ReFineVLA: Multimodal Reasoning-Aware Generalist Robotic Policies via Teacher-Guided Fine-Tuning

TMLR Paper5107 Authors

Abstract: Vision-Language-Action (VLA) models have gained much attention from the research community thanks to their strength in translating multimodal observations with linguistic instructions into desired robotic actions. Despite their advancements, VLAs often overlook explicit reasoning and learn the functional input-action mappings, omitting crucial logical steps, which are especially pronounced in interpretability and generalization for complex, long-horizon manipulation tasks. In this work, we propose ReFineVLA, a multimodal reasoning-aware framework that fine-tunes VLAs with teacher-guided reasons. We first augment robotic datasets with reasoning rationales generated by an expert teacher model, guiding VLA models to learn to reason about their actions. Then, we fine-tune pre-trained VLAs with the reasoning-enriched datasets with ReFineVLA, while maintaining the underlying generalization abilities and boosting reasoning capabilities. We also conduct attention map visualization to analyze the alignment among visual observation, linguistic prompts, and to-be-executed actions of ReFineVLA, reflecting the model's ability to focus on relevant tasks and actions. Through this additional step, we explore that ReFineVLA-trained models exhibit a meaningful agreement between vision-language and action domains, highlighting the enhanced multimodal understanding and generalization. Evaluated across a suite of simulated manipulation benchmarks on SimplerEnv with both WidowX and Google Robot tasks, ReFineVLA achieves state-of-the-art performance, with an average 5.0% improvement in success rate over the second-best method on the WidowX benchmark, reaching 47.7% task success. In more visually and contextually diverse scenarios, ReFineVLA yields 3.5% and 2.3% gains in variant aggregation 68.8% and visual matching 76.6% settings, respectively. Notably, it improves performance by 9.6% on the Move Near task and 8.2% on Open/Close Drawer in challenging settings. The source code, models, and all datasets are released anonymously in the appendix materials and will be made publicly available.

Submission Length: Regular submission (no more than 12 pages of main content)

Assigned Action Editor: ~Adam_W_Harley1

Submission Number: 5107