49:59<11:46,  7.53step/s]INFO 2026-05-19 02:01:52 ot_train.py:488 step:25K smpl:790K ep:1K epch:9.86 loss:0.009 grdn:0.228 lr:7.6e-07 updt_s:0.058 data_s:0.064
+Training:  82%|█████████████████████████████████████████▉         | 24700/30000 [50:01<11:39,  7.58step/s]INFO 2026-05-19 02:01:54 ot_train.py:488 step:25K smpl:790K ep:1K epch:9.87 loss:0.008 grdn:0.213 lr:7.6e-07 updt_s:0.058 data_s:0.063
+Training:  82%|██████████████████████████████████████████         | 24720/30000 [50:04<11:12,  7.86step/s]INFO 2026-05-19 02:01:56 ot_train.py:488 step:25K smpl:791K ep:1K epch:9.88 loss:0.009 grdn:0.253 lr:7.5e-07 updt_s:0.058 data_s:0.059
+Training:  82%|██████████████████████████████████████████         | 24740/30000 [50:06<07:42, 11.38step/s]INFO 2026-05-19 02:01:58 ot_train.py:488 step:25K smpl:792K ep:1K epch:9.89 loss:0.009 grdn:0.226 lr:7.5e-07 updt_s:0.058 data_s:0.045
+Training:  83%|██████████████████████████████████████████         | 24759/30000 [50:09<10:10,  8.58step/s]INFO 2026-05-19 02:02:02 ot_train.py:488 step:25K smpl:792K ep:1K epch:9.89 loss:0.009 grdn:0.212 lr:7.4e-07 updt_s:0.059 data_s:0.091
+Training:  83%|██████████████████████████████████████████         | 24779/30000 [50:11<09:27,  9.19step/s]INFO 2026-05-19 02:02:04 ot_train.py:488 step:25K smpl:793K ep:1K epch:9.90 loss:0.010 grdn:0.232 lr:7.4e-07 updt_s:0.060 data_s:0.060
+Training:  83%|██████████████████████████████████████████▏        | 24799/30000 [50:14<09:28,  9.15step/s]INFO 2026-05-19 02:02:06 ot_train.py:488 step:25K smpl:794K ep:1K epch:9.91 loss:0.007 grdn:0.179 lr:7.3e-07 updt_s:0.059 data_s:0.061
+Training:  83%|██████████████████████████████████████████▏        | 24819/30000 [50:16<09:33,  9.04step/s]INFO 2026-05-19 02:02:09 ot_train.py:488 step:25K smpl:794K ep:1K epch:9.92 loss:0.009 grdn:0.215 lr:7.3e-07 updt_s:0.058 data_s:0.061
+Training:  83%|██████████████████████████████████████████▏        | 24839/30000 [50:18<09:33,  9.00step/s]INFO 2026-05-19 02:02:11 ot_train.py:488 step:25K smpl:795K ep:1K epch:9.93 loss:0.009 grdn:0.225 lr:7.2e-07 updt_s:0.059 data_s:0.061
+Training:  83%|██████████████████████████████████████████▎        | 24859/30000 [50:21<09:43,  8.81step/s]INFO 2026-05-19 02:02:14 ot_train.py:488 step:25K smpl:796K ep:1K epch:9.93 loss:0.010 grdn:0.236 lr:7.1e-07 updt_s:0.060 data_s:0.063
+Training:  83%|██████████████████████████████████████████▎        | 24879/30000 [50:23<09:34,  8.91step/s]INFO 2026-05-19 02:02:16 ot_train.py:488 step:25K smpl:796K ep:1K epch:9.94 loss:0.009 grdn:0.203 lr:7.1e-07 updt_s:0.061 data_s:0.059
+Training:  83%|██████████████████████████████████████████▎        | 24899/30000 [50:26<09:39,  8.80step/s]INFO 2026-05-19 02:02:19 ot_train.py:488 step:25K smpl:797K ep:1K epch:9.95 loss:0.011 grdn:0.246 lr:7.0e-07 updt_s:0.060 data_s:0.063
+Training:  83%|██████████████████████████████████████████▎        | 24919/30000 [50:28<09:00,  9.41step/s]INFO 2026-05-19 02:02:21 ot_train.py:488 step:25K smpl:797K ep:1K epch:9.96 loss:0.009 grdn:0.226 lr:7.0e-07 updt_s:0.062 data_s:0.055
+Training:  83%|██████████████████████████████████████████▍        | 24939/30000 [50:30<08:57,  9.41step/s]INFO 2026-05-19 02:02:23 ot_train.py:488 step:25K smpl:798K ep:1K epch:9.97 loss:0.009 grdn:0.218 lr:6.9e-07 updt_s:0.060 data_s:0.056
+Training:  83%|██████████████████████████████████████████▍        | 24959/30000 [50:33<09:13,  9.11step/s]INFO 2026-05-19 02:02:25 ot_train.py:488 step:25K smpl:799K ep:1K epch:9.97 loss:0.009 grdn:0.212 lr:6.9e-07 updt_s:0.061 data_s:0.053
+Training:  83%|██████████████████████████████████████████▍        | 24979/30000 [50:35<09:30,  8.80step/s]INFO 2026-05-19 02:02:28 ot_train.py:488 step:25K smpl:799K ep:1K epch:9.98 loss:0.011 grdn:0.259 lr:6.8e-07 updt_s:0.059 data_s:0.062
+Training:  83%|██████████████████████████████████████████▍        | 24999/30000 [50:38<09:07,  9.14step/s]INFO 2026-05-19 02:02:30 ot_train.py:488 step:25K smpl:800K ep:1K epch:9.99 loss:0.009 grdn:0.207 lr:6.8e-07 updt_s:0.060 data_s:0.059
+INFO 2026-05-19 02:02:30 ot_train.py:502 Checkpoint policy after step 25000
+Training:  83%|██████████████████████████████████████████▌        | 25019/30000 [50:46<09:26,  8.79step/s]INFO 2026-05-19 02:02:39 ot_train.py:488 step:25K smpl:801K ep:1K epch:10.00 loss:0.008 grdn:0.217 lr:6.7e-07 updt_s:0.060 data_s:0.006
+Training:  83%|██████████████████████████████████████████▌        | 25039/30000 [50:48<09:25,  8.77step/s]INFO 2026-05-19 02:02:41 ot_train.py:488 step:25K smpl:801K ep:1K epch:10.01 loss:0.008 grdn:0.206 lr:6.7e-07 updt_s:0.058 data_s:0.040
+Training:  84%|██████████████████████████████████████████▌        | 25059/30000 [50:51<09:56,  8.28step/s]INFO 2026-05-19 02:02:44 ot_train.py:488 step:25K smpl:802K ep:1K epch:10.01 loss:0.010 grdn:0.208 lr:6.6e-07 updt_s:0.059 data_s:0.065
+Training:  84%|██████████████████████████████████████████▋        | 25079/30000 [50:53<09:26,  8.69step/s]INFO 2026-05-19 02:02:46 ot_train.py:488 step:25K smpl:803K ep:1K epch:10.02 loss:0.009 grdn:0.196 lr:6.6e-07 updt_s:0.058 data_s:0.060
+Training:  84%|██████████████████████████████████████████▋        | 25099/30000 [50:56<08:56,  9.14step/s]INFO 2026-05-19 02:02:48 ot_train.py:488 step:25K smpl:803K ep:1K epch:10.03 loss:0.009 grdn:0.223 lr:6.5e-07 updt_s:0.058 data_s:0.058
+Training:  84%|██████████████████████████████████████████▋        | 25119/30000 [50:58<09:02,  8.99step/s]INFO 2026-05-19 02:02:51 ot_train.py:488 step:25K smpl:804K ep:1K epch:10.04 loss:0.009 grdn:0.226 lr:6.5e-07 updt_s:0.059 data_s:0.063
+Training:  84%|██████████████████████████████████████████▋        | 25139/30000 [51:01<09:08,  8.86step/s]INFO 2026-05-19 02:02:53 ot_train.py:488 step:25K smpl:804K ep:1K epch:10.05 loss:0.009 grdn:0.217 lr:6.4e-07 updt_s:0.058 data_s:0.061
+Training:  84%|██████████████████████████████████████████▊        | 25159/30000 [51:03<09:58,  8.08step/s]INFO 2026-05-19 02:02:56 ot_train.py:488 step:25K smpl:805K ep:1K epch:10.05 loss:0.009 grdn:0.198 lr:6.4e-07 updt_s:0.059 data_s:0.066
+Training:  84%|██████████████████████████████████████████▊        | 25179/30000 [51:05<09:10,  8.75step/s]INFO 2026-05-19 02:02:58 ot_train.py:488 step:25K smpl:806K ep:1K epch:10.06 loss:0.009 grdn:0.231 lr:6.3e-07 updt_s:0.059 data_s:0.061
+Training:  84%|██████████████████████████████████████████▊        | 25199/30000 [51:08<09:18,  8.60step/s]INFO 2026-05-19 02:03:01 ot_train.py:488 step:25K smpl:806K ep:1K epch:10.07 loss:0.009 grdn:0.227 lr:6.2e-07 updt_s:0.059 data_s:0.066
+Training:  84%|██████████████████████████████████████████▊        | 25219/30000 [51:10<09:13,  8.64step/s]INFO 2026-05-19 02:03:03 ot_train.py:488 step:25K smpl:807K ep:1K epch:10.08 loss:0.008 grdn:0.188 lr:6.2e-07 updt_s:0.059 data_s:0.064
+Training:  84%|██████████████████████████████████████████▉        | 25239/30000 [51:13<09:00,  8.80step/s]INFO 2026-05-19 02:03:06 ot_train.py:488 step:25K smpl:808K ep:1K epch:10.09 loss:0.009 grdn:0.228 lr:6.1e-07 updt_s:0.059 data_s:0.062
+Training:  84%|██████████████████████████████████████████▉        | 25259/30000 [51:15<09:25,  8.39step/s]INFO 2026-05-19 02:03:08 ot_train.py:488 step:25K smpl:808K ep:1K epch:10.09 loss:0.010 grdn:0.239 lr:6.1e-07 updt_s:0.060 data_s:0.066
+Training:  84%|██████████████████████████████████████████▉        | 25279/30000 [51:18<09:20,  8.43step/s]INFO 2026-05-19 02:03:11 ot_train.py:488 step:25K smpl:809K ep:1K epch:10.10 loss:0.010 grdn:0.235 lr:6.0e-07 updt_s:0.059 data_s:0.081
+Training:  84%|███████████████████████████████████████████        | 25299/30000 [51:21<08:45,  8.94step/s]INFO 2026-05-19 02:03:13 ot_train.py:488 step:25K smpl:810K ep:1K epch:10.11 loss:0.008 grdn:0.223 lr:6.0e-07 updt_s:0.058 data_s:0.063
