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

Authors: Haoxiang Zhang, Qinglin Chen, Hiroaki Hayashi, Zhuofeng Li, Siming Zhang, Jiaxin Zhang, Jixuan Chen, Fang Wu, Pan Lu, Silvio Savarese, Julian McAuley, Chien-Sheng Wu (Salesforce AI Research)

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Abstract

Reinforcement learning with verifiable rewards (RLVR) turns agent experience into learning signals primarily through scalar outcome rewards after interaction. For group-relative objectives, however, this signal vanishes when all rollouts receive the same reward, even though their trajectories may reveal useful information about what the task requires and how the agent fails. We ask a complementary question: can hindsight teach an agent what it could have anticipated before acting? We introduce prospective learning, which uses post-hoc experience to supervise foresight predictions from the pre-interaction view, and instantiate it with Self-Retrospection Distillation (SRD). Intuitively, a completed trajectory reveals knowledge that would have been useful and pitfalls that should be avoided; SRD distills this privileged hindsight into trajectory-blind foresight of the same policy. Foresight serves only as a training target and need not be explicitly generated at inference time. Across 10 tool-integrated reasoning and long-horizon agentic tasks, SRD complements RLVR and self-distillation baselines with gains of up to 24.2 pp. Its advantage is especially pronounced when reward contrast is scarce: when 37–98% of rollout groups are reward-uniform across model scales, yet SRD can still exploit learning signal from sampled trajectories. In 2B setting, where 98% of groups are all-failure, the RLVR training ends up at 0.0% success, while adding SRD reaches 60.6% under the same rollout budget. Our results suggest that post-hoc agent experience is useful not only for evaluating or improving behavior, but also for shaping predictive representations before available interaction. Code

1 Introduction

Reinforcement learning with verifiable rewards (RLVR) has emerged as a scalable post-training paradigm for large language model (LLM) agents: by executing against automated verifiers and optimizing policies from outcome signals, it enables agents to develop complex, multi-step behavior without human-annotated traces (Shao et al., 2024; Guo et al., 2025; Yu et al., 2025). Applied to tool-integrated reasoning (Jin et al., 2025a; Xue et al., 2026; Qian et al., 2025), code execution (Le et al., 2022; Cao et al., 2026), computer use (Wang et al., 2025a), and multi-agent coordination (Li et al., 2026; Ke et al., 2026), RLVR has proven remarkably effective in settings where agents must act in an external environment and learn from feedback revealed only through interaction.

The supervision in RLVR, however, is inherently retrospective: outcome signals become available only after interaction and are then assigned back to the actions that produced them. Yet a broad line of work on predictive control, world models, and planning suggests that effective interaction also depends on what an agent can anticipate before acting, how its interaction may fail, and what the environment is likely to demand (Pathak et al., 2017; Hafner et al., 2020; Zhou et al., 2023). Such pre-interaction expectations shape subsequent exploration, but in RLVR they remain an implicit byproduct of optimizing behavior. More importantly, relying exclusively on post-hoc outcome contrast creates a fundamental blind spot: when all rollouts in a group receive the same reward, especially in long-horizon reasoning tasks where most attempts fail (Lightman et al., 2024), their group-relative advantages vanish (Schulman et al., 2017). The agent may have already revealed rich interactive information about the task and environment, yet learn no signal from that experience.

A recent line of on-policy self-distillation extracts a denser signal from this experience: a single model serves as both teacher and student, with the teacher conditioned on a privileged correct solution and the student trained to match its token-level distribution over the student’s own rollouts (Zhao et al., 2026; Hübotter et al., 2026). Follow-ups vary what the privileged context contains — runtime feedback, skill summaries, or self-revisions — and hybrid objectives fold this signal back into RLVR (Wang et al., 2026; Yang et al., 2026; He et al., 2026). Across these variants, however, the supervision target is unchanged: hindsight conditions the teacher to sharpen what the agent should do next. We ask instead what the agent could have known — whether the same completed experience can teach it to anticipate, before acting, the demands and failures that only interaction reveals.

We call this prospective learning: hindsight supervises the agent’s foresight. Given a task, the agent predicts from pre-interaction information alone the knowledge the task may require and the failures it may invite, and the completed trajectory then provides privileged supervision for those predictions.

Building on this principle, we propose Self-Retrospection Distillation (SRD). For each on-policy trajectory, SRD pairs a pre-interaction prediction with a hindsight prediction conditioned on the completed interaction, and aligns their token-level distributions on the student’s own prospective rollout. As a result, every trajectory can contribute prospective supervision, precisely where reward contrast leaves none: on a failed rollout, the self-teacher’s hindsight lets the agent distill the mistake it just made into an anticipation it can carry forward, and on a saturated all-correct group, the same pairing keeps extracting the knowledge that made those rollouts succeed. Reward thus decides which lesson a trajectory teaches rather than whether it teaches one; learning then continues at both ends of the difficulty range that group-relative methods discard. We make three contributions:

• A pre-interaction supervision target for agent experience. We formulate prospective learning (§3.1), a post-training target that distills structured information revealed after interaction into predictions constrained to the task and environment context available beforehand, complementary to the retrospective use of experience in RLVR and self-distillation.
• A composable self-distillation instantiation. Self-Retrospection Distillation (SRD, §3.2) adds a lightweight auxiliary objective to RLVR and self-distillation algorithms, and improves performance across diverse tool-integrated reasoning and long-horizon agentic settings (§4.2); our analysis characterizes the regimes in which this additional signal is most useful (§5).
• Learning beyond scalar reward variance. SRD provides dense supervision from all trajectories and remains informative for reward-uniform baselines where RL yields zero advantage (§5).

2 Preliminaries

We consider an agent policy πθ interacting with an environment \mathcal{E}. Given a task x\sim\mathcal{D}, the agent produces a trajectory τ=(a1,o1,…,aT,oT), where at∼πθ(⋅∣ht) is the action at step t, o_{t}\sim\mathcal{E}(\cdot\mid h_{t},a_{t}) the resulting observation, and ht the previous interaction history.

Reinforcement Learning with Verifiable Rewards (RLVR)

evaluates a completed trajectory with a verifier \mathcal{V} (e.g. a math checker, unit test, or task rubric), producing a scalar reward r(\tau)=\mathcal{V}(x,\tau)