Argos: Multimodal reinforcement learning with agentic verifier for AI agents
Reuben Tan, Baolin Peng, Zhengyuan Yang, Oier Mees, Jianfeng Gao
Argos improves multimodal RL by evaluating whether an agent’s reasoning aligns with what it observes over time. The approach reduces visual hallucinations and produces more reliable, data-efficient agents for real-world applications. The post Argos: Multimodal reinforcement learning with agentic verifier for AI agents appeared first on Microsoft Research .