+Training:  84%|███████████████████████████████████████████        | 25319/30000 [51:23<08:53,  8.78step/s]INFO 2026-05-19 02:03:16 ot_train.py:488 step:25K smpl:810K ep:1K epch:10.12 loss:0.009 grdn:0.222 lr:5.9e-07 updt_s:0.058 data_s:0.063
+Training:  84%|███████████████████████████████████████████        | 25339/30000 [51:26<09:21,  8.30step/s]INFO 2026-05-19 02:03:18 ot_train.py:488 step:25K smpl:811K ep:1K epch:10.12 loss:0.009 grdn:0.207 lr:5.9e-07 updt_s:0.058 data_s:0.064
+Training:  85%|███████████████████████████████████████████        | 25359/30000 [51:28<09:58,  7.75step/s]INFO 2026-05-19 02:03:21 ot_train.py:488 step:25K smpl:812K ep:1K epch:10.13 loss:0.008 grdn:0.196 lr:5.8e-07 updt_s:0.058 data_s:0.065
+Training:  85%|███████████████████████████████████████████▏       | 25379/30000 [51:30<10:04,  7.65step/s]INFO 2026-05-19 02:03:23 ot_train.py:488 step:25K smpl:812K ep:1K epch:10.14 loss:0.010 grdn:0.249 lr:5.8e-07 updt_s:0.060 data_s:0.062
+Training:  85%|███████████████████████████████████████████▏       | 25399/30000 [51:33<09:38,  7.95step/s]INFO 2026-05-19 02:03:26 ot_train.py:488 step:25K smpl:813K ep:1K epch:10.15 loss:0.009 grdn:0.221 lr:5.8e-07 updt_s:0.059 data_s:0.059
+Training:  85%|███████████████████████████████████████████▏       | 25419/30000 [51:35<09:53,  7.71step/s]INFO 2026-05-19 02:03:28 ot_train.py:488 step:25K smpl:813K ep:1K epch:10.16 loss:0.009 grdn:0.208 lr:5.7e-07 updt_s:0.059 data_s:0.061
+Training:  85%|███████████████████████████████████████████▏       | 25439/30000 [51:38<09:36,  7.91step/s]INFO 2026-05-19 02:03:30 ot_train.py:488 step:25K smpl:814K ep:1K epch:10.16 loss:0.010 grdn:0.217 lr:5.7e-07 updt_s:0.059 data_s:0.051
+Training:  85%|███████████████████████████████████████████▎       | 25459/30000 [51:40<10:46,  7.03step/s]INFO 2026-05-19 02:03:33 ot_train.py:488 step:25K smpl:815K ep:1K epch:10.17 loss:0.008 grdn:0.202 lr:5.6e-07 updt_s:0.059 data_s:0.066
+Training:  85%|███████████████████████████████████████████▎       | 25479/30000 [51:43<09:30,  7.93step/s]INFO 2026-05-19 02:03:35 ot_train.py:488 step:25K smpl:815K ep:1K epch:10.18 loss:0.010 grdn:0.237 lr:5.6e-07 updt_s:0.061 data_s:0.057
+Training:  85%|███████████████████████████████████████████▎       | 25499/30000 [51:45<10:05,  7.44step/s]INFO 2026-05-19 02:03:38 ot_train.py:488 step:26K smpl:816K ep:1K epch:10.19 loss:0.010 grdn:0.215 lr:5.5e-07 updt_s:0.061 data_s:0.060
+Training:  85%|███████████████████████████████████████████▍       | 25519/30000 [51:47<09:15,  8.06step/s]INFO 2026-05-19 02:03:40 ot_train.py:488 step:26K smpl:817K ep:1K epch:10.20 loss:0.010 grdn:0.230 lr:5.5e-07 updt_s:0.061 data_s:0.057
+Training:  85%|███████████████████████████████████████████▍       | 25539/30000 [51:50<09:28,  7.85step/s]INFO 2026-05-19 02:03:42 ot_train.py:488 step:26K smpl:817K ep:1K epch:10.20 loss:0.009 grdn:0.235 lr:5.4e-07 updt_s:0.060 data_s:0.059
+Training:  85%|███████████████████████████████████████████▍       | 25559/30000 [51:52<09:38,  7.68step/s]INFO 2026-05-19 02:03:45 ot_train.py:488 step:26K smpl:818K ep:1K epch:10.21 loss:0.009 grdn:0.231 lr:5.4e-07 updt_s:0.059 data_s:0.059
+Training:  85%|███████████████████████████████████████████▍       | 25579/30000 [51:55<09:50,  7.49step/s]INFO 2026-05-19 02:03:47 ot_train.py:488 step:26K smpl:819K ep:1K epch:10.22 loss:0.009 grdn:0.211 lr:5.3e-07 updt_s:0.060 data_s:0.062
+Training:  85%|███████████████████████████████████████████▌       | 25599/30000 [51:57<10:10,  7.20step/s]INFO 2026-05-19 02:03:50 ot_train.py:488 step:26K smpl:819K ep:1K epch:10.23 loss:0.010 grdn:0.228 lr:5.3e-07 updt_s:0.060 data_s:0.067
+Training:  85%|███████████████████████████████████████████▌       | 25619/30000 [52:00<09:35,  7.62step/s]INFO 2026-05-19 02:03:52 ot_train.py:488 step:26K smpl:820K ep:1K epch:10.24 loss:0.010 grdn:0.244 lr:5.2e-07 updt_s:0.059 data_s:0.064
+Training:  85%|███████████████████████████████████████████▌       | 25639/30000 [52:02<09:19,  7.79step/s]INFO 2026-05-19 02:03:55 ot_train.py:488 step:26K smpl:820K ep:1K epch:10.24 loss:0.008 grdn:0.203 lr:5.2e-07 updt_s:0.059 data_s:0.060
+Training:  86%|███████████████████████████████████████████▌       | 25659/30000 [52:04<09:36,  7.54step/s]INFO 2026-05-19 02:03:57 ot_train.py:488 step:26K smpl:821K ep:1K epch:10.25 loss:0.008 grdn:0.221 lr:5.1e-07 updt_s:0.059 data_s:0.062
+Training:  86%|███████████████████████████████████████████▋       | 25679/30000 [52:07<09:36,  7.50step/s]INFO 2026-05-19 02:04:00 ot_train.py:488 step:26K smpl:822K ep:1K epch:10.26 loss:0.010 grdn:0.239 lr:5.1e-07 updt_s:0.058 data_s:0.063
+Training:  86%|███████████████████████████████████████████▋       | 25699/30000 [52:09<09:44,  7.35step/s]INFO 2026-05-19 02:04:02 ot_train.py:488 step:26K smpl:822K ep:1K epch:10.27 loss:0.009 grdn:0.218 lr:5.0e-07 updt_s:0.060 data_s:0.063
+Training:  86%|███████████████████████████████████████████▋       | 25719/30000 [52:12<09:31,  7.49step/s]INFO 2026-05-19 02:04:05 ot_train.py:488 step:26K smpl:823K ep:1K epch:10.28 loss:0.011 grdn:0.248 lr:5.0e-07 updt_s:0.060 data_s:0.062
+Training:  86%|███████████████████████████████████████████▊       | 25739/30000 [52:14<09:37,  7.37step/s]INFO 2026-05-19 02:04:07 ot_train.py:488 step:26K smpl:824K ep:1K epch:10.28 loss:0.010 grdn:0.246 lr:4.9e-07 updt_s:0.058 data_s:0.064
+Training:  86%|███████████████████████████████████████████▊       | 25759/30000 [52:17<09:04,  7.79step/s]INFO 2026-05-19 02:04:09 ot_train.py:488 step:26K smpl:824K ep:1K epch:10.29 loss:0.008 grdn:0.203 lr:4.9e-07 updt_s:0.058 data_s:0.059
+Training:  86%|███████████████████████████████████████████▊       | 25779/30000 [52:19<08:54,  7.89step/s]INFO 2026-05-19 02:04:12 ot_train.py:488 step:26K smpl:825K ep:1K epch:10.30 loss:0.008 grdn:0.234 lr:4.9e-07 updt_s:0.058 data_s:0.059
+Training:  86%|███████████████████████████████████████████▊       | 25799/30000 [52:21<09:24,  7.44step/s]INFO 2026-05-19 02:04:14 ot_train.py:488 step:26K smpl:826K ep:1K epch:10.31 loss:0.008 grdn:0.224 lr:4.8e-07 updt_s:0.059 data_s:0.061
+Training:  86%|███████████████████████████████████████████▉       | 25819/30000 [52:24<09:18,  7.49step/s]INFO 2026-05-19 02:04:17 ot_train.py:488 step:26K smpl:826K ep:1K epch:10.32 loss:0.007 grdn:0.202 lr:4.8e-07 updt_s:0.059 data_s:0.062
+Training:  86%|███████████████████████████████████████████▉       | 25839/30000 [52:26<09:01,  7.69step/s]INFO 2026-05-19 02:04:19 ot_train.py:488 step:26K smpl:827K ep:1K epch:10.32 loss:0.009 grdn:0.214 lr:4.7e-07 updt_s:0.059 data_s:0.061
+Training:  86%|███████████████████████████████████████████▉       | 25859/30000 [52:29<09:18,  7.41step/s]INFO 2026-05-19 02:04:21 ot_train.py:488 step:26K smpl:828K ep:1K epch:10.33 loss:0.008 grdn:0.206 lr:4.7e-07 updt_s:0.059 data_s:0.060
+Training:  86%|███████████████████████████████████████████▉       | 25879/30000 [52:31<09:17,  7.39step/s]INFO 2026-05-19 02:04:24 ot_train.py:488 step:26K smpl:828K ep:1K epch:10.34 loss:0.008 grdn:0.211 lr:4.6e-07 updt_s:0.060 data_s:0.065
+Training:  86%|████████████████████████████████████████████       | 25899/30000 [52:34<09:11,  7.44step/s]INFO 2026-05-19 02:04:26 ot_train.py:488 step:26K smpl:829K ep:1K epch:10.35 loss:0.008 grdn:0.194 lr:4.6e-07 updt_s:0.058 data_s:0.065
+Training:  86%|████████████████████████████████████████████       | 25919/30000 [52:36<08:58,  7.58step/s]INFO 2026-05-19 02:04:29 ot_train.py:488 step:26K smpl:829K ep:1K epch:10.36 loss:0.008 grdn:0.215 lr:4.5e-07 updt_s:0.058 data_s:0.061
+Training:  86%|████████████████████████████████████████████       | 25939/30000 [52:39<08:55,  7.58step/s]INFO 2026-05-19 02:04:31 ot_train.py:488 step:26K smpl:830K ep:1K epch:10.36 loss:0.009 grdn:0.226 lr:4.5e-07 updt_s:0.059 data_s:0.062
