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

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.

Figures (7)

• Figure 1: Performance on LIBERO. VLAs built upon MLLMs specialized for embodied reasoning (fine-tuned variants of Qwen2.5-VL-7B-Instruct) fail to significantly improve performance and often degrade it compared to the baseline VLA based on the original model. In contrast, RoboAlign achieves significant gains, as detailed in Section \ref{['sec:experiment']}.
• Figure 2: Overview of RoboAlign framework.RoboAlign directly aligns MLLM representations with low-level action generation using reasoning-incentivized reinforcement learning guo2025deepseek. The framework consists of two stages: (i) Stage 1 integrates embodied reasoning, zero-shot reasoning, and FAST-tokenized low-level action generation via supervised fine-tuning, and (ii) Stage 2 optimizes responses through reinforcement learning to improve token-level action accuracy and better alignment. The resulting model serves as an MLLM tailored for effective VLA training.
• Figure 3: Examples of observations. Visual inputs for training and evaluation (from left to right): BridgeV2 for FAST token training, CALVIN, LIBERO benchmark, and the real-robot.
• Figure 4: Summary of VLA performance. Comparison of VLA performance across different MLLM training methods on LIBERO and CALVIN. RoboAlign achieves the highest gains.
• Figure 5: Prompt for FAST token generation. We use this prompt template for both FAST token prediction and reinforcement learning.