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This paper addresses the problem of tool failures in medical AI agents by proposing a GRPO-based reinforcement learning framework that leverages instance-level selection, disagreement-aware synergy learning, and entropy-guided sampling to correct erroneous tool consensus and improve reliability across seven medical benchmarks.