+Training:  87%|████████████████████████████████████████████▏      | 25959/30000 [52:41<09:03,  7.44step/s]INFO 2026-05-19 02:04:34 ot_train.py:488 step:26K smpl:831K ep:1K epch:10.37 loss:0.010 grdn:0.226 lr:4.5e-07 updt_s:0.060 data_s:0.064
+Training:  87%|████████████████████████████████████████████▏      | 25979/30000 [52:43<08:57,  7.48step/s]INFO 2026-05-19 02:04:36 ot_train.py:488 step:26K smpl:831K ep:1K epch:10.38 loss:0.008 grdn:0.222 lr:4.4e-07 updt_s:0.060 data_s:0.062
+Training:  87%|████████████████████████████████████████████▏      | 25999/30000 [52:46<08:56,  7.46step/s]INFO 2026-05-19 02:04:39 ot_train.py:488 step:26K smpl:832K ep:1K epch:10.39 loss:0.009 grdn:0.214 lr:4.4e-07 updt_s:0.058 data_s:0.064
+Training:  87%|████████████████████████████████████████████▏      | 26019/30000 [52:48<08:45,  7.58step/s]INFO 2026-05-19 02:04:41 ot_train.py:488 step:26K smpl:833K ep:1K epch:10.40 loss:0.008 grdn:0.198 lr:4.3e-07 updt_s:0.060 data_s:0.063
+Training:  87%|████████████████████████████████████████████▎      | 26039/30000 [52:51<09:18,  7.09step/s]INFO 2026-05-19 02:04:44 ot_train.py:488 step:26K smpl:833K ep:1K epch:10.40 loss:0.008 grdn:0.189 lr:4.3e-07 updt_s:0.061 data_s:0.066
+Training:  87%|████████████████████████████████████████████▎      | 26059/30000 [52:53<08:37,  7.62step/s]INFO 2026-05-19 02:04:46 ot_train.py:488 step:26K smpl:834K ep:1K epch:10.41 loss:0.009 grdn:0.218 lr:4.2e-07 updt_s:0.062 data_s:0.055
+Training:  87%|████████████████████████████████████████████▎      | 26079/30000 [52:56<08:31,  7.67step/s]INFO 2026-05-19 02:04:48 ot_train.py:488 step:26K smpl:835K ep:1K epch:10.42 loss:0.010 grdn:0.208 lr:4.2e-07 updt_s:0.060 data_s:0.060
+Training:  87%|████████████████████████████████████████████▎      | 26099/30000 [52:58<08:38,  7.52step/s]INFO 2026-05-19 02:04:51 ot_train.py:488 step:26K smpl:835K ep:1K epch:10.43 loss:0.009 grdn:0.218 lr:4.2e-07 updt_s:0.060 data_s:0.062
+Training:  87%|████████████████████████████████████████████▍      | 26119/30000 [53:01<08:39,  7.47step/s]INFO 2026-05-19 02:04:53 ot_train.py:488 step:26K smpl:836K ep:1K epch:10.44 loss:0.008 grdn:0.203 lr:4.1e-07 updt_s:0.058 data_s:0.064
+Training:  87%|████████████████████████████████████████████▍      | 26139/30000 [53:03<08:16,  7.77step/s]INFO 2026-05-19 02:04:56 ot_train.py:488 step:26K smpl:836K ep:1K epch:10.44 loss:0.007 grdn:0.194 lr:4.1e-07 updt_s:0.061 data_s:0.061
+Training:  87%|████████████████████████████████████████████▍      | 26159/30000 [53:05<08:07,  7.88step/s]INFO 2026-05-19 02:04:58 ot_train.py:488 step:26K smpl:837K ep:1K epch:10.45 loss:0.010 grdn:0.233 lr:4.0e-07 updt_s:0.059 data_s:0.060
+Training:  87%|████████████████████████████████████████████▌      | 26179/30000 [53:08<08:14,  7.72step/s]INFO 2026-05-19 02:05:01 ot_train.py:488 step:26K smpl:838K ep:1K epch:10.46 loss:0.011 grdn:0.250 lr:4.0e-07 updt_s:0.061 data_s:0.059
+Training:  87%|████████████████████████████████████████████▌      | 26199/30000 [53:10<08:25,  7.51step/s]INFO 2026-05-19 02:05:03 ot_train.py:488 step:26K smpl:838K ep:1K epch:10.47 loss:0.009 grdn:0.205 lr:4.0e-07 updt_s:0.061 data_s:0.060
+Training:  87%|████████████████████████████████████████████▌      | 26219/30000 [53:13<08:33,  7.37step/s]INFO 2026-05-19 02:05:06 ot_train.py:488 step:26K smpl:839K ep:1K epch:10.48 loss:0.009 grdn:0.238 lr:3.9e-07 updt_s:0.061 data_s:0.063
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+Training:  88%|████████████████████████████████████████████▊      | 26339/30000 [53:27<08:06,  7.53step/s]INFO 2026-05-19 02:05:20 ot_train.py:488 step:26K smpl:843K ep:1K epch:10.52 loss:0.010 grdn:0.221 lr:3.7e-07 updt_s:0.059 data_s:0.060
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+Training:  88%|████████████████████████████████████████████▊      | 26379/30000 [53:32<07:38,  7.90step/s]INFO 2026-05-19 02:05:25 ot_train.py:488 step:26K smpl:844K ep:1K epch:10.54 loss:0.009 grdn:0.219 lr:3.6e-07 updt_s:0.059 data_s:0.056
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+Training:  88%|████████████████████████████████████████████▉      | 26459/30000 [53:42<07:24,  7.97step/s]INFO 2026-05-19 02:05:34 ot_train.py:488 step:26K smpl:847K ep:1K epch:10.57 loss:0.009 grdn:0.213 lr:3.4e-07 updt_s:0.059 data_s:0.060
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+Training:  88%|█████████████████████████████████████████████      | 26539/30000 [53:52<07:35,  7.60step/s]INFO 2026-05-19 02:05:44 ot_train.py:488 step:27K smpl:849K ep:1K epch:10.60 loss:0.009 grdn:0.206 lr:3.3e-07 updt_s:0.061 data_s:0.059
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+Training:  89%|█████████████████████████████████████████████▏     | 26579/30000 [53:56<07:14,  7.86step/s]INFO 2026-05-19 02:05:49 ot_train.py:488 step:27K smpl:851K ep:1K epch:10.62 loss:0.011 grdn:0.224 lr:3.2e-07 updt_s:0.058 data_s:0.060
+Training:  89%|█████████████████████████████████████████████▏     | 26599/30000 [53:59<07:35,  7.47step/s]INFO 2026-05-19 02:05:52 ot_train.py:488 step:27K smpl:851K ep:1K epch:10.63 loss:0.009 grdn:0.223 lr:3.2e-07 updt_s:0.058 data_s:0.065
+Training:  89%|█████████████████████████████████████████████▎     | 26619/30000 [54:01<07:22,  7.65step/s]INFO 2026-05-19 02:05:54 ot_train.py:488 step:27K smpl:852K ep:1K epch:10.64 loss:0.009 grdn:0.219 lr:3.1e-07 updt_s:0.057 data_s:0.061
+Training:  89%|█████████████████████████████████████████████▎     | 26639/30000 [54:04<07:13,  7.74step/s]INFO 2026-05-19 02:05:56 ot_train.py:488 step:27K smpl:852K ep:1K epch:10.64 loss:0.008 grdn:0.196 lr:3.1e-07 updt_s:0.058 data_s:0.062
+Training:  89%|█████████████████████████████████████████████▎     | 26659/30000 [54:06<07:20,  7.58step/s]INFO 2026-05-19 02:05:59 ot_train.py:488 step:27K smpl:853K ep:1K epch:10.65 loss:0.009 grdn:0.207 lr:3.1e-07 updt_s:0.058 data_s:0.062
+Training:  89%|█████████████████████████████████████████████▎     | 26679/30000 [54:09<07:19,  7.56step/s]INFO 2026-05-19 02:06:01 ot_train.py:488 step:27K smpl:854K ep:1K epch:10.66 loss:0.009 grdn:0.202 lr:3.0e-07 updt_s:0.058 data_s:0.062
+Training:  89%|█████████████████████████████████████████████▍     | 26699/30000 [54:11<06:57,  7.91step/s]INFO 2026-05-19 02:06:04 ot_train.py:488 step:27K smpl:854K ep:1K epch:10.67 loss:0.008 grdn:0.234 lr:3.0e-07 updt_s:0.060 data_s:0.060
+Training:  89%|█████████████████████████████████████████████▍     | 26719/30000 [54:13<07:22,  7.41step/s]INFO 2026-05-19 02:06:06 ot_train.py:488 step:27K smpl:855K ep:1K epch:10.68 loss:0.010 grdn:0.220 lr:3.0e-07 updt_s:0.059 data_s:0.063
+Training:  89%|█████████████████████████████████████████████▍     | 26739/30000 [54:16<07:06,  7.65step/s]INFO 2026-05-19 02:06:08 ot_train.py:488 step:27K smpl:856K ep:1K epch:10.68 loss:0.011 grdn:0.252 lr:2.9e-07 updt_s:0.060 data_s:0.059
+Training:  89%|█████████████████████████████████████████████▍     | 26759/30000 [54:18<06:45,  7.99step/s]INFO 2026-05-19 02:06:11 ot_train.py:488 step:27K smpl:856K ep:1K epch:10.69 loss:0.008 grdn:0.198 lr:2.9e-07 updt_s:0.061 data_s:0.055
+Training:  89%|█████████████████████████████████████████████▌     | 26779/30000 [54:20<06:44,  7.96step/s]INFO 2026-05-19 02:06:13 ot_train.py:488 step:27K smpl:857K ep:1K epch:10.70 loss:0.010 grdn:0.238 lr:2.9e-07 updt_s:0.060 data_s:0.057
+Training:  89%|█████████████████████████████████████████████▌     | 26799/30000 [54:23<07:10,  7.44step/s]INFO 2026-05-19 02:06:16 ot_train.py:488 step:27K smpl:858K ep:1K epch:10.71 loss:0.009 grdn:0.218 lr:2.8e-07 updt_s:0.061 data_s:0.061
