Self-Retrospection Distillation Improves Agent Foresight

Self-Retrospection Distillation turns post-hoc agent trajectories into foresight, boosting RLVR by up to 24.2 points when rewards lack contrast.

Self-Retrospection Distillation (SRD) turns post-hoc agent trajectories into foresight predictions from the pre-interaction view using completed trajectories to supervise training. Foresight serves solely as a training target and is not required at inference. SRD distills hindsight from finished trajectories into trajectory-blind foresight while complementing reinforcement learning with verifiable rewards (RLVR). Across 10 tool-integrated reasoning and long-horizon agentic tasks, SRD adds up to 24.2 percentage points over RLVR and self-distillation baselines. When 37–98% of rollout groups share the same reward, SRD extracts signal; in a 2B setting where 98% of groups were all failures, RLVR stayed at 0.0% success while SRD reached 60.6% under the same rollout budget. Reports from Hugging Face daily papers and arXiv contain identical claims with no discrepancies.

• SRD improves RLVR baselines by up to 24.2 percentage points across 10 tool-integrated reasoning and long-horizon agentic tasks
• In a 2B setting with 98% all-failure rollouts, SRD raises success from 0.0% to 60.6% under same rollout budget
• Foresight is a training target only and need not be generated at inference
• Distills hindsight from finished trajectories into trajectory-blind foresight
• Still extracts signal when 37–98% of rollout groups share uniform rewards
• First reported in Hugging Face daily papers 2026-10-05
• Confirmed in arXiv cs.AI/cs.LG/cs.CL 2026-10-06
• Complements RLVR with verifiable rewards