Prospective Hindsight: Self-Calibrating Reinforcement Learning via Prediction–Reality Gaps

Reinforcement learning for long-horizon agents relies on \emph{purely retrospective} training signals: credit is assigned only after observing environmental consequences, leaving the agent's belief at action time invisible to the gradient. We introduce \textbf{Prospective Hindsight (PH)}, a self-calibrating training principle that augments any retrospective base method with a signal derived from the gap between the agent's \emph{prospective prediction} (before feedback) and the \emph{retrospective evaluation} (after feedback). This per-rollout \emph{surprise} identifies samples where the agent's self-model is most inaccurate and amplifies their gradient contribution through a stop-gradient surprise-weighted advantage. Since the prospective predictor shares parameters with the policy, the two co-evolve, progressively shifting focus to the agent's remaining blind spots. We connect this principle to a privileged-information gap and show that minimizing the surprise residual provides a descent pathway on the agent's miscalibration rate; calibration thus emerges as a byproduct of optimization rather than from an added objective. On single-turn verifiable tasks and a multi-turn personal-agent task (under GRPO, on-policy distillation, and their combination), PH improves both task performance and calibration, with consistent gains across model scales. Notably, the dominant miscalibration mode shifts structurally between regimes, overconfident failures in single-turn, underconfident successes in multi-turn, yet the same training principle addresses both successfully.