+Training:  89%|█████████████████████████████████████████████▌     | 26819/30000 [54:25<07:02,  7.52step/s]INFO 2026-05-19 02:06:18 ot_train.py:488 step:27K smpl:858K ep:1K epch:10.72 loss:0.008 grdn:0.194 lr:2.8e-07 updt_s:0.061 data_s:0.057
+Training:  89%|█████████████████████████████████████████████▋     | 26839/30000 [54:28<06:28,  8.13step/s]INFO 2026-05-19 02:06:20 ot_train.py:488 step:27K smpl:859K ep:1K epch:10.72 loss:0.009 grdn:0.212 lr:2.7e-07 updt_s:0.059 data_s:0.060
+Training:  90%|█████████████████████████████████████████████▋     | 26859/30000 [54:30<06:37,  7.91step/s]INFO 2026-05-19 02:06:23 ot_train.py:488 step:27K smpl:860K ep:1K epch:10.73 loss:0.009 grdn:0.220 lr:2.7e-07 updt_s:0.059 data_s:0.059
+Training:  90%|█████████████████████████████████████████████▋     | 26879/30000 [54:32<06:41,  7.78step/s]INFO 2026-05-19 02:06:25 ot_train.py:488 step:27K smpl:860K ep:1K epch:10.74 loss:0.008 grdn:0.187 lr:2.7e-07 updt_s:0.058 data_s:0.061
+Training:  90%|█████████████████████████████████████████████▋     | 26899/30000 [54:35<06:27,  8.00step/s]INFO 2026-05-19 02:06:28 ot_train.py:488 step:27K smpl:861K ep:1K epch:10.75 loss:0.010 grdn:0.215 lr:2.6e-07 updt_s:0.059 data_s:0.060
+Training:  90%|█████████████████████████████████████████████▊     | 26919/30000 [54:37<06:01,  8.51step/s]INFO 2026-05-19 02:06:30 ot_train.py:488 step:27K smpl:861K ep:1K epch:10.76 loss:0.009 grdn:0.199 lr:2.6e-07 updt_s:0.059 data_s:0.059
+Training:  90%|█████████████████████████████████████████████▊     | 26939/30000 [54:40<05:44,  8.88step/s]INFO 2026-05-19 02:06:32 ot_train.py:488 step:27K smpl:862K ep:1K epch:10.76 loss:0.009 grdn:0.215 lr:2.6e-07 updt_s:0.058 data_s:0.057
+Training:  90%|█████████████████████████████████████████████▊     | 26959/30000 [54:42<06:01,  8.41step/s]INFO 2026-05-19 02:06:35 ot_train.py:488 step:27K smpl:863K ep:1K epch:10.77 loss:0.009 grdn:0.215 lr:2.5e-07 updt_s:0.058 data_s:0.062
+Training:  90%|█████████████████████████████████████████████▊     | 26979/30000 [54:44<05:59,  8.41step/s]INFO 2026-05-19 02:06:37 ot_train.py:488 step:27K smpl:863K ep:1K epch:10.78 loss:0.010 grdn:0.235 lr:2.5e-07 updt_s:0.059 data_s:0.059
+Training:  90%|█████████████████████████████████████████████▉     | 26999/30000 [54:47<05:49,  8.58step/s]INFO 2026-05-19 02:06:39 ot_train.py:488 step:27K smpl:864K ep:1K epch:10.79 loss:0.009 grdn:0.214 lr:2.5e-07 updt_s:0.059 data_s:0.058
+Training:  90%|█████████████████████████████████████████████▉     | 27019/30000 [54:49<05:40,  8.75step/s]INFO 2026-05-19 02:06:42 ot_train.py:488 step:27K smpl:865K ep:1K epch:10.80 loss:0.010 grdn:0.223 lr:2.4e-07 updt_s:0.058 data_s:0.058
+Training:  90%|█████████████████████████████████████████████▉     | 27039/30000 [54:51<05:35,  8.83step/s]INFO 2026-05-19 02:06:44 ot_train.py:488 step:27K smpl:865K ep:1K epch:10.80 loss:0.009 grdn:0.225 lr:2.4e-07 updt_s:0.059 data_s:0.058
+Training:  90%|██████████████████████████████████████████████     | 27059/30000 [54:54<05:25,  9.04step/s]INFO 2026-05-19 02:06:46 ot_train.py:488 step:27K smpl:866K ep:1K epch:10.81 loss:0.011 grdn:0.236 lr:2.4e-07 updt_s:0.058 data_s:0.058
+Training:  90%|██████████████████████████████████████████████     | 27079/30000 [54:56<05:22,  9.06step/s]INFO 2026-05-19 02:06:49 ot_train.py:488 step:27K smpl:867K ep:1K epch:10.82 loss:0.010 grdn:0.234 lr:2.3e-07 updt_s:0.058 data_s:0.059
+Training:  90%|██████████████████████████████████████████████     | 27099/30000 [54:58<05:08,  9.39step/s]INFO 2026-05-19 02:06:51 ot_train.py:488 step:27K smpl:867K ep:1K epch:10.83 loss:0.008 grdn:0.203 lr:2.3e-07 updt_s:0.059 data_s:0.058
+Training:  90%|██████████████████████████████████████████████     | 27119/30000 [55:01<05:11,  9.24step/s]INFO 2026-05-19 02:06:53 ot_train.py:488 step:27K smpl:868K ep:1K epch:10.84 loss:0.008 grdn:0.202 lr:2.3e-07 updt_s:0.058 data_s:0.059
+Training:  90%|██████████████████████████████████████████████▏    | 27139/30000 [55:03<05:13,  9.11step/s]INFO 2026-05-19 02:06:56 ot_train.py:488 step:27K smpl:868K ep:1K epch:10.84 loss:0.009 grdn:0.203 lr:2.3e-07 updt_s:0.060 data_s:0.058
+Training:  91%|██████████████████████████████████████████████▏    | 27159/30000 [55:05<05:03,  9.36step/s]INFO 2026-05-19 02:06:58 ot_train.py:488 step:27K smpl:869K ep:1K epch:10.85 loss:0.009 grdn:0.230 lr:2.2e-07 updt_s:0.058 data_s:0.058
+Training:  91%|██████████████████████████████████████████████▏    | 27179/30000 [55:08<05:12,  9.03step/s]INFO 2026-05-19 02:07:01 ot_train.py:488 step:27K smpl:870K ep:1K epch:10.86 loss:0.009 grdn:0.208 lr:2.2e-07 updt_s:0.059 data_s:0.059
+Training:  91%|██████████████████████████████████████████████▏    | 27199/30000 [55:11<05:24,  8.63step/s]INFO 2026-05-19 02:07:03 ot_train.py:488 step:27K smpl:870K ep:1K epch:10.87 loss:0.009 grdn:0.232 lr:2.2e-07 updt_s:0.059 data_s:0.073
+Training:  91%|██████████████████████████████████████████████▎    | 27219/30000 [55:13<07:10,  6.47step/s]INFO 2026-05-19 02:07:06 ot_train.py:488 step:27K smpl:871K ep:1K epch:10.88 loss:0.011 grdn:0.242 lr:2.1e-07 updt_s:0.058 data_s:0.073
+Training:  91%|██████████████████████████████████████████████▎    | 27239/30000 [55:15<05:53,  7.82step/s]INFO 2026-05-19 02:07:08 ot_train.py:488 step:27K smpl:872K ep:1K epch:10.88 loss:0.010 grdn:0.226 lr:2.1e-07 updt_s:0.059 data_s:0.054
+Training:  91%|██████████████████████████████████████████████▎    | 27259/30000 [55:18<05:57,  7.66step/s]INFO 2026-05-19 02:07:11 ot_train.py:488 step:27K smpl:872K ep:1K epch:10.89 loss:0.008 grdn:0.216 lr:2.1e-07 updt_s:0.060 data_s:0.060
+Training:  91%|██████████████████████████████████████████████▎    | 27279/30000 [55:20<06:05,  7.45step/s]INFO 2026-05-19 02:07:13 ot_train.py:488 step:27K smpl:873K ep:1K epch:10.90 loss:0.011 grdn:0.214 lr:2.0e-07 updt_s:0.059 data_s:0.062
+Training:  91%|██████████████████████████████████████████████▍    | 27299/30000 [55:23<05:43,  7.87step/s]INFO 2026-05-19 02:07:15 ot_train.py:488 step:27K smpl:874K ep:1K epch:10.91 loss:0.009 grdn:0.230 lr:2.0e-07 updt_s:0.059 data_s:0.059
+Training:  91%|██████████████████████████████████████████████▍    | 27319/30000 [55:25<05:47,  7.72step/s]INFO 2026-05-19 02:07:18 ot_train.py:488 step:27K smpl:874K ep:1K epch:10.92 loss:0.009 grdn:0.234 lr:2.0e-07 updt_s:0.061 data_s:0.059
+Training:  91%|██████████████████████████████████████████████▍    | 27339/30000 [55:27<05:31,  8.03step/s]INFO 2026-05-19 02:07:20 ot_train.py:488 step:27K smpl:875K ep:1K epch:10.92 loss:0.009 grdn:0.214 lr:2.0e-07 updt_s:0.059 data_s:0.059
+Training:  91%|██████████████████████████████████████████████▌    | 27359/30000 [55:30<05:34,  7.90step/s]INFO 2026-05-19 02:07:23 ot_train.py:488 step:27K smpl:876K ep:1K epch:10.93 loss:0.008 grdn:0.194 lr:1.9e-07 updt_s:0.060 data_s:0.057
+Training:  91%|██████████████████████████████████████████████▌    | 27379/30000 [55:32<05:44,  7.61step/s]INFO 2026-05-19 02:07:25 ot_train.py:488 step:27K smpl:876K ep:1K epch:10.94 loss:0.008 grdn:0.200 lr:1.9e-07 updt_s:0.059 data_s:0.058
+Training:  91%|██████████████████████████████████████████████▌    | 27399/30000 [55:35<05:28,  7.91step/s]INFO 2026-05-19 02:07:27 ot_train.py:488 step:27K smpl:877K ep:1K epch:10.95 loss:0.008 grdn:0.234 lr:1.9e-07 updt_s:0.060 data_s:0.056
+Training:  91%|██████████████████████████████████████████████▌    | 27419/30000 [55:37<05:39,  7.60step/s]INFO 2026-05-19 02:07:30 ot_train.py:488 step:27K smpl:877K ep:1K epch:10.96 loss:0.010 grdn:0.225 lr:1.8e-07 updt_s:0.060 data_s:0.061
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+Training:  92%|██████████████████████████████████████████████▉    | 27639/30000 [56:03<05:10,  7.60step/s]INFO 2026-05-19 02:07:56 ot_train.py:488 step:28K smpl:884K ep:1K epch:11.04 loss:0.008 grdn:0.225 lr:1.5e-07 updt_s:0.060 data_s:0.057
