RoboAlign: Learning Test-Time Reasoning for Language-Action Alignment in Vision-Language-Action Models

• URL: http://arxiv.org/abs/2603.21341v1
• Date: Sun, 22 Mar 2026 17:57:55 GMT
• Title: RoboAlign: Learning Test-Time Reasoning for Language-Action Alignment in Vision-Language-Action Models
• Abstract summary: RoboAlign trains vision-language-action models (VLAs) to translate multimodal understanding into low-level actions.<n>Our key idea is to sample action tokens via zero-shot natural language reasoning and refine this reasoning using reinforcement learning (RL) to improve action accuracy.<n>RoboAlign achieves performance improvements of 17.5%, 18.9%, and 106.6% over SFT baselines on LIBERO, CALVIN, and real-world environments, respectively.
• Score: 58.83401587988675
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Improving embodied reasoning in multimodal-large-language models (MLLMs) is essential for building vision-language-action models (VLAs) on top of them to readily translate multimodal understanding into low-level actions. Accordingly, recent work has explored enhancing embodied reasoning in MLLMs through supervision of vision-question-answering type. However, these approaches have been reported to result in unstable VLA performance, often yielding only marginal or even negative gains. In this paper, we propose a more systematic MLLM training framework RoboAlign that reliably improves VLA performance. Our key idea is to sample action tokens via zero-shot natural language reasoning and refines this reasoning using reinforcement learning (RL) to improve action accuracy. As a result, RoboAlign bridges the modality gap between language and low-level actions in MLLMs, and facilitate knowledge transfer from MLLM to VLA. To validate the effectiveness of RoboAlign, we train VLAs by adding a diffusion-based action head on top of an MLLM backbone and evaluate them on major robotics benchmarks. Remarkably, by performing RL-based alignment after SFT using less than 1\% of the data, RoboAlign achieves performance improvements of 17.5\%, 18.9\%, and 106.6\% over SFT baselines on LIBERO, CALVIN, and real-world environments, respectively.

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