arXiv: Self-Evolving Agents through Privileged Retrospective Distillation (Meta AI)
rohanpaul_ai · x · 2026-10-23
arXiv link post for the same Meta AI paper (2610.27495) by Yuhang Zhou et al.
- RSD turns unlabeled agent trajectories into dense supervision: the same model, given privileged retrospective context (cross-task precedents, turn-level reflection, future hindsight), acts as teacher and re-scores its own realized actions
- Teacher distributions are distilled back on-policy; the experience bank grows and context is refreshed each round so model, data and supervision co-evolve
- A stronger frontier model proposes constrained edits to context construction rules (frontier-guided context-policy evolution), adding up to 44.7% relative improvement over a fixed context policy
- Across ALFWorld, ScienceWorld and SearchQA with Qwen3-8B/14B, RSD improves performance up to 59.7% relative to the base model
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