Graph-Based Credit Assignment for Agentic RL

A novel reinforcement learning technique known as Graph-based Group Policy Optimization (GraphGPO) tackles the challenge of credit assignment in agentic tasks by creating a comprehensive state-transition graph derived from rollout trajectories. In contrast to conventional group-based RL, which depends on broad trajectory-level attribution linked to final results, GraphGPO evaluates the distance from each state to the task's goal utilizing global graph data. It allocates credit to each edge according to how significantly the transition decreases that distance, facilitating a more precise step-level credit assignment. This method reveals hidden insights from unsuccessful trajectories, where crucial steps might be overlooked. Tailored for agentic reinforcement learning and large language models, it enhances current group-based policy optimization strategies. The research can be found on arXiv with the identifier 2605.26684.