arXiv:2606.25010v1 Announce Type: new Abstract: Neural scaling laws for transformer language models predict smooth improvements in pretraining loss with increasing parameters, but downstream capabilities such as in-context learning are known to emerge abruptly past a certain model scale. In this paper, we show that emergent capabilities arise stochastically throughout training, with larger models acquiring them earlier on average. We demonstrate that the emergence of capabilities such as pattern completion and indirect object identification corresponds to the abrupt learning of task-relevant a
The paper provides a new theoretical understanding of scaling laws and emergent capabilities in AI, building on recent empirical observations regarding large language models.
Understanding the stochastic and abrupt emergence of AI capabilities could fundamentally alter how AI models are designed, trained, and evaluated, leading to more efficient and predictable development.
The focus might shift from simply scaling up models to actively engineering for task-relevant sparse attention patterns, potentially democratizing access to powerful AI models by reducing the necessity for extreme scale in all cases.
- · AI researchers
- · AI model developers
- · Hardware accelerators for sparse models
- · AI development relying solely on brute-force scaling
- · Inefficient AI training methodologies
More sophisticated and targeted AI training techniques will be developed based on the understanding of sparse attention patterns.
This could lead to breakthroughs in achieving advanced AI capabilities with fewer computational resources or smaller model sizes.
The ability to predict and engineer emergent capabilities could accelerate the development of more robust and trustworthy AI, influencing regulatory approaches and societal integration.
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Read at arXiv cs.LG