Continual Learning for LLM Agents

Continual learning for large language model agents asks how they can improve from ongoing interaction, feedback, and changing environments rather than starting each task from scratch. Common approaches store and retrieve episodes, rules, or reusable skills; refine prompts and agent harnesses; or use reinforcement learning and fine-tuning to consolidate experience into model parameters. Work evaluates these methods in long-horizon settings such as web tasks, coding, games, and embodied environments, where agents must transfer what they learn to later tasks. A central tension is balancing adaptation with retention: accumulated memories and updates can cause interference, stale guidance, or forgetting, and gains in one setting do not always transfer to another.