ArXiv

PriorVLA: Prior-Preserving Adaptation for Vision-Language-Action Models

2026-05-11 · 17:56 UTC ·Xinyu Guo, Bin Xie, Wei Chai... ·1 min read

Authors: Xinyu Guo, Bin Xie, Wei Chai...
Categories: cs.RO
arXiv: https://arxiv.org/abs/2605.10925v1
PDF: https://arxiv.org/pdf/2605.10925v1

Brief

PriorVLA introduces a prior-preserving adaptation method for Vision-Language-Action models that freezes a Prior Expert and trains an Adaptation Expert, using Expert Queries to inject pretrained scene and motor priors. By updating only 25% of parameters, it outperforms full fine-tuning and SOTA baselines on RoboTwin 2.0, LIBERO, and eight real-world tasks, with strong OOD and few-shot gains.

Why it matters

PriorVLA is a prior-preserving adaptation framework that keeps a frozen Prior Expert and trains an Adaptation Expert using Expert Queries to integrate pretrained scene and motor priors, while updating only 25% of the parameters compared to full fine-tuning.

Key details

On benchmarks, PriorVLA outperforms full fine-tuning and SOTA VLA baselines: it improves over pi0.5 by 11 points on RoboTwin 2.0-Hard and achieves 99.1% average success on LIBERO.
In real-world evaluation across eight tasks and two embodiments, PriorVLA attains 81% in-distribution (ID) and 57% out-of-distribution (OOD) success with standard data; with 10 demonstrations per task it reaches 48% ID and 32% OOD, surpassing pi0.5 by 24 and 22 points respectively.

Source evidence

Abstract

Large-scale pretraining has made Vision-Language-Action (VLA) models promising foundations for generalist robot manipulation, yet adapting them to downstream tasks remains necessary. However, the common practice of full fine-tuning treats pretraining as initialization and can shift broad priors toward narrow training-distribution patterns. We propose PriorVLA, a novel framework that preserves pretrained priors and learns to leverage them for effective adaptation. PriorVLA keeps a frozen Prior Expert as a read-only prior source and trains an Adaptation Expert for downstream specialization. Expert Queries capture scene priors from the pretrained VLM and motor priors from the Prior Expert, integrating both into the Adaptation Expert to guide adaptation. Together, PriorVLA updates only 25% of the parameters updated by full fine-tuning. Across RoboTwin 2.0, LIBERO, and real-world tasks, PriorVLA achieves stronger overall performance than full fine-tuning and state-of-the-art VLA baselines, with the largest gains under out-of-distribution (OOD) and few-shot settings. PriorVLA improves over pi0.5 by 11 points on RoboTwin 2.0-Hard and achieves 99.1% average success on LIBERO. Across eight real-world tasks and two embodiments, PriorVLA reaches 81% in-distribution (ID) and 57% OOD success with standard data. With only 10 demonstrations per task, PriorVLA reaches 48% ID and 32% OOD success, surpassing pi0.5 by 24 and 22 points, respectively.

Comment: 32 pages. Project page: https://priorvla.github.io/