When training agents that interact with environments through discrete actions, assigning credit based on semantic role categories (not just final outcomes) reduces variance and improves learning by properly rewarding exploration and penalizing waste.
TRIAGE improves credit assignment in agentic RL by classifying action segments into semantic roles (progress, exploration, infrastructure, regression) and assigning role-specific rewards.