Multimodal Reinforcement Learning with Adaptive Verifier for AI Agents

ArXi:2512.03438v2 Announce Type: replace Agentic reasoning models trained with multimodal reinforcement learning (MMRL) have become increasingly capable, yet they are almost universally optimized using sparse, outcome-based rewards computed based on the final answers. Richer rewards computed from the reasoning tokens can improve learning significantly by providing fine-grained guidance.