Language Self-Play For Data-Free Training

2025-09-09

AI Summary

The continual advancement of large language models (LLMs) is bottlenecked by the scarcity of high-quality training data. Method: This paper introduces Language Self-Play—a novel framework that integrates game-theoretic principles with reinforcement learning to model language capability as a self-antagonistic strategy evolution process, enabling autonomous model improvement without new external data. Departing from reliance on large-scale annotated datasets, it employs Llama-3.2-3B-Instruct as the base model and conducts iterative self-play training on instruction-following tasks. Contribution/Results: Through a closed-loop optimization cycle—comprising self-generated instruction refinement and automated evaluation—the method achieves significant performance gains on complex instruction-following benchmarks, outperforming data-driven baselines of comparable scale. Empirical results validate the feasibility and efficacy of sustained LLM capability evolution in a truly data-free paradigm.

Technology Category

• Natural Language Processing: (Large) Language Models
• Machine Learning: Large Multimodal Models (LMMs)
• Search and Optimization: Learning to Search

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

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.

Problem

• Overcoming data dependency bottleneck in LLM training
• Enabling model improvement without additional external data
• Enhancing performance through competitive self-play framework

Innovation

• Reinforcement learning without additional data dependency
• Game-theoretic self-play framework for model improvement
• Language Self-Play enabling competitive policy enhancement