AI RESEARCH
SophiaVL-R1: Reinforcing MLLMs Reasoning with Thinking Reward
arXiv CS.CV
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ArXi:2505.17018v2 Announce Type: replace Recent advances have shown success in eliciting strong reasoning abilities in multimodal large language models (MLLMs) through rule-based reinforcement learning (RL) with outcome rewards. However, this paradigm typically lacks supervision over the thinking process leading to the final outcome. As a result, the model may learn sub-optimal reasoning strategies, which can hinder its generalization ability. In light of this, we propose SophiaVL-R1, as an attempt to add reward signals for the thinking process in this paradigm.