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README.md

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+---
+tags:
+- robotics
+- libero
+- pi0
+- alam
+library_name: jax
+---
+
+# ALAM + pi0 post-trained for LIBERO
+
+Repository ID: `Mark-ZJTang/alam_plus_pi_libero`.
+
+Inference-only step-30,000 Orbax checkpoint used as the shared checkpoint for the
+LIBERO Table 9 release configuration. Training effective horizon is 20. At
+inference, Spatial/Object use H=14 and Goal/Long use H=18; replan steps are 5,
+10, 7, and 12 respectively.
+
+The public full post-training resource contract is 8 GPUs. Inference and the
+documented CUDA/EGL acceptance use one sufficiently large idle GPU.
+
+The checkpoint includes `params`, `_CHECKPOINT_METADATA`, and
+`assets/libero_real/norm_stats.json`; optimizer `train_state` is intentionally
+excluded. The matching ALAM tokenizer is `libero_epoch16_step49024`.
+
+The available historical evidence supports the shared-checkpoint release
+configuration, but the Spatial server identity was not recorded in its client
+log; this provenance limitation is documented in the GitHub release. License
+metadata must be completed before public publication.
+
+From the matching GitHub code checkout:
+
+```bash
+.venvs/publish/bin/python workflows/publishing/download_huggingface.py \
+  --artifact alam_pretrain \
+  --artifact alam_plus_pi_libero
+bash workflows/libero_evaluation/evaluate_all_suites.sh --gpu 0
+```
+
+Each suite uses 10 tasks x 50 trials. Recorded successes are Spatial 496/500
+(99.2%), Object 498/500 (99.6%), Goal 495/500 (99.0%), and Long 472/500
+(94.4%), for a 98.05% mean reported as 98.1%.