TRIAGE

TRIAGE: Role-typed credit assignment framework improves reinforcement learning for agentic tasks

TRIAGE is a new credit assignment framework for agentic reinforcement learning that augments standard GRPO by classifying action segments into semantic roles (decisive progress, useful exploration, no-progress infrastructure, regression) and applying role-conditioned process rewards. The approach addresses two structural blind spots of outcome-only credit: punishing useful exploration in failed rollouts and reinforcing redundant actions in successful ones. Evaluated on ALFWorld, Search-QA, and WebShop, TRIAGE improves success rates over GRPO and reduces environment-facing turns by 10-15%, with regression detection inside successful trajectories identified as the dominant contributor.