Language Self-Play For Data-Free Training

Kuba Grudzien, Mengting Gu, Qi Ma, Yuandong Tian, Vijai Mohan

Language Self-Play For Data-Free Training: 32 upvotes on Hugging Face Daily Papers, #8 of 18 papers on 2025-09-10. Day-by-day upvote history.

Large language models (LLMs) have advanced rapidly in recent years, driven by scale, abundant high-quality training data, and reinforcement learning. Yet this progress faces a fundamental bottleneck: the need for ever more data from which models can continue to learn. In this work, we propose a reinforcement learning approach that removes this dependency by enabling models to improve without additional data. Our method leverages a game-theoretic framework of self-play, where a model's capabilities are cast as performance in a competitive game and stronger policies emerge by having the model play against itself - a process we call Language Self-Play (LSP). Experiments with Llama-3.2-3B-Instruct on instruction-following benchmarks show that pretrained models can not only enhance their performance on challenging tasks through self-play alone, but can also do so more effectively than data-driven baselines.

Paper page on Hugging Face · arXiv

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