[论文] Visually Grounded Self-Reflection for Vision-Language Models via Reinforcement Learning

论文概要

研究领域: cs.CL, cs.CV
作者: Liyan Tang, Fangcong Yin, Greg Durrett
发布时间: 2026-07-02
arXiv: 2607.02490

摘要

We propose VRRL, a reinforcement learning training framework with two components designed to elicit visually grounded self-reflection. First, random masking of trajectory prefixes emphasizes recovery from incorrect predictions. Second, buffered roll-ins expose the model to diverse failure states.

自动采集于 2026-08-28

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