Summary
This forum post summarizes an NLP paper (arXiv:2509.04288) by Joseph Lee, Yidi Huang, and Dokyoon Kim, published 2026-09-06, investigating how large language models acquire knowledge during pre-training. The authors hypothesize that auxiliary views—reformulations of knowledge—are causally helpful for learning and design controlled experiments to isolate the effect. Key findings: (1) repetition is necessary for knowledge acquisition, while paraphrasing helps only at smaller batch sizes; (2) under a fixed token budget, shifting tokens from document repetition to auxiliary views improves learning, counterintuitively including factual recall; (3) the benefit of auxiliary views does not depend on the strength of the teacher model generating them; (4) contextual and foundational knowledge forms aid learning when prior knowledge gaps exist; and (5) layer-wise bias and compression analyses reveal how these effects manifest mechanistically. The authors conclude that auxiliary knowledge representations, which naturally arise in large pre-training corpora, are a key factor in pre-training success and help explain why data diversity matters.
Paper Overview
Field: NLP
Authors: Joseph Lee, Yidi Huang, Dokyoon Kim
Published: 2026-09-06
arXiv: 2509.04288
Abstract
Gaps remain in our understanding of how large language models (LLMs) acquire knowledge during pre-training. The authors posit that auxiliary views—reformulations of knowledge—are causally helpful for learning, and design controlled experiments to isolate this effect.
Key findings:
1. Repetition is necessary for knowledge acquisition, and paraphrasing helps only at smaller batch sizes.
2. Token reallocation: holding the token budget fixed, allocating tokens from document repetition to auxiliary views improves learning—counterintuitively, even for factual recall.
3. Teacher strength doesn't matter: the effectiveness of auxiliary views is not contingent on the strength of the teacher model that generates them.
4. Knowledge forms: contextual and foundational knowledge aid learning in the presence of prior knowledge gaps.
5. Mechanistic analysis: layer-wise bias and compression experiments show how these effects manifest.
Taken together, the findings suggest that auxiliary knowledge representations—which naturally arise in large-scale pre-training corpora—are a key factor in pre-training success, and provide a rationale for why data diversity matters.
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*Auto-collected 2026-09-07*
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