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.

Incompleteness of domain ontology and unavailability of some values are two inevitable problems of dialogue state tracking (DST). Existing approaches generally fall into two extremes: choosing models without ontology or embedding ontology in models leading to over-dependence. In this paper, we propose a new architecture to cleverly exploit ontology, which consists of Slot Attention (SA) and Value Normalization (VN), referred to as SAVN. Moreover, we supplement the annotation of supporting span for MultiWOZ 2.1, which is the shortest span in utterances to support the labeled value. SA shares knowledge between slots and utterances and only needs a simple structure to predict the supporting span. VN is designed specifically for the use of ontology, which can convert supporting spans to the values. Empirical results demonstrate that SAVN achieves the state-of-the-art joint accuracy of 54.52% on MultiWOZ 2.0 and 54.86% on MultiWOZ 2.1. Besides, we evaluate VN with incomplete ontology. The results show that even if only 30% ontology is used, VN can also contribute to our model.