# К самообучению LLM в условиях дефицита данных: Эффективность, Обобщающая способность и Надежность

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**Published:** 9 ноября 2026 г.
**Author:** Zhiyin Yu, Yuchen Mou, Bingzhe Wu, Zhonghai Wu, Shiyang Feng, Xiao Luo
**Source:** arXiv — cs.AI
**Language:** en-US
**HTML:** https://aiqazaqstan.com/ru/articles/toward-llm-self-play-under-data-scarcity-effectiveness-generalizability

arXiv:2611.08908v1 Тип объявления: новый Абстракт: Быстрое развитие больших языковых моделей (LLM) во многом было обусловлено увеличением размера модели и человечески отобранными обучающими данными, однако эта парадигма все больше ограничивается дефицитом данных и убывающей отдачей от человеческого…

arXiv:2611.08908v1 Announce Type: new Abstract: 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.

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*Материал агрегирован из первоисточника: [arXiv — cs.AI](https://arxiv.org/abs/2611.08908). Открыть оригинал.*

**Теги:** #arXiv

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