license: apache-2.0
pipeline_tag: robotics
PolicyTrim: Boosting Intrinsic Policy Efficiency of Vision-Language-Action Models
This repository contains the models and codebase for PolicyTrim, a reinforcement learning-based post-training framework that extends the reliable action chunk length and reduces redundant physical steps for Vision-Language-Action (VLA) models.
- Paper: PolicyTrim: Boosting Intrinsic Policy Efficiency of Vision-Language-Action Models
- Project Page: https://inceptionwang.github.io/PolicyTrim/
- Repository: https://github.com/INCEPTIONwang/PolicyTrim
Introduction
PolicyTrim is a two-stage RL post-training framework designed to reduce the number of inference calls required to complete robotic manipulation tasks. It targets two key factors:
- Reliable action chunk extension: Progressively probes longer execution windows and rewards successful rollouts that sustain longer reliable chunks.
- Redundancy-aware step reduction: Encourages successful trajectories that reach the goal in fewer physical steps while discouraging fragile, non-reproducible shortcuts.
Across three benchmarks (including LIBERO and ManiSkill) and three VLA models (OpenPI, OpenVLA-OFT, and GR00T), PolicyTrim improves action chunk utilization by 3×, reduces physical execution steps by 51.4%, and achieves up to a 5.83× end-to-end deployment speedup without sacrificing task success rates.
Citation
If you find PolicyTrim helpful, please cite the following work:
@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}
}