[2603.21341] RoboAlign: Learning Test-Time Reasoning for Language-Action Alignment in Vision-Language-Action Models

Computer Science > Artificial Intelligence arXiv:2603.21341 (cs) [Submitted on 22 Mar 2026] Title:RoboAlign: Learning Test-Time Reasoning for Language-Action Alignment in Vision-Language-Action Models Authors:Dongyoung Kim, Sumin Park, Woomin Song, Seungku Kim, Taeyoung Kim, Huiwon Jang, Jinwoo Shin, Jaehyung Kim, Younggyo Seo View a PDF of the paper titled RoboAlign: Learning Test-Time Reasoning for Language-Action Alignment in Vision-Language-Action Models, by Dongyoung Kim and 8 other authors View PDF HTML (experimental) 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...