arXiv:2606.04703v1 Announce Type: cross Abstract: Experience internalization converts contextual experience from past interactions into reusable parametric capability, offering a promising path toward continual learning in large language models (LLMs). While prior work has predominantly focused on single-iteration transfer, we discover that under multi-iteration experience learning, existing methods suffer from a progressive capability collapse rather than compounding improvement. We systematically examine this failure through three vital dimensions of experience internalization: (1) Experienc
The rapid development and deployment of LLMs are pushing the boundaries of autonomous agency, necessitating robust continual learning mechanisms.
This research addresses a critical failure mode in multi-iteration experience learning for LLM agents, which is essential for their self-evolving capabilities and long-term utility.
Understanding the 'progressive capability collapse' allows for the development of more stable and effective continual learning methods for LLMs, enhancing their practical application.
- · AI researchers
- · LLM developers
- · Companies deploying AI agents
- · Companies relying on monolithic, non-adaptive AI systems
Improved continual learning techniques enable more robust and adaptable LLM agents.
Enhanced agent capabilities lead to broader adoption of autonomous AI in various industries, streamlining complex tasks.
The widespread deployment of self-evolving AI agents could fundamentally alter white-collar workflows and the nature of work itself.
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Read at arXiv cs.LG