---
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
---
# PolicyTrim
### Boosting Intrinsic Policy Efficiency of Vision-Language-Action Models
[](https://arxiv.org/abs/2606.22540)
[](https://github.com/INCEPTIONwang/PolicyTrim)
[](https://inceptionwang.github.io/PolicyTrim/)
[](https://huggingface.co/papers/2606.22540)
[](https://github.com/INCEPTIONwang/PolicyTrim/blob/main/LICENSE)
**Xianghui Wang\***, **Feng Chen\***, Wenbo Zhang, Hua Yan, Zixuan Wang†, Changsheng Li, Yinjie Lei‡
* Equal contribution · † Project lead · ‡ Corresponding author
## 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).
## 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}
}
```
