Reflection / Self-Correction

How it is done

A typical flow: the model produces a draft → the model (or a separate critic model) checks it point by point against the requirements → the critique plus the draft is fed back to produce a revision, repeatable for several rounds. Reflexion additionally stores the "lesson" from each failure as text, used as extra context for the next attempt.

When it actually works

Self-correction is most reliable when there is an external signal: runnable tests, compiler errors, retrieved facts, an explicit rubric. With no external reference — the model just "thinking again" — research (Huang et al. 2023) finds it often fails to fix errors and can change correct answers to wrong ones. So in practice reflection is usually tied to real feedback (tool results, tests).

Common misconceptions

• "Have the model check its own work" is not reliable correction — with no external signal the benefit is limited.
• More reflection rounds is not always better; it can loop on rewrites without converging.

Related terms

Sources

1. Shinn et al. — Reflexion: Language Agents with Verbal Reinforcement Learning (arXiv:2303.11366)
2. Madaan et al. — Self-Refine: Iterative Refinement with Self-Feedback (arXiv:2303.17651)
3. Huang et al. — Large Language Models Cannot Self-Correct Reasoning Yet (arXiv:2310.01798)