Reinforcement Learning (RL) has demonstrated significant potential in enhancing the capabilities of large language models (LLMs). However, the success of RL for LLMs heavily relies on human-curated datasets, which pose a burden on scalability and sustainability. Inspired by the success of the paradigm in games and Go, Self-Play methods are deployed to enhance LLM capabilities without human-annotated data. However, these methods primarily depend on a grounded environment for verifiable feedback (e.g., a Python interpreter or a game engine); extending them to general domains remains challenging. To address these challenges, we propose **Multi-Agent Evolve (MAE)**, a framework in which LLM can self-evolve across diverse tasks from mathematics to general knowledge. MAE implements asymmetric Self-Play by leveraging a triplet of interacting roles (*Proposer, Solver, Judge*) that are instantiated from a single LLM. *Proposer* generates challenging questions, *Solver* optimizes solutions, and *Judge* provides reward signals. These roles are jointly optimized via reinforcement learning, creating a closed-loop system where the model continuously challenges and improves itself. Experiments on Qwen2.5-3B-Instruct demonstrate that MAE achieves an average improvement of 4.86% across multiple benchmarks, significantly outperforming baselines. These results highlight MAE as a scalable, data-efficient method for enhancing LLMs’ general reasoning abilities with minimal reliance on human supervision.