ConRFT: A Reinforced Fine-tuning Method for VLA Models via Consistency Policy

• URL: http://arxiv.org/abs/2502.05450v2
• Date: Mon, 14 Apr 2025 04:53:32 GMT
• Title: ConRFT: A Reinforced Fine-tuning Method for VLA Models via Consistency Policy
• Abstract summary: Vision-Language-Action (VLA) models have shown substantial potential in real-world robotic manipulation.<n>Fine-tuning these models through supervised learning struggles to achieve robust performance due to limited, inconsistent demonstrations.<n>We propose a reinforced fine-tuning approach for VLA models, named ConRFT, which consists of offline and online fine-tuning.
• Score: 10.596344084789434
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Vision-Language-Action (VLA) models have shown substantial potential in real-world robotic manipulation. However, fine-tuning these models through supervised learning struggles to achieve robust performance due to limited, inconsistent demonstrations, especially in contact-rich environments. In this paper, we propose a reinforced fine-tuning approach for VLA models, named ConRFT, which consists of offline and online fine-tuning with a unified consistency-based training objective, to address these challenges. In the offline stage, our method integrates behavior cloning and Q-learning to effectively extract policy from a small set of demonstrations and stabilize value estimating. In the online stage, the VLA model is further fine-tuned via consistency policy, with human interventions to ensure safe exploration and high sample efficiency. We evaluate our approach on eight diverse real-world manipulation tasks. It achieves an average success rate of 96.3% within 45-90 minutes of online fine-tuning, outperforming prior supervised methods with a 144% improvement in success rate and 1.9x shorter episode length. This work highlights the potential of integrating reinforcement learning to enhance the performance of VLA models for real-world robotic applications. Videos and code are available at our project website https://cccedric.github.io/conrft/.

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