AI

Self-Retrospection Distillation: Turning Post-hoc Experiences into Prior Foresight

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Briefings

Self-Retrospection Distillation (SRD) uses post-hoc experience to train agents to predict what they could have anticipated before acting. This method improves performance by up to 24.2 percentage points across 10 tool-integrated tasks, with gains especially pronounced when reward contrast is scarce. In the 2B setting, SRD boosted success rates from 0.0% to 60.6% despite 98% of rollout groups failing. The approach demonstrates that post-hoc experience shapes predictive representations before interaction.

Key points

• Self-Retrospection Distillation (SRD) distills post-hoc trajectory knowledge into trajectory-blind foresight for training.
• SRD achieves up to 24.2 percentage point gains across 10 tool-integrated reasoning and long-horizon agentic tasks.

Context

This research addresses the limitation of reinforcement learning with verifiable rewards (RLVR) where group-relative objectives vanish when all rollouts receive identical rewards. SRD introduces prospective learning to use post-hoc experience for supervising foresight predictions from a pre-interaction view.

Limitations

Results are limited to 10 tool-integrated reasoning and long-horizon agentic tasks. Performance gains are observed only when reward contrast is scarce, such as in the 2B setting where 98% of groups failed. The 2B setting shows 0.0% success without SRD, highlighting the method's necessity in high-failure scenarios.

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