Improving Pre-Trained Vision-Language-Action Policies with Model-Based Search

• URL: http://arxiv.org/abs/2508.12211v1
• Date: Sun, 17 Aug 2025 02:59:42 GMT
• Title: Improving Pre-Trained Vision-Language-Action Policies with Model-Based Search
• Authors: Cyrus Neary, Omar G. Younis, Artur Kuramshin, Ozgur Aslan, Glen Berseth,
• Abstract summary: We present Vision-Language-Action Planning & Search (VLAPS)<n>It embeds model-based search into the inference procedure of pre-trained VLA policies.<n>VLAPS significantly outperforms VLA-only baselines on language-specified tasks.
• Score: 7.9342097024286815
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pre-trained vision-language-action (VLA) models offer a promising foundation for generalist robot policies, but often produce brittle behaviours or unsafe failures when deployed zero-shot in out-of-distribution scenarios. We present Vision-Language-Action Planning & Search (VLAPS) -- a novel framework and accompanying algorithms that embed model-based search into the inference procedure of pre-trained VLA policies to improve their performance on robotic tasks. Specifically, our method biases a modified Monte Carlo Tree Search (MCTS) algorithm -- run using a model of the target environment -- using action priors defined by the VLA policy. By using VLA-derived abstractions and priors in model-based search, VLAPS efficiently explores language-conditioned robotics tasks whose search spaces would otherwise be intractably large. Conversely, by integrating model-based search with the VLA policy's inference procedure, VLAPS yields behaviours that are more performant than those obtained by directly following the VLA policy's action predictions. VLAPS offers a principled framework to: i) control test-time compute in VLA models, ii) leverage a priori knowledge of the robotic environment, and iii) integrate established planning and reinforcement learning techniques into the VLA inference process. Across all experiments, VLAPS significantly outperforms VLA-only baselines on language-specified tasks that would otherwise be intractable for uninformed search algorithms, increasing success rates by as much as 67 percentage points.

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