AI RESEARCH
Beyond Where to Look: Trajectory-Guided Reinforcement Learning for Multimodal RLVR
arXiv CS.CV
•
ArXi:2603.26126v1 Announce Type: new Recent advances in Reinforcement Learning with Verifiable Rewards (RLVR) for multimodal large language models (MLLMs) have mainly focused on improving final answer correctness and strengthening visual grounding. However, a critical bottleneck remains: although models can attend to relevant visual regions, they often fail to effectively incorporate visual evidence into subsequent reasoning, leading to reasoning chains that are weakly grounded in visual facts.