Self-Credit for Dense Supervision in Long-Horizon Agentic RL

Self-Credit uses intra-group rescaling to generate dense supervision signals from sparse outcome rewards in RL training. The method utilizes policy-generated summaries with citations to critical evidence to assign credit across long-horizon trajectories.