从失败到反馈：组修正解锁目标级视觉定位中的困难案例
From Failure to Feedback: Group Revision Unlocks Hard Cases in Object-Level Grounding
提出组修正优化范式，通过生成修正候选并量化改进来构造塑形信号，缓解GRPO在困难样本上奖励稀疏的问题，可复用于视觉语言模型的强化学习微调。
原文依据 we propose a group-revision optimisation paradigm that enhances learning on hard cases
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论使用强化学习微调大型视觉语言模型已成为增强其目标级视觉定位能力的一种有前景的方法。然而，现有方法主要基于GRPO，在响应级别分配奖励。这种稀疏奖励导致在挑战性场景中所有候选响应均失败时学习信号极少。在本工作中，我们提出一种组修正优化范式，以增强对困难案例的学习。它从采样的初始响应开始，生成一组修正候选以探索改进的定位结果。受奖励塑形启发，我们引入一个巩固过程，量化每个候选相对于初始尝试的改进，并将其转换为信息丰富的塑形信号。这些信号用于细化奖励并调制优势，放大高质量修正的影响。与先前基于GRPO的模型相比，我们的方法在指代分割和推理分割、REC以及计数基准上取得了一致性提升。代码可在 https://github.com/yyliu01/GroupRevision 获取。
英文原文摘要 1114 字符
Finetuning Large Vision-Language Models with reinforcement learning has emerged as a promising approach to enhance their capability in object-level grounding. However, existing methods, mainly based on GRPO, assign rewards at the response level. Such sparse reward leads to minimal learning signals when all candidate responses are failed in challenging scenarios.In this work, we propose a group-revision optimisation paradigm that enhances learning on hard cases. It begins with a sampled initial response and generates a set of revised candidates to explore improved grounding outcomes. Inspired by reward shaping, we introduce a consolidation process that quantifies each candidate's improvement over the initial attempt and converts it into informative shaping signals.These signals are used to both refine the reward and modulate the advantage, amplifying the influence of high-quality revisions.Our method achieves consistent gains across referring and reasoning segmentation, REC, and counting benchmarks compared with prior GRPO-based models. Code is available at https://github.com/yyliu01/GroupRevision.