LLM演化作为行业规模生态系统：持续学习的生命周期视角
LLM Evolution as an Industry-Scale Ecosystem: A Lifecycle Perspective on Continual Learning
AI总结：
从生态系统视角重新定义工业级LLM持续学习为闭环更新与发布问题，识别三大挑战，提出五项生命周期设计原则，并评估其成熟度与部署蓝图。
摘要：
持续学习能力对于工业级LLM至关重要，因为部署的模型必须不断更新以满足不断变化的需求和环境，而不是从头开始重复训练。然而，现有研究大多关注静态基准的改进，未能捕捉真实的工业需求。在本综述中，我们将LLM的工业持续学习（ICL）重新定义为版本化生态系统中的闭环更新与发布问题，其中更新分层传播到工业级、应用特定模型和基于LLM的应用，并在版本和模型家族之间实现能力继承和迁移。从这一生态系统视角，我们识别出三个核心挑战：重复适应削弱模型可塑性、基础模型升级破坏能力继承、长期可持续性受部署需求约束。然后，我们围绕五个生命周期设计原则组织ICL的技术全景：保持可塑性余量、将升级视为能力迁移、实现可信的持续强化学习、使训练配方自我优化、以及将问责制作为长期迭代的基础层。对于每个原则，我们综合了代表性技术方向。最后，我们通过基于证据的视角评估每个原则及其技术组件的成熟度，识别阻碍实际部署的关键差距，并概述了实用的ICL部署蓝图以及将工业现实反馈到学术研究的路径。
英文摘要：
Continual learning capability is critical for Industrial LLMs, as deployed models must be continuously updated to meet evolving requirements and environments, rather than repeatedly retrained from scratch. However, most existing research focuses on improvements on static benchmarks, failing to capture real industrial needs. In this survey, we reformulate Industrial Continual Learning (ICL) for LLMs as a closed-loop update-and-release problem in a versioned ecosystem, where updates propagate hierarchically to industrial, application-specific models and LLM-powered applications, with capability inheritance and transfer across versions and model families. From this ecosystem perspective, we identify three core challenges: repeated adaptation erodes model plasticity, foundation-model upgrades break capability inheritance, and long-term sustainability is constrained by deployment requirements. We then organize the technical landscape of ICL around five lifecycle design principles: preserving plasticity headroom, treating upgrades as capability transfer, enabling trustworthy continual reinforcement learning, making training recipes self-optimizing, and building accountability as a base layer for long-term iteration. For each principle, we synthesize representative technical directions. Finally, we evaluate the maturity of each principle and its technical components via an evidence-based lens, identify key gaps hindering real-world deployment, and outline a practical ICL deployment blueprint and a pathway for feeding industrial realities back into academic research.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
