ReSAIL mitigates collapse in iterative agent self-distillation, +22.5% final-cycle success
RUC · hf · 2026-10-08
RUC's ReSAIL tackles performance collapse in iterative self-distillation of LLM agents, a key path toward recursive self-improvement. It selects interaction steps where privileged information (PI) most changes teacher predictions, balances distillation losses, and regularizes the student's PI-conditioned outputs toward the frozen teacher to preserve supervision for the next cycle. On ALFWorld and TextCraft, it sustains gains over three cycles with an average absolute 22.5% final-cycle success improvement over self-distillation baselines, and also improves multimodal GUI agent action prediction on AITZ.
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