agent-r1-end-to-end-rl

Train LLM-based agents with end-to-end RL by extending MDPs to handle tool invocation and environmental stochasticity—enable dense process rewards for intermediate steps and masked policy gradients for learnable actions.

Independent GitHub snapshot · Automated classification · Original documentation governs