arXiv:2602.06470v3 Announce Type: replace-cross Abstract: Scaling training data and model parameters has long driven progress in large language models (LLMs), but this paradigm is increasingly constrained by the scarcity of high-quality data and diminishing returns from rising computational costs. As a result, recent work is increasing the focus on continual learning from real-world deployment, where user interaction logs provide a rich source of authentic human feedback and procedural knowledge. However, learning from user logs is challenging due to their unstructured and noisy nature. Vanill
The increasing constraints on traditional LLM scaling, such as data scarcity and computational costs, are forcing a pivot towards alternative learning paradigms.
Learning from user logs represents a crucial avenue for continued LLM improvement, enabling models to adapt and learn dynamically in real-world environments.
The focus for LLM development shifts from purely pre-training on static datasets to emphasizing continual learning and adaptation from authentic, real-time user interactions.
- · AI agents developers
- · Companies with proprietary user data
- · AI ethics and safety researchers
- · Continual learning research
- · LLM developers solely focused on pre-training scaling
- · Entities without real-world deployment data
Increased performance and contextual understanding of deployed large language models.
Accelerated development of more adaptive and autonomous AI agents capable of complex tasks.
Ethical and privacy frameworks becoming more central to AI development, as user interaction data gains criticality for model evolution.
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Read at arXiv cs.AI