LLM Evolution as an Industry-Scale Ecosystem: A Lifecycle Perspective on Continual Learning
This survey reformulates industrial continual learning for LLMs as a closed-loop update-and-release problem in a versioned ecosystem. It identifies three core challenges (plasticity erosion, capability inheritance breakage, sustainability constraints) and proposes five lifecycle design principles. The paper evaluates maturity of each principle and outlines a deployment blueprint.
[2606.24901] LLM Evolution as an Industry-Scale Ecosystem: A Lifecycle Perspective on Continual Learning
[Submitted on 12 Jun 2026]
Title:LLM Evolution as an Industry-Scale Ecosystem: A Lifecycle Perspective on Continual Learning
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Abstract: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.
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.24901 [cs.LG]
(or arXiv:2606.24901v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.24901
arXiv-issued DOI via DataCite
Submission history
From: Enneng Yang [view email] [v1] Fri, 12 Jun 2026 13:44:48 UTC (11,666 KB)
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