Distill Until Divergence: Error-Truncated Hindsight Distillation for Agentic RL
Long-horizon interaction requires agents to make multiple decisions in a changing environment. Outcome-based GRPO lacks fine-grained guidance for intermediate decisions. Recent methods distill completed trajectories into reusable skills and use hindsight-conditioned OPD to provide dense token-level credit. However, skill-induced probability shifts do not necessarily represent reward-aligned credit; especially after an erroneous action changes subsequent interaction states, skill-conditioned supervision may become mismatched with the current state. To address this mismatch, we propose \methodname{}, which routes two complementary hindsight skills according to trajectory outcomes and error ...
| Authors | Yibin Huang, Conghui Zhu |
|---|---|
| Primary category | cs.AI |
| Published | 16 October 2026 |
| Last updated at source | 16 October 2026 |
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