This post is the second in a series exploring reflection techniques in AI systems. For the complete series, see our posts on Reflexion and stay tuned for more.

Comparing Self-Refine and Reflexion: Two Paths to AI Self-Improvement

Large AI models can improve their own outputs by reflecting on mistakes or giving themselves feedback. Here we compare two recent methods – Self-Refine and Reflexion – which let AI models self-correct in different ways. We’ll explain each approach in simple terms, then highlight how they differ in strategy and goals.

Self-Refine: AI Editing Its Own Work

Self-Refine is an approach that lets an AI model iteratively improve its answer or content by acting as its own editor. The idea is inspired by how people write a draft and then revise it to make it better1. Often an AI’s first answer isn’t its best; with Self-Refine, the same AI goes back to review and polish its response without any human intervention.

The process works in a few simple stages:

  1. The AI produces an initial answer.
  2. It critiques its own output – pointing out flaws or areas to improve.
  3. It refines the answer based on that feedback.
  4. Optionally, the cycle repeats to further improve the result.

The goal of Self-Refine is to polish the AI’s answer for quality, clarity, or correctness. In studies, this method led to answers that people preferred over the one-shot replies from the same AI without self-refinement.

Reflexion: AI Learning from Mistakes

Reflexion, by contrast, helps an AI learn from its errors over multiple attempts. The idea is closer to how humans improve at tasks: try something, reflect on what went wrong, remember that lesson, and try again. Reflexion gives an AI that ability – to reflect after a failed attempt, write down a lesson, and consult that memory in future attempts2.

The Reflexion process looks like this:

  1. The AI tries a task.
  2. If it fails, it receives feedback (like an error message).
  3. It reflects on the failure by generating a short note to itself.
  4. It stores that reflection in memory.
  5. On the next try, it uses that reflection to guide its new attempt.

Over time, this makes the AI more effective at problem-solving, because it’s no longer repeating the same mistakes.

Key Differences

Feature Self-Refine Reflexion
Goal Improve a single output Improve performance over multiple tries
Feedback Source Self-generated critique Feedback from the environment or task
Memory Use No memory between sessions Stores reflections for future guidance
Use Case Answer polishing, writing, Q&A Problem-solving, coding, game-playing
Trigger Always applies Applies after failure or feedback

In Summary

While both Self-Refine and Reflexion use feedback loops to make AI smarter, they tackle different challenges:

  • Self-Refine is about making a single response better by having the AI review and revise its own answer.
  • Reflexion is about helping an AI learn from failure over time – remembering what didn’t work and trying a better strategy next time.

They both push AI toward something more thoughtful, less brittle, and better aligned with how humans actually work.

References

  1. Madaan, A., et al. (2023). Self-Refine: Iterative Refinement with Self-Feedback. arXiv:2303.17651 [cs.CL]. ↩

  2. Shinn, N., et al. (2023). Reflexion: Language Agents with Verbal Reinforcement Learning. arXiv:2303.11366 [cs.CL]. ↩