arXiv:2609.39168v1 Announce Type: new
Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has improved the reasoning capabilities of Multimodal Large Language Models (MLLMs), yet existing...
By Zhihan Zhang, Lizi Liao
arXiv:2606. 29984v1 Announce Type: new Abstract: Reinforcement Learning (RL) is an important paradigm for improving the reasoning capabilities of Vision-Language Models (VLMs).
By Peng, Lee, Yin Zhang, Yanglin Zhang, Haonan Wu, Zishan Liu, Ruoxi Zang, Xin Zhu, Jiayin Zheng, Jian Yao, Zefeng Ji, Fei Ma
The paper introduces GRIT, a training framework that adds visual grounding to reinforcement learning with verifiable rewards (RLVR) for vision-language models, addressing the issue of Correct-but-Visually-Misgrounded Reasoning (CVMR). CVMR occurs when models produce correct final answers while misperceiving visual information during intermediate reasoning steps, a problem that standard RLVR rewards cannot detect. By analyzing token-level attention patterns, the authors show that CVMR tokens differ from well-grounded tokens and use this insight to guide GRIT, which improves both reasoning accuracy and grounding fidelity. Additionally, the authors present GRIT-Bench, a new benchmark for evaluating text-rich reasoning across varying text lengths and reasoning depths.
By Chanseul Cho, Noori Bae, Dongheon Lee, Youngmin Ro
arXiv:2608. 07581v1 Announce Type: cross Abstract: Group-based reinforcement learning methods for multimodal large language models typically rely on trajectory-level credit assignment that applies a single advantage to all tokens in a response.
By Shuai Lyu, Yuning Gong, Ruiling Gao, Xiaoran Shang, Zhonghong Ou, Ping Zong, Yifan Zhu, Yuan Sun, Yang Qin, Peng Hu
arXiv:2609.35942v1 Announce Type: new
Abstract: Recent work in visual question answering has shown that vision-language models can exhibit strong reasoning capabilities by translating visual inputs i...
By Ting-Chih Chen, Emile van Krieken, Shujian Yu, Filip Ilievski
VideoLVR introduces a method for multimodal large language models to maintain visual grounding during chain-of-thought reasoning by constructing latent visual evidence from local video regions at relevant moments. It uses a visual token prediction objective that unifies visual evidence reconstruction and text generation, allowing models to interleave text tokens with latent visual states. The approach is further extended with reinforcement learning, treating visual tokens as actions and optimizing policies with final-answer rewards, thereby improving video understanding without expanding the multimodal context.
By Shilong Li, Xingyuan Bu, Hao Shi, Yun Xing, Weixin Feng, Qiangpeng Yang, Shilei Wen