New arXiv Survey Redefines LLM Continual Learning as an Industry Ecosystem

A new arXiv survey from Hao Jiang and nine co-authors reframes how large language models (LLMs) should be updated in production environments. Instead of focusing on static benchmarks, the paper proposes an Industrial Continual Learning (ICL) ecosystem where model updates follow a closed-loop, versioned release cycle. This lifecycle perspective treats updates as hierarchical propagations—from foundation models down to application-specific models and LLM-powered apps—emphasizing capability inheritance and transfer across versions. The authors identify three core obstacles: repeated fine-tuning erodes model plasticity, foundation-model upgrades can break inherited capabilities, and deployment constraints limit long-term sustainability.

The survey organizes technical solutions around five lifecycle design principles: (1) preserving plasticity headroom to avoid catastrophic forgetting, (2) treating model upgrades as capability transfer rather than retraining, (3) enabling trustworthy continual reinforcement learning (RL) with human feedback, (4) making training recipes self-optimizing to reduce manual tuning, and (5) building accountability as a base layer for iterative improvement. Each principle is evaluated by maturity using evidence from existing research, revealing gaps that hinder real-world adoption. The paper culminates in a practical ICL deployment blueprint and a call to feed industrial realities back into academia—offering a structured path for teams that need to continuously evolve their LLMs without restarting from scratch every time.