AI RESEARCH
Vision-EKIPL: External Knowledge-Infused Policy Learning for Visual Reasoning
arXiv CS.CV
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ArXi:2506.06856v3 Announce Type: replace Visual reasoning is crucial for understanding complex multimodal data and advancing Artificial General Intelligence. Existing methods enhance the reasoning capability of Multimodal Large Language Models (MLLMs) through Reinforcement Learning (RL) fine-tuning (e.g., GRPO). However, current RL approaches sample action groups solely from the policy model itself, which limits the upper boundary of the model's reasoning capability and leads to inefficient