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+Training:  92%|███████████████████████████████████████████████    | 27719/30000 [56:13<04:52,  7.81step/s]INFO 2026-05-19 02:08:06 ot_train.py:488 step:28K smpl:887K ep:1K epch:11.08 loss:0.010 grdn:0.231 lr:1.4e-07 updt_s:0.059 data_s:0.059
+Training:  92%|███████████████████████████████████████████████▏   | 27739/30000 [56:15<04:24,  8.53step/s]INFO 2026-05-19 02:08:08 ot_train.py:488 step:28K smpl:888K ep:1K epch:11.08 loss:0.008 grdn:0.209 lr:1.4e-07 updt_s:0.061 data_s:0.050
+Training:  93%|███████████████████████████████████████████████▏   | 27759/30000 [56:18<04:27,  8.39step/s]INFO 2026-05-19 02:08:10 ot_train.py:488 step:28K smpl:888K ep:1K epch:11.09 loss:0.009 grdn:0.213 lr:1.4e-07 updt_s:0.061 data_s:0.051
+Training:  93%|███████████████████████████████████████████████▏   | 27779/30000 [56:20<04:37,  8.00step/s]INFO 2026-05-19 02:08:13 ot_train.py:488 step:28K smpl:889K ep:1K epch:11.10 loss:0.010 grdn:0.231 lr:1.4e-07 updt_s:0.060 data_s:0.056
+Training:  93%|███████████████████████████████████████████████▎   | 27799/30000 [56:22<04:34,  8.03step/s]INFO 2026-05-19 02:08:15 ot_train.py:488 step:28K smpl:890K ep:1K epch:11.11 loss:0.008 grdn:0.206 lr:1.3e-07 updt_s:0.060 data_s:0.056
+Training:  93%|███████████████████████████████████████████████▎   | 27819/30000 [56:25<04:32,  8.01step/s]INFO 2026-05-19 02:08:17 ot_train.py:488 step:28K smpl:890K ep:1K epch:11.12 loss:0.008 grdn:0.189 lr:1.3e-07 updt_s:0.059 data_s:0.057
+Training:  93%|███████████████████████████████████████████████▎   | 27839/30000 [56:27<04:36,  7.81step/s]INFO 2026-05-19 02:08:20 ot_train.py:488 step:28K smpl:891K ep:1K epch:11.12 loss:0.009 grdn:0.212 lr:1.3e-07 updt_s:0.058 data_s:0.058
+Training:  93%|███████████████████████████████████████████████▎   | 27859/30000 [56:29<04:36,  7.73step/s]INFO 2026-05-19 02:08:22 ot_train.py:488 step:28K smpl:892K ep:1K epch:11.13 loss:0.011 grdn:0.241 lr:1.3e-07 updt_s:0.058 data_s:0.062
+Training:  93%|███████████████████████████████████████████████▍   | 27879/30000 [56:32<04:32,  7.78step/s]INFO 2026-05-19 02:08:24 ot_train.py:488 step:28K smpl:892K ep:1K epch:11.14 loss:0.008 grdn:0.214 lr:1.2e-07 updt_s:0.059 data_s:0.061
+Training:  93%|███████████████████████████████████████████████▍   | 27899/30000 [56:34<04:30,  7.77step/s]INFO 2026-05-19 02:08:27 ot_train.py:488 step:28K smpl:893K ep:1K epch:11.15 loss:0.008 grdn:0.218 lr:1.2e-07 updt_s:0.059 data_s:0.060
+Training:  93%|███████████████████████████████████████████████▍   | 27919/30000 [56:37<04:27,  7.77step/s]INFO 2026-05-19 02:08:29 ot_train.py:488 step:28K smpl:893K ep:1K epch:11.16 loss:0.011 grdn:0.235 lr:1.2e-07 updt_s:0.059 data_s:0.060
+Training:  93%|███████████████████████████████████████████████▍   | 27939/30000 [56:39<04:15,  8.07step/s]INFO 2026-05-19 02:08:32 ot_train.py:488 step:28K smpl:894K ep:1K epch:11.16 loss:0.010 grdn:0.222 lr:1.2e-07 updt_s:0.058 data_s:0.056
+Training:  93%|███████████████████████████████████████████████▌   | 27959/30000 [56:41<04:14,  8.02step/s]INFO 2026-05-19 02:08:34 ot_train.py:488 step:28K smpl:895K ep:1K epch:11.17 loss:0.009 grdn:0.217 lr:1.2e-07 updt_s:0.059 data_s:0.058
+Training:  93%|███████████████████████████████████████████████▌   | 27979/30000 [56:44<04:26,  7.58step/s]INFO 2026-05-19 02:08:36 ot_train.py:488 step:28K smpl:895K ep:1K epch:11.18 loss:0.009 grdn:0.211 lr:1.1e-07 updt_s:0.061 data_s:0.058
+Training:  93%|███████████████████████████████████████████████▌   | 27999/30000 [56:46<04:23,  7.59step/s]INFO 2026-05-19 02:08:39 ot_train.py:488 step:28K smpl:896K ep:1K epch:11.19 loss:0.010 grdn:0.237 lr:1.1e-07 updt_s:0.060 data_s:0.060
+Training:  93%|███████████████████████████████████████████████▋   | 28019/30000 [56:48<04:14,  7.77step/s]INFO 2026-05-19 02:08:41 ot_train.py:488 step:28K smpl:897K ep:2K epch:11.20 loss:0.010 grdn:0.222 lr:1.1e-07 updt_s:0.060 data_s:0.058
+Training:  93%|███████████████████████████████████████████████▋   | 28039/30000 [56:51<04:11,  7.79step/s]INFO 2026-05-19 02:08:43 ot_train.py:488 step:28K smpl:897K ep:2K epch:11.20 loss:0.008 grdn:0.221 lr:1.1e-07 updt_s:0.058 data_s:0.061
+Training:  94%|███████████████████████████████████████████████▋   | 28059/30000 [56:53<04:10,  7.75step/s]INFO 2026-05-19 02:08:46 ot_train.py:488 step:28K smpl:898K ep:2K epch:11.21 loss:0.009 grdn:0.206 lr:1.0e-07 updt_s:0.059 data_s:0.059
+Training:  94%|███████████████████████████████████████████████▋   | 28079/30000 [56:56<04:03,  7.89step/s]INFO 2026-05-19 02:08:48 ot_train.py:488 step:28K smpl:899K ep:2K epch:11.22 loss:0.009 grdn:0.204 lr:1.0e-07 updt_s:0.061 data_s:0.056
+Training:  94%|███████████████████████████████████████████████▊   | 28099/30000 [56:58<04:00,  7.90step/s]INFO 2026-05-19 02:08:51 ot_train.py:488 step:28K smpl:899K ep:2K epch:11.23 loss:0.010 grdn:0.235 lr:1.0e-07 updt_s:0.059 data_s:0.060
+Training:  94%|███████████████████████████████████████████████▊   | 28119/30000 [57:00<03:54,  8.01step/s]INFO 2026-05-19 02:08:53 ot_train.py:488 step:28K smpl:900K ep:2K epch:11.24 loss:0.009 grdn:0.210 lr:9.8e-08 updt_s:0.059 data_s:0.056
+Training:  94%|███████████████████████████████████████████████▊   | 28139/30000 [57:03<03:43,  8.31step/s]INFO 2026-05-19 02:08:55 ot_train.py:488 step:28K smpl:900K ep:2K epch:11.24 loss:0.009 grdn:0.212 lr:9.6e-08 updt_s:0.059 data_s:0.058
+Training:  94%|███████████████████████████████████████████████▊   | 28159/30000 [57:05<03:31,  8.70step/s]INFO 2026-05-19 02:08:58 ot_train.py:488 step:28K smpl:901K ep:2K epch:11.25 loss:0.011 grdn:0.238 lr:9.4e-08 updt_s:0.058 data_s:0.057
+Training:  94%|███████████████████████████████████████████████▉   | 28179/30000 [57:07<03:26,  8.80step/s]INFO 2026-05-19 02:09:00 ot_train.py:488 step:28K smpl:902K ep:2K epch:11.26 loss:0.009 grdn:0.234 lr:9.2e-08 updt_s:0.060 data_s:0.058
+Training:  94%|███████████████████████████████████████████████▉   | 28199/30000 [57:10<03:29,  8.58step/s]INFO 2026-05-19 02:09:02 ot_train.py:488 step:28K smpl:902K ep:2K epch:11.27 loss:0.009 grdn:0.219 lr:9.0e-08 updt_s:0.060 data_s:0.059
+Training:  94%|███████████████████████████████████████████████▉   | 28219/30000 [57:12<03:28,  8.53step/s]INFO 2026-05-19 02:09:05 ot_train.py:488 step:28K smpl:903K ep:2K epch:11.28 loss:0.009 grdn:0.206 lr:8.8e-08 updt_s:0.059 data_s:0.057
+Training:  94%|████████████████████████████████████████████████   | 28239/30000 [57:14<03:21,  8.74step/s]INFO 2026-05-19 02:09:07 ot_train.py:488 step:28K smpl:904K ep:2K epch:11.28 loss:0.009 grdn:0.212 lr:8.6e-08 updt_s:0.058 data_s:0.058
+Training:  94%|████████████████████████████████████████████████   | 28259/30000 [57:17<03:22,  8.58step/s]INFO 2026-05-19 02:09:09 ot_train.py:488 step:28K smpl:904K ep:2K epch:11.29 loss:0.010 grdn:0.224 lr:8.4e-08 updt_s:0.059 data_s:0.058
+Training:  94%|████████████████████████████████████████████████   | 28279/30000 [57:19<03:25,  8.39step/s]INFO 2026-05-19 02:09:12 ot_train.py:488 step:28K smpl:905K ep:2K epch:11.30 loss:0.009 grdn:0.224 lr:8.2e-08 updt_s:0.058 data_s:0.059
+Training:  94%|████████████████████████████████████████████████   | 28299/30000 [57:21<03:29,  8.11step/s]INFO 2026-05-19 02:09:14 ot_train.py:488 step:28K smpl:906K ep:2K epch:11.31 loss:0.009 grdn:0.249 lr:8.0e-08 updt_s:0.059 data_s:0.060
+Training:  94%|████████████████████████████████████████████████▏  | 28319/30000 [57:24<03:35,  7.80step/s]INFO 2026-05-19 02:09:17 ot_train.py:488 step:28K smpl:906K ep:2K epch:11.32 loss:0.009 grdn:0.229 lr:7.9e-08 updt_s:0.059 data_s:0.062
+Training:  94%|████████████████████████████████████████████████▏  | 28339/30000 [57:26<03:21,  8.24step/s]INFO 2026-05-19 02:09:19 ot_train.py:488 step:28K smpl:907K ep:2K epch:11.32 loss:0.009 grdn:0.226 lr:7.7e-08 updt_s:0.059 data_s:0.057
