Knowledge Acquisition During Pre-training? Large Language Models Learn Better With Auxiliary Views
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Computer Science > Computation and Language
arXiv:2609.04180 (cs)
[Submitted on 3 Sep 2026]
Title:Knowledge Acquisition During Pre-training? Large Language Models Learn Better With Auxiliary Views
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Abstract:Gaps remain in our understanding of how large language models (LLMs) acquire knowledge during pre-training. We posit that auxiliary views, reformulations of knowledge, are causally helpful for learning. We design controlled experiments to isolate this. First, we confirm that repetition is necessary for acquisition and clarify that paraphrasing helps only at smaller batch sizes. Second, holding the token budget fixed, allocating tokens from document repetition to auxiliary views improves learning, counterintuitively, even for factual recall. Third, the effectiveness of auxiliary views is not contingent on the strength of the teacher model that generates them. Fourth, we identify forms of knowledge, contextual and foundational, that aid learning in the presence of prior knowledge gaps. Finally, we examine how these effects manifest mechanistically via layer-wise biases and compression. Together, our findings suggest that auxiliary representations of knowledge, which arise naturally in large pre-training corpora, are a key factor in the success of pre-training and offer a plausible explanation for why data diversity matters.
| Comments: | Accepted to Findings of EMNLP 2026 |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2609.04180 [cs.CL] |
| (or arXiv:2609.04180v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2609.04180
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled Knowledge Acquisition During Pre-training? Large Language Models Learn Better With Auxiliary Views, by Joseph Lee and 4 other authors
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