arXiv:2606.17803v1 Announce Type: new Abstract: Large language models achieve strong reasoning performance by scaling inference-time compute, yet remain fundamentally stateless, discarding the rich, self-produced reasoning traces generated during this process. We investigate whether models can instead learn online from this experience, converting transient computation (reasoning traces) into persistent reusable knowledge, and without external supervision or access to future data. We show that In-Context Learning (ICL) over raw reasoning traces fails to generalize, reflecting a fundamental limi
The increasing scale and computational intensity of large language models are driving a need for more efficient and adaptive learning paradigms.
This research explores a novel method for AI self-improvement, potentially unlocking new pathways for autonomous learning and reducing reliance on continuous external data or re-training.
Models might evolve from stateless inference machines to entities capable of accumulating and reusing internal reasoning, similar to experience in biological systems.
- · AI research and development (academia and industry)
- · Companies developing autonomous AI agents
- · Developers seeking more efficient model updates
- · Companies whose business models rely solely on static, pre-trained models
This research introduces 'lightweight experiential latent memories' allowing LLMs to learn online from their own reasoning traces.
Successful implementation could lead to more robust and less resource-intensive AI systems capable of continuous adaptation in dynamic environments.
These improvements in AI autonomy and learning could accelerate the development and deployment of truly intelligent AI agents across various industries.
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Read at arXiv cs.LG