AI看病不再一步错步步错
AI看医学影像时，经常第一步推理就错了，然后一路错到底。这篇论文发现，在医疗视觉问答中，64%的错误源于早期推理的连锁崩溃。研究者提出一种强化学习算法MRPO，当最终答案错误时，它会重点惩罚最早出错的推理步骤，而不是平均惩罚所有步骤。结果早期推理失败率从64%降到13%，在多个模型上效果都超过现有方法。它不是你明天能用上的工具，但揭示了一个关键思路：纠正AI的推理过程比只纠正答案更有效。
📄 原文摘要(英文)
Recent multimodal large language models have shown great promise in clinical image reasoning, but existing post-training pipelines remain predominantly outcome-centric, relying on final answer correctness or sequence-level preferences. This suffers from sparse credit assignment, making it difficult to optimize the reasoning process essential for clinical applications. Our analysis reveals that cascading errors from early-stage reasoning failures are a leading cause of incorrect predictions in medical visual question answering (VQA) benchmarks. Motivated by this, we propose Medical Reasoning-aware Policy Optimization (MRPO), an RL algorithm that incorporates step-wise process rewards. When the final answer is incorrect, MRPO assigns exponentially larger penalties to tokens in earlier invalid reasoning steps, breaking failure cascades without compromising successful paths. Across three multimodal LLM backbones, MRPO consistently outperforms standard GRPO and a recent RL baseline, and on Qwen3-VL-8B-Instruct even surpasses substantially larger medical MLLMs such as HuatuoGPT-Vision-34B by 2.79 points. Moreover, MRPO reduces early-stage reasoning failures from 64.0% to 13.0%, showing that targeted mitigation of cascading failures improves both reasoning quality and final answer accuracy. Our code is available at https://github.com/dmis-lab/MRPO