Language Self-Play For Data-Free Training
Authors: name: Jakub Grudzien Kuba, name: Mengting Gu, name: Qi Ma, name: Yuandong Tian, name: Vijai Mohan

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.