LLM 自我博弈综述：数据稀缺下的有效性、泛化性与可信性
Toward LLM Self-Play Under Data Scarcity: Effectiveness, Generalizability, and Trustworthiness
这篇论文把自我博弈这个热门方向拆成三个研究路线来讲，关心 LLM 训练方法的人可以看看它列的问题清单。
arXiv 论文（编号 2611.08908）讨论 LLM 自我博弈在数据稀缺场景下的应用。论文指出现有自我博弈框架缺乏数据中心视角、跨域泛化能力有限、存在可信性与安全隐患三个问题。作者提出三个研究方向：数据中心自我博弈、面向开放世界和不可验证任务的可泛化自我博弈、可信自我博弈。论文目标是把有限的人类知识转化为持续生成的高质量经验数据。
Toward LLM Self-Play Under Data Scarcity: Effectiveness, Generalizability, and Trustworthiness
The rapid development of large language models (LLMs) has been driven largely by scaling model size and human-curated training data, yet this paradigm is increasingly constrained by data scarcity and diminishing returns from human supervision. Self-play offers a promising pathway for autonomous data generation and improvement, but current frameworks remain unsatisfactory due to a lack of a data-centric perspective, limited cross-domain generalization, and growing trustworthiness and security concerns. In this paper, we argue that real-world LLM self-play systems should achieve effectiveness, generalizability, and trustworthiness under data scarcity. To address these challenges, we outline three blue-sky research directions for future self-play systems: (1) data-centric self-play for effective experience generation, (2) generalizable self-play for open-world and unverifiable tasks, and (3) trustworthy self-play for reliable self-evolution. We further discuss research opportunities and solutions within each direction. This paradigm shift will establish a sustainable and safe AI ecosystem by transforming finite human knowledge into inexhaustible high-quality experience through self-play architectures.