LLM Evolution as an Industry-Scale Ecosystem: A Lifecycle Perspective on Continual Learning
For practitioners deploying LLMs in industry, this work provides a structured framework and practical guidance for managing continual learning in production systems, addressing gaps between academic benchmarks and real-world requirements.
This survey reformulates continual learning for industrial LLMs as a closed-loop update-and-release problem in a versioned ecosystem, identifying core challenges and organizing technical directions around five lifecycle design principles. It evaluates the maturity of these principles and outlines a deployment blueprint to bridge industrial needs and academic research.
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.