MIRA 训练框架用欺骗性视觉上下文提升多模态推理稳健性
MIRA: Reinforcing Multimodal Reasoning via Deceptive Contextual Augmentation
一篇研究论文：MIRA 训练时故意塞误导性视觉信息，让模型学会自己发现并纠正错误，多模态推理基准平均涨 7%，还不用人工标注。
MIRA 是一个面向多模态推理的训练框架，针对 RLVR 中感知与推理脱节的问题。它在训练时注入经过筛选的欺骗性视觉上下文，构造"hard traps"压力测试模型对误导信息的稳健性。同时引入组级反思触发机制，让模型自主发现视觉线索与逻辑约束的不一致并加以纠正。整套流程不需要人工标注，在多个推理和感知基准上平均提升 7%，视觉中心的基准也有稳定改进。
MIRA: Reinforcing Multimodal Reasoning via Deceptive Contextual Augmentation
While Reinforcement Learning with Verifiable Rewards (RLVR) has significantly advanced multimodal reasoning, existing frameworks suffer from a critical "perception-reasoning gap." Due to an over-reliance on seemingly relevant visual tokens, even minor perceptual perturbations can propagate through the reasoning process, leading to compounding hallucinations. To address this issue, we propose MIRA, a training framework that improves robustness to erroneous visual contexts while enhancing logical reasoning ability. By injecting filtered, deceptive visual contexts during training, we construct challenging "hard traps" that stress-test the model's robustness to misleading information. Furthermore, we introduce a group-wise reflection-triggering mechanism that enables the model to autonomously detect and rectify inconsistencies between visual cues and logical constraints, which is ultimately internalized as an intrinsic policy behavior. Importantly, MIRA does not rely on human annotations. Experimental results across multiple reasoning and perception benchmarks show that MIRA yields an average 7% improvement on in-domain benchmarks, with consistent improvements on vision-centric benchmarks. These findings highlight that active error recovery is essential for robust multimodal reasoning. Comprehensive ablation studies and analyses further provide insights into why MIRA is effective.
