PolicyTrim: Boosting Intrinsic Policy Efficiency of Vision-Language-Action Models
PolicyTrim is a reinforcement learning-based framework that enhances VLA model efficiency by extending reliable action chunk lengths and reducing redundant physical steps through d…
Hugging Face · Daily Papers
·Xianghui Wang, Feng Chen
·
·▲ 2 upvotes
Este artigo está em destaque na seleção diária de papers do Hugging Face, curada pela comunidade de pesquisa em IA.
Autores: Xianghui Wang, Feng Chen, Wenbo Zhang, Hua Yan, Zixuan Wang, Changsheng Li
- 2 upvotes da comunidade
- Temas: Vision-Language-Action models, policy efficiency, action chunk length, physical steps, reinforcement learning, dynamic exploration
Resumo
Resumo original (em inglês), extraído do paper:
PolicyTrim is a reinforcement learning-based framework that enhances VLA model efficiency by extending reliable action chunk lengths and reducing redundant physical steps through dynamic exploration and redundancy-aware rewards.
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