+Training:  95%|████████████████████████████████████████████████▏  | 28359/30000 [57:29<03:21,  8.13step/s]INFO 2026-05-19 02:09:21 ot_train.py:488 step:28K smpl:908K ep:2K epch:11.33 loss:0.010 grdn:0.224 lr:7.5e-08 updt_s:0.058 data_s:0.059
+Training:  95%|████████████████████████████████████████████████▏  | 28379/30000 [57:31<03:17,  8.21step/s]INFO 2026-05-19 02:09:24 ot_train.py:488 step:28K smpl:908K ep:2K epch:11.34 loss:0.008 grdn:0.203 lr:7.3e-08 updt_s:0.058 data_s:0.057
+Training:  95%|████████████████████████████████████████████████▎  | 28399/30000 [57:33<03:17,  8.12step/s]INFO 2026-05-19 02:09:26 ot_train.py:488 step:28K smpl:909K ep:2K epch:11.35 loss:0.009 grdn:0.219 lr:7.1e-08 updt_s:0.059 data_s:0.059
+Training:  95%|████████████████████████████████████████████████▎  | 28419/30000 [57:36<03:04,  8.58step/s]INFO 2026-05-19 02:09:28 ot_train.py:488 step:28K smpl:909K ep:2K epch:11.36 loss:0.011 grdn:0.264 lr:7.0e-08 updt_s:0.058 data_s:0.062
+Training:  95%|████████████████████████████████████████████████▎  | 28439/30000 [57:38<02:56,  8.82step/s]INFO 2026-05-19 02:09:31 ot_train.py:488 step:28K smpl:910K ep:2K epch:11.36 loss:0.009 grdn:0.221 lr:6.8e-08 updt_s:0.060 data_s:0.059
+Training:  95%|████████████████████████████████████████████████▍  | 28459/30000 [57:40<02:49,  9.12step/s]INFO 2026-05-19 02:09:33 ot_train.py:488 step:28K smpl:911K ep:2K epch:11.37 loss:0.010 grdn:0.243 lr:6.6e-08 updt_s:0.061 data_s:0.058
+Training:  95%|████████████████████████████████████████████████▍  | 28479/30000 [57:43<02:45,  9.16step/s]INFO 2026-05-19 02:09:36 ot_train.py:488 step:28K smpl:911K ep:2K epch:11.38 loss:0.010 grdn:0.234 lr:6.4e-08 updt_s:0.060 data_s:0.057
+Training:  95%|████████████████████████████████████████████████▍  | 28499/30000 [57:45<02:43,  9.17step/s]INFO 2026-05-19 02:09:38 ot_train.py:488 step:28K smpl:912K ep:2K epch:11.39 loss:0.010 grdn:0.233 lr:6.3e-08 updt_s:0.060 data_s:0.058
+Training:  95%|████████████████████████████████████████████████▍  | 28519/30000 [57:48<02:43,  9.04step/s]INFO 2026-05-19 02:09:40 ot_train.py:488 step:29K smpl:913K ep:2K epch:11.40 loss:0.009 grdn:0.219 lr:6.1e-08 updt_s:0.059 data_s:0.060
+Training:  95%|████████████████████████████████████████████████▌  | 28539/30000 [57:50<02:37,  9.28step/s]INFO 2026-05-19 02:09:43 ot_train.py:488 step:29K smpl:913K ep:2K epch:11.40 loss:0.008 grdn:0.214 lr:5.9e-08 updt_s:0.060 data_s:0.057
+Training:  95%|████████████████████████████████████████████████▌  | 28559/30000 [57:52<02:33,  9.39step/s]INFO 2026-05-19 02:09:45 ot_train.py:488 step:29K smpl:914K ep:2K epch:11.41 loss:0.008 grdn:0.201 lr:5.8e-08 updt_s:0.060 data_s:0.056
+Training:  95%|████████████████████████████████████████████████▌  | 28579/30000 [57:55<02:33,  9.25step/s]INFO 2026-05-19 02:09:47 ot_train.py:488 step:29K smpl:915K ep:2K epch:11.42 loss:0.008 grdn:0.219 lr:5.6e-08 updt_s:0.058 data_s:0.059
+Training:  95%|████████████████████████████████████████████████▌  | 28599/30000 [57:57<02:34,  9.05step/s]INFO 2026-05-19 02:09:50 ot_train.py:488 step:29K smpl:915K ep:2K epch:11.43 loss:0.009 grdn:0.217 lr:5.5e-08 updt_s:0.059 data_s:0.061
+Training:  95%|████████████████████████████████████████████████▋  | 28619/30000 [57:59<02:32,  9.05step/s]INFO 2026-05-19 02:09:52 ot_train.py:488 step:29K smpl:916K ep:2K epch:11.44 loss:0.009 grdn:0.210 lr:5.3e-08 updt_s:0.058 data_s:0.060
+Training:  95%|████████████████████████████████████████████████▋  | 28639/30000 [58:02<02:31,  8.99step/s]INFO 2026-05-19 02:09:55 ot_train.py:488 step:29K smpl:916K ep:2K epch:11.44 loss:0.010 grdn:0.233 lr:5.2e-08 updt_s:0.059 data_s:0.059
+Training:  96%|████████████████████████████████████████████████▋  | 28659/30000 [58:04<02:26,  9.13step/s]INFO 2026-05-19 02:09:57 ot_train.py:488 step:29K smpl:917K ep:2K epch:11.45 loss:0.008 grdn:0.211 lr:5.0e-08 updt_s:0.059 data_s:0.060
+Training:  96%|████████████████████████████████████████████████▊  | 28679/30000 [58:07<02:24,  9.15step/s]INFO 2026-05-19 02:09:59 ot_train.py:488 step:29K smpl:918K ep:2K epch:11.46 loss:0.009 grdn:0.239 lr:4.9e-08 updt_s:0.059 data_s:0.056
+Training:  96%|████████████████████████████████████████████████▊  | 28699/30000 [58:09<02:25,  8.97step/s]INFO 2026-05-19 02:10:02 ot_train.py:488 step:29K smpl:918K ep:2K epch:11.47 loss:0.008 grdn:0.201 lr:4.7e-08 updt_s:0.059 data_s:0.059
+Training:  96%|████████████████████████████████████████████████▊  | 28719/30000 [58:11<02:11,  9.77step/s]INFO 2026-05-19 02:10:04 ot_train.py:488 step:29K smpl:919K ep:2K epch:11.48 loss:0.010 grdn:0.234 lr:4.6e-08 updt_s:0.060 data_s:0.054
+Training:  96%|████████████████████████████████████████████████▊  | 28739/30000 [58:14<02:14,  9.36step/s]INFO 2026-05-19 02:10:06 ot_train.py:488 step:29K smpl:920K ep:2K epch:11.48 loss:0.009 grdn:0.215 lr:4.4e-08 updt_s:0.059 data_s:0.059
+Training:  96%|████████████████████████████████████████████████▉  | 28759/30000 [58:16<02:12,  9.38step/s]INFO 2026-05-19 02:10:09 ot_train.py:488 step:29K smpl:920K ep:2K epch:11.49 loss:0.009 grdn:0.218 lr:4.3e-08 updt_s:0.059 data_s:0.057
+Training:  96%|████████████████████████████████████████████████▉  | 28779/30000 [58:18<02:08,  9.47step/s]INFO 2026-05-19 02:10:11 ot_train.py:488 step:29K smpl:921K ep:2K epch:11.50 loss:0.010 grdn:0.222 lr:4.2e-08 updt_s:0.058 data_s:0.056
+Training:  96%|████████████████████████████████████████████████▉  | 28799/30000 [58:21<02:09,  9.28step/s]INFO 2026-05-19 02:10:13 ot_train.py:488 step:29K smpl:922K ep:2K epch:11.51 loss:0.009 grdn:0.216 lr:4.0e-08 updt_s:0.059 data_s:0.058
+Training:  96%|████████████████████████████████████████████████▉  | 28819/30000 [58:23<02:08,  9.22step/s]INFO 2026-05-19 02:10:16 ot_train.py:488 step:29K smpl:922K ep:2K epch:11.52 loss:0.009 grdn:0.218 lr:3.9e-08 updt_s:0.060 data_s:0.056
+Training:  96%|█████████████████████████████████████████████████  | 28839/30000 [58:25<02:07,  9.07step/s]INFO 2026-05-19 02:10:18 ot_train.py:488 step:29K smpl:923K ep:2K epch:11.52 loss:0.009 grdn:0.216 lr:3.8e-08 updt_s:0.059 data_s:0.059
+Training:  96%|█████████████████████████████████████████████████  | 28859/30000 [58:28<02:00,  9.49step/s]INFO 2026-05-19 02:10:20 ot_train.py:488 step:29K smpl:924K ep:2K epch:11.53 loss:0.009 grdn:0.237 lr:3.6e-08 updt_s:0.060 data_s:0.053
+Training:  96%|█████████████████████████████████████████████████  | 28879/30000 [58:30<02:00,  9.32step/s]INFO 2026-05-19 02:10:23 ot_train.py:488 step:29K smpl:924K ep:2K epch:11.54 loss:0.008 grdn:0.223 lr:3.5e-08 updt_s:0.059 data_s:0.056
+Training:  96%|█████████████████████████████████████████████████▏ | 28899/30000 [58:32<01:58,  9.30step/s]INFO 2026-05-19 02:10:25 ot_train.py:488 step:29K smpl:925K ep:2K epch:11.55 loss:0.008 grdn:0.208 lr:3.4e-08 updt_s:0.059 data_s:0.057
+Training:  96%|█████████████████████████████████████████████████▏ | 28919/30000 [58:35<01:55,  9.33step/s]INFO 2026-05-19 02:10:27 ot_train.py:488 step:29K smpl:925K ep:2K epch:11.56 loss:0.010 grdn:0.221 lr:3.3e-08 updt_s:0.060 data_s:0.055
+Training:  96%|█████████████████████████████████████████████████▏ | 28939/30000 [58:37<01:55,  9.17step/s]INFO 2026-05-19 02:10:30 ot_train.py:488 step:29K smpl:926K ep:2K epch:11.56 loss:0.009 grdn:0.214 lr:3.2e-08 updt_s:0.061 data_s:0.056
+Training:  97%|█████████████████████████████████████████████████▏ | 28959/30000 [58:39<01:54,  9.06step/s]INFO 2026-05-19 02:10:32 ot_train.py:488 step:29K smpl:927K ep:2K epch:11.57 loss:0.010 grdn:0.210 lr:3.0e-08 updt_s:0.060 data_s:0.058
