SPIRAL: Self-Play on Zero-Sum Games Incentivizes Reasoning via Multi-Agent Multi-Turn Reinforcement Learning

Researchers introduce SPIRAL, a self-play reinforcement learning framework that enables language models to develop reasoning capabilities by playing zero-sum games against themselves without human supervision. The system improves performance by up to 10% across 8 reasoning benchmarks on multiple model families including Qwen and Llama.