让大模型上线后持续学习，自动积累经验并优化知识。
Experience Scaling: Post-Deployment Evolution For Large Language Models
- 通过自主交互收集经验，提炼为可复用的知识
- 在新任务中准确率提升，长期性能更稳定
- 适合需要持续进化的智能系统开发者
模型规模、训练数据和算力的扩展曾推动大语言模型（LLMs）的发展，但随着人工文本资源枯竭，增长已趋饱和。本文提出“经验扩展”框架，支持大模型在部署后通过与环境自主互动，并协作共享积累的经验实现持续进化。该框架捕获原始交互，将其提炼为紧凑可复用的知识，并定期优化存储内容以保持相关性与效率。我们在模拟真实场景中验证了该框架，涵盖对未见过但相关的任务泛化、重复查询及知识库过载等情况。结果表明，在所有测试场景中，经验扩展均提升了准确性，维持了长期性能，并在新情境下保持改进效果。这证明结构化的部署后学习能突破静态人工数据的限制，为智能系统的持续发展提供可扩展路径。
原文摘要 · Abstract (English)
Scaling model size, training data, and compute power have driven advances in large language models (LLMs), but these approaches are reaching saturation as human-generated text is exhausted and further gains diminish. We propose experience scaling, a framework for continuous post-deployment evolution for LLMs through autonomous interaction with the environment and collaborative sharing of accumulated experience. The framework captures raw interactions, distills them into compact, reusable knowledge, and periodically refines stored content to preserve relevance and efficiency. We validate the framework in simulated real-world scenarios involving generalization to previously unseen but related tasks, repetitive queries, and over-saturated knowledge stores. Across all settings, experience scaling improves accuracy, sustains performance over time, and maintains gains when applied to novel situations. These results demonstrate that structured post-deployment learning can extend LLM capabilities beyond the limits of static human-generated data, offering a scalable path for continued intelligence progress.
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