docs: expand PolicyTrim model card

README.md

@@ -1,3 +1,139 @@
 ---
 license: apache-2.0
+language:
+  - en
+library_name: pytorch
+tags:
+  - robotics
+  - vision-language-action
+  - reinforcement-learning
+  - grpo
+  - policy-efficiency
+  - embodied-ai
+  - libero
+  - maniskill
+  - metaworld
+  - openpi
+  - openvla
+arxiv: 2606.22540
 ---
+
+<div align="center">
+
+# PolicyTrim
+
+### Boosting Intrinsic Policy Efficiency of Vision-Language-Action Models
+
+[![Paper](https://img.shields.io/badge/arXiv-2606.22540-b31b1b.svg)](https://arxiv.org/abs/2606.22540)
+[![GitHub](https://img.shields.io/badge/GitHub-PolicyTrim-181717.svg?logo=github)](https://github.com/INCEPTIONwang/PolicyTrim)
+[![Project Page](https://img.shields.io/badge/Project-Page-2563eb.svg)](https://inceptionwang.github.io/PolicyTrim/)
+[![Hugging Face Paper](https://img.shields.io/badge/Hugging%20Face-Paper-ffcc4d.svg)](https://huggingface.co/papers/2606.22540)
+[![License](https://img.shields.io/badge/License-Apache--2.0-green.svg)](https://github.com/INCEPTIONwang/PolicyTrim/blob/main/LICENSE)
+
+**Xianghui Wang\***, **Feng Chen\***, Wenbo Zhang, Hua Yan, Zixuan Wang<sup>†</sup>, Changsheng Li, Yinjie Lei<sup>‡</sup>
+
+<sup>*</sup> Equal contribution · <sup>†</sup> Project lead · <sup>‡</sup> Corresponding author
+
+</div>
+
+## Model Card
+
+This repository provides the released post-training actor checkpoints for
+**PolicyTrim**, a two-stage reinforcement learning framework for improving the
+intrinsic policy efficiency of Vision-Language-Action (VLA) models.
+
+Most deployment-efficiency methods reduce the latency of each model forward
+pass. PolicyTrim instead reduces how many inference calls and physical actions
+are required to finish a task. It targets two policy-level bottlenecks:
+
+1. unreliable predictions near the tail of an action chunk;
+2. redundant physical execution steps and corrective actions.
+
+PolicyTrim first extends the reliable executable action horizon, then applies a
+redundancy-aware step-saving objective with stability regularization. Across
+three benchmarks and three VLA model families, the method reports:
+
+- **3x** improvement in action chunk utilization;
+- **51.4%** reduction in physical execution steps;
+- up to **5.83x** end-to-end deployment speedup;
+- no compromise in task success rates.
+
+For the method, training code, configuration files, and evaluation scripts, see
+the [PolicyTrim GitHub repository](https://github.com/INCEPTIONwang/PolicyTrim).
+
+<div align="center">
+  <img src="https://raw.githubusercontent.com/INCEPTIONwang/PolicyTrim/main/overview_01.png" alt="PolicyTrim overview" width="100%"/>
+</div>
+
+## Resources
+
+- **Paper:** [arXiv:2606.22540](https://arxiv.org/abs/2606.22540)
+- **PDF:** [PolicyTrim paper](https://arxiv.org/pdf/2606.22540)
+- **Project page:** [inceptionwang.github.io/PolicyTrim](https://inceptionwang.github.io/PolicyTrim/)
+- **Code:** [github.com/INCEPTIONwang/PolicyTrim](https://github.com/INCEPTIONwang/PolicyTrim)
+- **Hugging Face paper page:** [huggingface.co/papers/2606.22540](https://huggingface.co/papers/2606.22540)
+
+## Download
+
+Install the Hugging Face Hub CLI:
+
+```bash
+pip install -U huggingface_hub
+```
+
+Download the complete repository:
+
+```bash
+hf download INCEPTIONwang/PolicyTrim \
+  --local-dir ./PolicyTrim-checkpoints
+```
+
+The complete repository is large. To download only one checkpoint, specify its
+path. For example:
+
+```bash
+hf download INCEPTIONwang/PolicyTrim \
+  libero_goal_grpo_openpi_pi05/checkpoints/global_step_500/actor/model_state_dict/full_weights.pt \
+  --local-dir ./PolicyTrim-checkpoints
+```
+
+Python equivalent:
+
+```python
+from huggingface_hub import hf_hub_download
+
+checkpoint_path = hf_hub_download(
+    repo_id="INCEPTIONwang/PolicyTrim",
+    filename=(
+        "libero_goal_grpo_openpi_pi05/checkpoints/global_step_500/"
+        "actor/model_state_dict/full_weights.pt"
+    ),
+)
+```
+
+## Loading and Evaluation
+
+Checkpoint restoration depends on the matching VLA backend and distributed
+training configuration. Follow the setup and evaluation instructions in the
+[GitHub README](https://github.com/INCEPTIONwang/PolicyTrim#installation), then
+point the corresponding PolicyTrim configuration to the downloaded checkpoint.
+
+## License
+
+The released materials are provided under the
+[Apache License 2.0](https://github.com/INCEPTIONwang/PolicyTrim/blob/main/LICENSE).
+Users are also responsible for complying with the licenses and terms of the
+corresponding base VLA models, datasets, and simulation environments.
+
+## Citation
+
+If you find PolicyTrim useful, please cite:
+
+```bibtex
+@inproceedings{policytrim2026,
+  title     = {PolicyTrim: Boosting Intrinsic Policy Efficiency of Vision-Language-Action Models},
+  author    = {Xianghui Wang and Feng Chen and Wenbo Zhang and Hua Yan and Zixuan Wang and Changsheng Li and Yinjie Lei},
+  booktitle = {European Conference on Computer Vision (ECCV)},
+  year      = {2026}
+}
+```