---
title: "Self-Play Limitations"
created: 2026-04-15
updated: 2026-04-15
type: concept
tags: [rl, self-play]
sources: []
---

# Self-Play Limitations

Self-play limitations refer to the constraints and failure modes of self-play approaches in reinforcement learning and model distillation. While self-play has been highly successful in games (e.g., AlphaGo), its application to LLM post-training and distillation faces challenges including reward hacking, distribution collapse, and the lack of a stable ground-truth objective. The [[on-policy-distillation-survey]] discusses these limitations in the context of modern LLM training.

## Related

- [[on-policy-distillation-survey]] — Surveys limitations of self-play in distillation contexts
- [[on-policy-self-distillation]] — Self-distillation as an alternative to traditional self-play

## References