Stanford Researchers Launched AgentFlow: In-the-Move Reinforcement Studying RL for Modular, Device-Utilizing AI Brokers
TL;DR: AgentFlow is a trainable agent framework with 4 modules—Planner, Executor, Verifier, Generator—coordinated by an express reminiscence and toolset. The planner is optimized within the loop with a brand new on-policy methodology, Move-GRPO, which broadcasts a trajectory-level consequence reward to each flip and applies token-level PPO-style updates with KL regularization and group-normalized benefits. On ten … Read more