A Pre-Training Analogue of Grokking in Language Models: Tracing Delayed Grammatical Generalization
arXiv:2606.00230v1 Announce Type: new Abstract: Grokking, the phenomenon in which neural networks generalize long after fitting their training data, has been studied in supervised settings on many epochs. LLM pre-training instead involves next-token prediction over an unlabeled corpus, with limited data repetition and no explicit train/validation split. To address this, we propose an exposure-based framework that enables the study of grokking-like dynamics during LLM pre-training. We ground our evaluation in BLiMP minimal pairs, which provide controlled grammatical contrasts. For every BLiMP m
This research provides a novel framework to understand a fundamental phenomenon (grokking) in the context of large language model pre-training, which is a critical area for current AI development.
Understanding the mechanisms behind 'grokking' in LLMs during pre-training is crucial for developing more efficient, reliable, and interpretable AI, directly impacting the quality and capability of future AI systems.
This research shifts the understanding of generalization in LLMs from purely supervised learning to an exposure-based framework, possibly leading to new training paradigms and performance optimizations.
- · AI researchers
- · Large Language Model developers
- · AI platform providers
- · Developers relying on black-box optimization
- · AI models with poor generalization capabilities
Improved understanding of LLM generalization during pre-training.
Development of new training techniques that leverage this understanding to achieve better performance with less data or compute.
Enhanced interpretability and trustworthiness of advanced AI systems, potentially accelerating AI adoption in sensitive domains.
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Read at arXiv cs.LG