RoboAlign: Learning Test-Time Reasoning for Language-Action Alignment in Vision-Language-Action Models
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| Format: | Preprint |
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2026
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| author | Kim, Dongyoung Park, Sumin Song, Woomin Kim, Seungku Kim, Taeyoung Jang, Huiwon Shin, Jinwoo Kim, Jaehyung Seo, Younggyo |
| author_facet | Kim, Dongyoung Park, Sumin Song, Woomin Kim, Seungku Kim, Taeyoung Jang, Huiwon Shin, Jinwoo Kim, Jaehyung Seo, Younggyo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_21341 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | RoboAlign: Learning Test-Time Reasoning for Language-Action Alignment in Vision-Language-Action Models Kim, Dongyoung Park, Sumin Song, Woomin Kim, Seungku Kim, Taeyoung Jang, Huiwon Shin, Jinwoo Kim, Jaehyung Seo, Younggyo Artificial Intelligence 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. |
| title | RoboAlign: Learning Test-Time Reasoning for Language-Action Alignment in Vision-Language-Action Models |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2603.21341 |