ReGE: turning recorded agent execution experience into synthesized guidance for self-improvement
TIGER-Lab · hf · 2026-10-28
TIGER-Lab released ReGE, a method that converts recorded agent execution experience into synthesized guidance to improve subsequent task attempts.
Key claim: it requires no stronger teacher model and no parameter updates — agents improve purely by leveraging their own execution history, a lightweight take on agent experience reuse and self-improvement.
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