Improving Medical Visual Reinforcement Fine-Tuning via Perception and Reasoning Augmentation

2026-02-11

AI Summary

Existing reinforcement fine-tuning (RFT) methods struggle to simultaneously achieve precise perception and structured reasoning in medical vision tasks, limiting the deployment of multimodal large models in high-stakes clinical settings. This work proposes VRFT-Aug, a novel framework that introduces, for the first time, a synergistic enhancement mechanism coupling perception and reasoning into medical visual RFT. By integrating prior knowledge injection, perception-driven policy optimization, clinically informed reward shaping, and behavioral imitation, VRFT-Aug establishes a generalizable paradigm for training strategies and reward design. Experiments demonstrate that the proposed method significantly outperforms standard supervised fine-tuning and conventional RFT approaches across multiple medical datasets, offering transferable insights for training robust, clinically aligned vision-language models.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

While recent advances in Reinforcement Fine-Tuning (RFT) have shown that rule-based reward schemes can enable effective post-training for large language models, their extension to cross-modal, vision-centric domains remains largely underexplored. This limitation is especially pronounced in the medical imaging domain, where effective performance requires both robust visual perception and structured reasoning. In this work, we address this gap by proposing VRFT-Aug, a visual reinforcement fine-tuning framework tailored for the medical domain. VRFT-Aug introduces a series of training strategies designed to augment both perception and reasoning, including prior knowledge injection, perception-driven policy refinement, medically informed reward shaping, and behavioral imitation. Together, these methods aim to stabilize and improve the RFT process. Through extensive experiments across multiple medical datasets, we show that our approaches consistently outperform both standard supervised fine-tuning and RFT baselines. Moreover, we provide empirically grounded insights and practical training heuristics that can be generalized to other medical image tasks. We hope this work contributes actionable guidance and fresh inspiration for the ongoing effort to develop reliable, reasoning-capable models for high-stakes medical applications.

Problem

• Reinforcement Fine-Tuning
• Medical Imaging
• Visual Perception
• Structured Reasoning
• Cross-modal Learning

Innovation

• Visual Reinforcement Fine-Tuning
• Perception-Reasoning Augmentation
• Medical Imaging
• Reward Shaping
• Cross-modal Learning