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Breaking Failure Cascades: Step-Aware Reinforcement Learning for Medical Multimodal Reasoning
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
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