Retrospective Progress-Aware Self-Refinement for LLM Agent Training
Long-horizon LLM-based agents receive rich environmental observations during interaction, yet outcome rewards provide limited explicit supervision about how individual actions advance task completion. We investigate whether agents can turn this interaction evidence into useful training signals through retrospective progress assessment. A WebShop pilot shows that direct progress prompting reduces task success, whereas hindsight-annotated demonstrations improve it. We introduce RePro, Retrospective Progress-Aware Training, with a forward-then-reflect rollout: the agent estimates progress while acting, then reassesses each step using the completed trajectory and outcome. After warmup with ex...
| Authors | Xinbei Ma, Congmin Zheng, Jiyang Qiu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 12 June 2026 |
| Last updated at source | 5 October 2026 |
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