+Training:  97%|█████████████████████████████████████████████████▎ | 28979/30000 [58:42<01:50,  9.27step/s]INFO 2026-05-19 02:10:34 ot_train.py:488 step:29K smpl:927K ep:2K epch:11.58 loss:0.009 grdn:0.199 lr:2.9e-08 updt_s:0.060 data_s:0.057
+Training:  97%|█████████████████████████████████████████████████▎ | 28999/30000 [58:44<01:48,  9.25step/s]INFO 2026-05-19 02:10:37 ot_train.py:488 step:29K smpl:928K ep:2K epch:11.59 loss:0.010 grdn:0.224 lr:2.8e-08 updt_s:0.059 data_s:0.061
+Training:  97%|█████████████████████████████████████████████████▎ | 29019/30000 [58:46<01:47,  9.09step/s]INFO 2026-05-19 02:10:39 ot_train.py:488 step:29K smpl:929K ep:2K epch:11.60 loss:0.010 grdn:0.239 lr:2.7e-08 updt_s:0.060 data_s:0.058
+Training:  97%|█████████████████████████████████████████████████▎ | 29039/30000 [58:49<01:45,  9.11step/s]INFO 2026-05-19 02:10:42 ot_train.py:488 step:29K smpl:929K ep:2K epch:11.60 loss:0.010 grdn:0.222 lr:2.6e-08 updt_s:0.060 data_s:0.062
+Training:  97%|█████████████████████████████████████████████████▍ | 29059/30000 [58:51<01:44,  9.01step/s]INFO 2026-05-19 02:10:44 ot_train.py:488 step:29K smpl:930K ep:2K epch:11.61 loss:0.010 grdn:0.215 lr:2.5e-08 updt_s:0.059 data_s:0.062
+Training:  97%|█████████████████████████████████████████████████▍ | 29079/30000 [58:54<01:41,  9.11step/s]INFO 2026-05-19 02:10:46 ot_train.py:488 step:29K smpl:931K ep:2K epch:11.62 loss:0.008 grdn:0.205 lr:2.4e-08 updt_s:0.058 data_s:0.061
+Training:  97%|█████████████████████████████████████████████████▍ | 29099/30000 [58:56<01:39,  9.05step/s]INFO 2026-05-19 02:10:49 ot_train.py:488 step:29K smpl:931K ep:2K epch:11.63 loss:0.008 grdn:0.215 lr:2.3e-08 updt_s:0.058 data_s:0.064
+Training:  97%|█████████████████████████████████████████████████▌ | 29119/30000 [58:59<01:38,  8.90step/s]INFO 2026-05-19 02:10:51 ot_train.py:488 step:29K smpl:932K ep:2K epch:11.64 loss:0.010 grdn:0.226 lr:2.2e-08 updt_s:0.058 data_s:0.063
+Training:  97%|█████████████████████████████████████████████████▌ | 29139/30000 [59:01<01:31,  9.36step/s]INFO 2026-05-19 02:10:54 ot_train.py:488 step:29K smpl:932K ep:2K epch:11.64 loss:0.009 grdn:0.208 lr:2.1e-08 updt_s:0.058 data_s:0.058
+Training:  97%|█████████████████████████████████████████████████▌ | 29159/30000 [59:03<01:33,  9.01step/s]INFO 2026-05-19 02:10:56 ot_train.py:488 step:29K smpl:933K ep:2K epch:11.65 loss:0.008 grdn:0.200 lr:2.0e-08 updt_s:0.059 data_s:0.062
+Training:  97%|█████████████████████████████████████████████████▌ | 29179/30000 [59:06<01:39,  8.24step/s]INFO 2026-05-19 02:10:59 ot_train.py:488 step:29K smpl:934K ep:2K epch:11.66 loss:0.009 grdn:0.210 lr:1.9e-08 updt_s:0.059 data_s:0.078
+Training:  97%|█████████████████████████████████████████████████▋ | 29199/30000 [59:09<01:28,  9.01step/s]INFO 2026-05-19 02:11:01 ot_train.py:488 step:29K smpl:934K ep:2K epch:11.67 loss:0.009 grdn:0.206 lr:1.8e-08 updt_s:0.060 data_s:0.057
+Training:  97%|█████████████████████████████████████████████████▋ | 29219/30000 [59:11<01:28,  8.79step/s]INFO 2026-05-19 02:11:04 ot_train.py:488 step:29K smpl:935K ep:2K epch:11.68 loss:0.009 grdn:0.208 lr:1.7e-08 updt_s:0.059 data_s:0.063
+Training:  97%|█████████████████████████████████████████████████▋ | 29239/30000 [59:13<01:25,  8.87step/s]INFO 2026-05-19 02:11:06 ot_train.py:488 step:29K smpl:936K ep:2K epch:11.68 loss:0.009 grdn:0.196 lr:1.6e-08 updt_s:0.059 data_s:0.061
+Training:  98%|█████████████████████████████████████████████████▋ | 29259/30000 [59:16<01:20,  9.24step/s]INFO 2026-05-19 02:11:08 ot_train.py:488 step:29K smpl:936K ep:2K epch:11.69 loss:0.008 grdn:0.198 lr:1.5e-08 updt_s:0.058 data_s:0.059
+Training:  98%|█████████████████████████████████████████████████▊ | 29279/30000 [59:18<01:23,  8.65step/s]INFO 2026-05-19 02:11:11 ot_train.py:488 step:29K smpl:937K ep:2K epch:11.70 loss:0.010 grdn:0.255 lr:1.5e-08 updt_s:0.058 data_s:0.072
+Training:  98%|█████████████████████████████████████████████████▊ | 29299/30000 [59:21<01:15,  9.30step/s]INFO 2026-05-19 02:11:13 ot_train.py:488 step:29K smpl:938K ep:2K epch:11.71 loss:0.009 grdn:0.208 lr:1.4e-08 updt_s:0.060 data_s:0.056
+Training:  98%|█████████████████████████████████████████████████▊ | 29319/30000 [59:23<01:12,  9.37step/s]INFO 2026-05-19 02:11:16 ot_train.py:488 step:29K smpl:938K ep:2K epch:11.72 loss:0.009 grdn:0.193 lr:1.3e-08 updt_s:0.059 data_s:0.055
+Training:  98%|█████████████████████████████████████████████████▉ | 29339/30000 [59:25<01:13,  9.02step/s]INFO 2026-05-19 02:11:18 ot_train.py:488 step:29K smpl:939K ep:2K epch:11.72 loss:0.008 grdn:0.207 lr:1.2e-08 updt_s:0.059 data_s:0.061
+Training:  98%|█████████████████████████████████████████████████▉ | 29359/30000 [59:28<01:10,  9.12step/s]INFO 2026-05-19 02:11:21 ot_train.py:488 step:29K smpl:940K ep:2K epch:11.73 loss:0.008 grdn:0.196 lr:1.2e-08 updt_s:0.061 data_s:0.057
+Training:  98%|█████████████████████████████████████████████████▉ | 29379/30000 [59:30<01:09,  8.90step/s]INFO 2026-05-19 02:11:23 ot_train.py:488 step:29K smpl:940K ep:2K epch:11.74 loss:0.011 grdn:0.225 lr:1.1e-08 updt_s:0.060 data_s:0.069
+Training:  98%|█████████████████████████████████████████████████▉ | 29399/30000 [59:33<01:07,  8.93step/s]INFO 2026-05-19 02:11:26 ot_train.py:488 step:29K smpl:941K ep:2K epch:11.75 loss:0.010 grdn:0.229 lr:1.0e-08 updt_s:0.059 data_s:0.061
+Training:  98%|██████████████████████████████████████████████████ | 29419/30000 [59:35<01:04,  9.05step/s]INFO 2026-05-19 02:11:28 ot_train.py:488 step:29K smpl:941K ep:2K epch:11.76 loss:0.009 grdn:0.230 lr:9.6e-09 updt_s:0.060 data_s:0.061
+Training:  98%|██████████████████████████████████████████████████ | 29439/30000 [59:38<01:01,  9.08step/s]INFO 2026-05-19 02:11:30 ot_train.py:488 step:29K smpl:942K ep:2K epch:11.76 loss:0.010 grdn:0.228 lr:8.9e-09 updt_s:0.060 data_s:0.059
+Training:  98%|██████████████████████████████████████████████████ | 29459/30000 [59:40<00:56,  9.66step/s]INFO 2026-05-19 02:11:33 ot_train.py:488 step:29K smpl:943K ep:2K epch:11.77 loss:0.009 grdn:0.217 lr:8.3e-09 updt_s:0.059 data_s:0.061
+Training:  98%|██████████████████████████████████████████████████ | 29479/30000 [59:43<00:58,  8.91step/s]INFO 2026-05-19 02:11:35 ot_train.py:488 step:29K smpl:943K ep:2K epch:11.78 loss:0.007 grdn:0.197 lr:7.7e-09 updt_s:0.058 data_s:0.062
+Training:  98%|██████████████████████████████████████████████████▏| 29499/30000 [59:45<00:56,  8.92step/s]INFO 2026-05-19 02:11:38 ot_train.py:488 step:30K smpl:944K ep:2K epch:11.79 loss:0.009 grdn:0.238 lr:7.2e-09 updt_s:0.059 data_s:0.060
+Training:  98%|██████████████████████████████████████████████████▏| 29519/30000 [59:47<00:54,  8.88step/s]INFO 2026-05-19 02:11:40 ot_train.py:488 step:30K smpl:945K ep:2K epch:11.80 loss:0.009 grdn:0.221 lr:6.6e-09 updt_s:0.058 data_s:0.062
+Training:  98%|██████████████████████████████████████████████████▏| 29539/30000 [59:50<00:51,  9.00step/s]INFO 2026-05-19 02:11:42 ot_train.py:488 step:30K smpl:945K ep:2K epch:11.80 loss:0.012 grdn:0.223 lr:6.1e-09 updt_s:0.059 data_s:0.061
+Training:  99%|██████████████████████████████████████████████████▎| 29559/30000 [59:52<00:48,  9.02step/s]INFO 2026-05-19 02:11:45 ot_train.py:488 step:30K smpl:946K ep:2K epch:11.81 loss:0.008 grdn:0.197 lr:5.6e-09 updt_s:0.059 data_s:0.064
+Training:  99%|██████████████████████████████████████████████████▎| 29579/30000 [59:55<00:46,  9.04step/s]INFO 2026-05-19 02:11:47 ot_train.py:488 step:30K smpl:947K ep:2K epch:11.82 loss:0.010 grdn:0.247 lr:5.1e-09 updt_s:0.059 data_s:0.062
+Training:  99%|██████████████████████████████████████████████████▎| 29599/30000 [59:57<00:44,  9.04step/s]INFO 2026-05-19 02:11:50 ot_train.py:488 step:30K smpl:947K ep:2K epch:11.83 loss:0.010 grdn:0.241 lr:4.6e-09 updt_s:0.059 data_s:0.061
