PROVENANCE.md

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.