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arXiv Computer Vision By Zihao Lin, Jingyuan Liu, Yucheng Zhang, Yuguang Yao, Lifu Huang

Confident First, Reflective Second: Reinforcement Learning of Confidence-Preserving Reflection for Video Understanding

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The paper introduces CoFire, a reinforcement learning framework for video understanding that addresses the problem of confidence erosion—where initial predictions lose reliability during iterative refinement. CoFire enforces a "confident first, reflective second" principle, ensuring the initial answer remains trustworthy and that subsequent reflection only improves upon it. The method achieves state‑of‑the‑art accuracy on Video‑MME, MLVU, and LongVideoBench, outperforming prior 7B models by 1.7 points and improving both initial and final accuracy over outcome‑reward RL by 4.1 and 3.7 points respectively.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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