+Training:  99%|██████████████████████████████████████████████████▎| 29619/30000 [59:59<00:41,  9.23step/s]INFO 2026-05-19 02:11:52 ot_train.py:488 step:30K smpl:948K ep:2K epch:11.84 loss:0.008 grdn:0.191 lr:4.2e-09 updt_s:0.059 data_s:0.059
+Training:  99%|████████████████████████████████████████████████▍| 29639/30000 [1:00:02<00:38,  9.31step/s]INFO 2026-05-19 02:11:54 ot_train.py:488 step:30K smpl:948K ep:2K epch:11.84 loss:0.009 grdn:0.221 lr:3.8e-09 updt_s:0.059 data_s:0.058
+Training:  99%|████████████████████████████████████████████████▍| 29659/30000 [1:00:04<00:36,  9.42step/s]INFO 2026-05-19 02:11:57 ot_train.py:488 step:30K smpl:949K ep:2K epch:11.85 loss:0.009 grdn:0.215 lr:3.4e-09 updt_s:0.059 data_s:0.057
+Training:  99%|████████████████████████████████████████████████▍| 29679/30000 [1:00:06<00:33,  9.68step/s]INFO 2026-05-19 02:11:59 ot_train.py:488 step:30K smpl:950K ep:2K epch:11.86 loss:0.009 grdn:0.205 lr:3.0e-09 updt_s:0.058 data_s:0.058
+Training:  99%|████████████████████████████████████████████████▌| 29699/30000 [1:00:09<00:35,  8.56step/s]INFO 2026-05-19 02:12:02 ot_train.py:488 step:30K smpl:950K ep:2K epch:11.87 loss:0.008 grdn:0.218 lr:2.6e-09 updt_s:0.057 data_s:0.080
+Training:  99%|████████████████████████████████████████████████▌| 29719/30000 [1:00:12<00:31,  8.99step/s]INFO 2026-05-19 02:12:04 ot_train.py:488 step:30K smpl:951K ep:2K epch:11.88 loss:0.009 grdn:0.208 lr:2.3e-09 updt_s:0.060 data_s:0.058
+Training:  99%|████████████████████████████████████████████████▌| 29739/30000 [1:00:14<00:28,  9.22step/s]INFO 2026-05-19 02:12:07 ot_train.py:488 step:30K smpl:952K ep:2K epch:11.88 loss:0.008 grdn:0.196 lr:2.0e-09 updt_s:0.059 data_s:0.063
+Training:  99%|████████████████████████████████████████████████▌| 29759/30000 [1:00:16<00:26,  8.96step/s]INFO 2026-05-19 02:12:09 ot_train.py:488 step:30K smpl:952K ep:2K epch:11.89 loss:0.009 grdn:0.225 lr:1.7e-09 updt_s:0.061 data_s:0.059
+Training:  99%|████████████████████████████████████████████████▋| 29779/30000 [1:00:19<00:24,  8.91step/s]INFO 2026-05-19 02:12:12 ot_train.py:488 step:30K smpl:953K ep:2K epch:11.90 loss:0.008 grdn:0.176 lr:1.5e-09 updt_s:0.061 data_s:0.061
+Training:  99%|████████████████████████████████████████████████▋| 29799/30000 [1:00:21<00:22,  8.91step/s]INFO 2026-05-19 02:12:14 ot_train.py:488 step:30K smpl:954K ep:2K epch:11.91 loss:0.010 grdn:0.222 lr:1.2e-09 updt_s:0.059 data_s:0.064
+Training:  99%|████████████████████████████████████████████████▋| 29819/30000 [1:00:24<00:20,  8.93step/s]INFO 2026-05-19 02:12:17 ot_train.py:488 step:30K smpl:954K ep:2K epch:11.92 loss:0.008 grdn:0.202 lr:9.9e-10 updt_s:0.059 data_s:0.064
+Training:  99%|████████████████████████████████████████████████▋| 29839/30000 [1:00:26<00:17,  9.22step/s]INFO 2026-05-19 02:12:19 ot_train.py:488 step:30K smpl:955K ep:2K epch:11.92 loss:0.009 grdn:0.222 lr:7.9e-10 updt_s:0.059 data_s:0.061
+Training: 100%|████████████████████████████████████████████████▊| 29859/30000 [1:00:29<00:15,  8.97step/s]INFO 2026-05-19 02:12:21 ot_train.py:488 step:30K smpl:956K ep:2K epch:11.93 loss:0.008 grdn:0.230 lr:6.2e-10 updt_s:0.060 data_s:0.060
+Training: 100%|████████████████████████████████████████████████▊| 29879/30000 [1:00:31<00:13,  8.84step/s]INFO 2026-05-19 02:12:24 ot_train.py:488 step:30K smpl:956K ep:2K epch:11.94 loss:0.008 grdn:0.210 lr:4.6e-10 updt_s:0.060 data_s:0.062
+Training: 100%|████████████████████████████████████████████████▊| 29899/30000 [1:00:34<00:11,  9.14step/s]INFO 2026-05-19 02:12:26 ot_train.py:488 step:30K smpl:957K ep:2K epch:11.95 loss:0.008 grdn:0.220 lr:3.3e-10 updt_s:0.060 data_s:0.059
+Training: 100%|████████████████████████████████████████████████▊| 29919/30000 [1:00:36<00:08,  9.02step/s]INFO 2026-05-19 02:12:29 ot_train.py:488 step:30K smpl:957K ep:2K epch:11.95 loss:0.010 grdn:0.234 lr:2.2e-10 updt_s:0.059 data_s:0.060
+Training: 100%|████████████████████████████████████████████████▉| 29939/30000 [1:00:38<00:06,  8.92step/s]INFO 2026-05-19 02:12:31 ot_train.py:488 step:30K smpl:958K ep:2K epch:11.96 loss:0.011 grdn:0.218 lr:1.3e-10 updt_s:0.061 data_s:0.060
+Training: 100%|████████████████████████████████████████████████▉| 29959/30000 [1:00:41<00:04,  8.67step/s]INFO 2026-05-19 02:12:34 ot_train.py:488 step:30K smpl:959K ep:2K epch:11.97 loss:0.009 grdn:0.217 lr:6.9e-11 updt_s:0.059 data_s:0.064
+Training: 100%|████████████████████████████████████████████████▉| 29979/30000 [1:00:43<00:02,  9.03step/s]INFO 2026-05-19 02:12:36 ot_train.py:488 step:30K smpl:959K ep:2K epch:11.98 loss:0.010 grdn:0.208 lr:2.5e-11 updt_s:0.059 data_s:0.061
+Training: 100%|████████████████████████████████████████████████▉| 29999/30000 [1:00:46<00:00,  9.01step/s]INFO 2026-05-19 02:12:38 ot_train.py:488 step:30K smpl:960K ep:2K epch:11.99 loss:0.009 grdn:0.215 lr:3.4e-12 updt_s:0.060 data_s:0.062
+INFO 2026-05-19 02:12:38 ot_train.py:502 Checkpoint policy after step 30000
+Training: 100%|█████████████████████████████████████████████████| 30000/30000 [1:00:53<00:00,  8.21step/s]
+INFO 2026-05-19 02:12:45 ot_train.py:576 End of training
diff --git a/checkpoints/diffusion_specialized_new_dataset_580/wandb/run-20260519_011148-d8akc176/files/requirements.txt b/checkpoints/diffusion_specialized_new_dataset_580/wandb/run-20260519_011148-d8akc176/files/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..9c53c01b66e04bb7a4e48d324c1eace001a49d7c
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+nvidia-nvtx-cu12==12.8.90
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+xxhash==3.7.0
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new file mode 100644
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+{
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+  "startedAt": "2026-05-19T01:11:48.341609Z",
+  "args": [
+    "--policy.path=/home/shadeform/Desktop/robot_learning/models/checkpoints/diffusion_580_new_dataset_dino/200000/pretrained_model/",
+    "--dataset.repo_id=local/act_collected_episodes",
+    "--dataset.root=/home/shadeform/Desktop/robot_learning/base_new_dataset_134",
+    "--dataset.video_backend=pyav",
+    "--policy.device=cuda",
+    "--policy.push_to_hub=false",
+    "--policy.freeze_vision_encoder=true",
+    "--policy.optimizer_lr=1e-5",
+    "--policy.scheduler_warmup_steps=100",
+    "--output_dir=/home/shadeform/Desktop/robot_learning/outputs/train/diffusion_specialized_eval1",
+    "--job_name=diffusion_specialized_eval1",
+    "--batch_size=32",
+    "--steps=30000",
+    "--num_workers=4",
+    "--log_freq=20",
+    "--save_freq=5000",
+    "--eval_freq=0",
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+  ],
+  "program": "/home/shadeform/Desktop/robot_learning/lerobot/.venv/bin/lerobot-train",
+  "codePath": ".venv/bin/lerobot-train",
+  "codePathLocal": ".venv/bin/lerobot-train",
+  "git": {
+    "remote": "https://github.com/michavol/lerobot.git",
+    "commit": "8d23fd47055bfd22999a0aa8504b2db2a224760e"
+  },
+  "root": "/home/shadeform/Desktop/robot_learning/outputs/train/diffusion_specialized_eval1",
+  "host": "brev-8nneacbnr",
+  "executable": "/home/shadeform/Desktop/robot_learning/lerobot/.venv/bin/python3",
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+  "cpu_count_logical": 16,
+  "gpu": "NVIDIA A100-SXM4-80GB",
+  "gpu_count": 1,
+  "disk": {
+    "/": {
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+  "memory": {
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+}
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diff --git a/checkpoints/diffusion_specialized_new_dataset_580/wandb/run-20260519_011148-d8akc176/files/wandb-summary.json b/checkpoints/diffusion_specialized_new_dataset_580/wandb/run-20260519_011148-d8akc176/files/wandb-summary.json
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