| # InternW0 LIBERO checkpoint provenance |
|
|
| ## Selected artifact |
|
|
| `InternW0-Libero.pt` is the model-only checkpoint selected from LIBERO run |
| `w0-lib-gl-rf-c16-e10-32g-r13` at step 600. |
|
|
| Step 600 was selected over the final step 660 because it had the highest |
| complete four-suite evaluation average and the largest number of successful |
| rollouts: |
|
|
| | Checkpoint | Spatial | Object | Goal | Long | Average | Successes | |
| |---|---:|---:|---:|---:|---:|---:| |
| | Step 600 (selected) | 99.4% | 99.4% | 98.6% | 97.0% | 98.6% | 1,972 / 2,000 | |
| | Step 660 | 99.0% | 99.4% | 98.4% | 97.2% | 98.5% | 1,970 / 2,000 | |
|
|
| ## Lineage |
|
|
| 1. Heterogeneous InternW0 pretraining at global step 30,000. |
| 2. LIBERO target-domain initialization with `new_domain` action/proprio |
| interfaces, seed 42. |
| 3. Earlier LIBERO C660 run, selected at step 2,640. |
| 4. R13 Goal+Long run, initialized from the step-2,640 LIBERO checkpoint and |
| selected at step 600. |
|
|
| The historical source implementation was `ahawam`; the public runtime is `w0`. |
| The source reader and public `w0` reader have identical Python ASTs apart from |
| non-semantic comments. The resolved training config is preserved in |
| `source_training_config.yaml`. |
|
|
| The checkpoint embeds `behavior_config`, `training_recipe`, |
| `posttrain_statistics`, and `posttrain_provenance`. These fields are exported |
| verbatim to `checkpoint_metadata.json`. |
|
|
| ## Training contract |
|
|
| - LIBERO suites used for posttraining: `libero_goal` and `libero_10`. |
| - Random-frame sampling with 100,000 samples per epoch. |
| - 10 epochs. |
| - Batch size 48 per process. |
| - Learning rate 5e-5, cosine schedule. |
| - Seed 42. |
| - Native 20 Hz EEF10 representation. |
| - 37-dimensional canonical action/proprio interface. |
| - Native EEF10 slots: |
| `[7, 8, 9, 10, 11, 12, 13, 14, 15, 16]`. |
| - Action horizon 64, chunk size 16, video plan horizon 96, video/action stride 8. |
| - Maximum action offset 15. |
| - K=8 target soft prompts. |
|
|
| The matching normalization file is `normalization.json`. |
|
|
| ## Evaluation record |
|
|
| The complete evaluation uses all 40 tasks in the four official LIBERO suites, |
| 50 trials per task, for 2,000 stochastic rollouts. The evaluation uses: |
|
|
| - environment/init seed 42; |
| - unset policy diffusion seed; |
| - 30 settling steps with `[0, 0, 0, 0, 0, 0, -1]`; |
| - execution of all 16 actions per predicted chunk; |
| - 10 inference steps; |
| - runtime gripper binarization; |
| - lossless PNG image transport; |
| - 400-step limits for Spatial/Object/Goal and 700 for `libero_10`; |
| - MuJoCo 3.3.2. |
|
|
| The full per-task record is included in `evaluation_summary.json`, and its |
| launch metadata is included in `evaluation_meta.json`. |
|
|
| ## Excluded from this repository |
|
|
| The checkpoint package does not include optimizer state, distributed training |
| state, logs, LIBERO data, Wan2.2 files, DINOv3 weights, text caches, or the |
| external LIBERO checkout. |
|
|
