# Self-Refine vs Reflexion

> Self-Refine improves a single output within one task, with the model critiquing and revising its own draft until a stopping point. Reflexion works across attempts, having an agent write a reflection on why an attempt failed, often using signals such as test results, and store it in memory to guide the next try.

- Canonical URL: https://protologue.com/compare/self-refine-vs-reflexion/

## Self-Refine

Self-Refine is an iterative method in which the same model generates an output, critiques it with specific feedback, and revises it, repeating until a stopping condition is met.
Entry: https://protologue.com/t/self-refine/

## Reflexion

Reflexion is an agent technique in which, after a failed attempt, the model writes a verbal reflection on what went wrong and stores it in memory to guide its next attempt.
Entry: https://protologue.com/t/reflexion/

## Differences

| | Self-Refine | Reflexion |
|---|---|---|
| Loop | Generate, critique and revise | Attempt, get feedback, reflect and retry |
| Feedback source | The model's own critique | External signals such as unit tests or rewards |
| Memory | None beyond the draft | Reflections stored and reused |

## Which to use

- Use Self-Refine to polish writing or code within one response.
- Use Reflexion for agents that can attempt a task repeatedly and observe whether they succeeded.

## Sources

- Madaan et al. (2023). Self-Refine: Iterative Refinement with Self-Feedback. https://arxiv.org/abs/2303.17651
- Huang et al. (2023). Large Language Models Cannot Self-Correct Reasoning Yet. https://arxiv.org/abs/2310.01798
- Shinn et al. (2023). Reflexion: Language Agents with Verbal Reinforcement Learning. https://arxiv.org/abs/2303.11366

## Cite this page

Protologue. (2026). Self-Refine vs Reflexion. In Protologue: A Taxonomy of Prompting and LLM Techniques (v1.4.0). https://protologue.com/compare/self-refine-vs-reflexion/

License: CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/)