HINT-SD Uses Trajectory-Level Hindsight to Target Failure-Relevant Actions in LLM Agents

HINT-SD selects only failure-relevant actions from full trajectories for self-distillation feedback, avoiding the inefficiency of per-turn feedback generation and the misalignment of fixed-turn supervision in long-horizon RL agent training.