Knowledge Acquisition During Pre-training? Large Language Models Learn Better With Auxiliary Views
Joseph Lee, Yidi Huang, Dokyoon Kim, Shu Yang, Li Shen
cs.CL · cs.AI
Quick verdict
Token-matched auxiliary views beat source/paraphrase for knowledge acquisition at 7B+ scale; well-controlled study, though single-seed, no significance tests.
Summary
The paper asks how knowledge should be represented in pre-training text. Using 36 documents (12 arXiv CS papers, 12 US federal appellate opinions, 12 medical case reports, verified absent from OLMo-2's corpus via Infini-gram), the authors continue pre-training OLMo-2 models (1B-32B) with 100 steps of single-batch knowledge injection, comparing the source document, paraphrases (Para. 9), and paraphrases plus synthetic auxiliary views (textbooks, Stack Exchange Q&A, blogs) under a matched knowledge-token budget. Evaluation uses 6,435 factual and 430 inference cloze probes plus MCQ variants. Auxiliary views outperform both baselines on factual recall and inference despite containing target spans less often (Table 3); the gap widens with model size (Figure 2) and holds in a pre-training-faithful continuation (Table 5), on Qwen-2.5-7B (Table 11), and with human-written views (only 2 documents). Gains are uncorrelated with generator strength across 11 configurations (Table 13; r=-0.24, n=10). Paraphrasing helps only at smaller batch sizes, reconciling conflicting prior work. Mechanistically, auxiliary views reduce parameter movement and redistribute change toward middle and final layers. Limitations: 36 documents, single seed without significance tests, small-n correlations, probe and view generation by related GPT models, medical factual probes as an exception, and scale capped at 32B.
Review complete
Assessment at a glance
5
risk flags identified
● high 0
● medium 3
● low 2
5 strengths · 5 weaknesses · 5 author questions
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Key claims under review
- Holding the knowledge-token budget fixed, reallocating tokens from document repetition to auxiliary views improves both factual recall and inference for OLMo-2 1B-32B, with benefits growing with model scale.
- The effectiveness of auxiliary views is independent of the strength of the generating teacher model, indicating they act as data augmentation rather than distillation (Table 13).
- Paraphrasing aids acquisition only at smaller batch sizes, which reconciles the conflicting findings of Chang et al. (2024) and Allen-Zhu and Li (2024).
- Auxiliary views leave a distinct mechanistic signature: reduced overall parameter movement (compression) and redistribution of weight change toward middle and final layers, absent at 1B.
- Contextual and prerequisite knowledge aid learning under prior-knowledge gaps, though neither consistently surpasses token-matched paraphrasing.
Strengths
Rigorous confound control
Token matching equalizes knowledge-bearing tokens across conditions; Table 3 shows auxiliary views contain factual target spans less often than source (0.112 vs 0.391 per 1k tokens) yet still win; Table 4 shows the advantage persists with upsampling halved (0.412 vs 0.382) and removed (0.392 vs 0.380).
Layered generalization checks
Pre-training-faithful continuation from OLMo-2 7B step 925,000 with restored optimizer state (Table 5), cross-family replication on Qwen-2.5-7B (Table 11), human-written views from the open web (Figure 3), and a 1B-32B scale sweep (Figure 2).
Reconciles conflicting prior work
Systematic teacher-strength study
Released artifacts at scale
Weaknesses
No uncertainty quantification
Small document base with subgroup fragility
Probe-view circularity only partially addressed
Mechanistic claims are correlational
Regime and scale limits
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Scope & Claims
The paper asks how knowledge should be represented in pre-training text (RQ1) and what surrounding knowledge should accompany it (RQ2), introducing auxiliary views: genre-and-context reformulations (textbook chapters, Stack Exchange Q&A, blogs) of the same document, contrasted with paraphrasing as mere linguistic variation of one view. Five findings are claimed (Abstract, Sections 4-7): repetition is necessary and paraphrasing helps conditionally; at a fixed knowledge-token budget, reallocating repetition tokens to auxiliary views improves factual recall and inference; the effect is independent of teacher strength; contextual and prerequisite knowledge help under prior-knowledge gaps; and auxiliary views induce layer-wise parameter-change signatures. The title's generalization to pre-training at large extrapolates from 100-step continued pre-training (CPT) injections on 36 documents.
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Frequently asked questions
What are auxiliary views in LLM pre-training?
They are reformulations of the same knowledge in different genres and contexts - textbook chapters, Stack-Exchange-style Q&A, blogs - as opposed to paraphrases, which only reword a single view. This paper shows that at a fixed token budget, training on such views helps language models acquire the underlying knowledge better than repeating the source document or its paraphrases.
Does paraphrasing training data help LLMs learn facts?
According to this study, paraphrasing helps mainly at smaller batch sizes because it prevents an overfitting collapse that source repetition suffers after roughly 20 exposures. At large batch sizes (around 256 and above) its advantage largely disappears, while richer reformulations called auxiliary views still improve learning.
Do you need a strong teacher model to generate synthetic training data views?
The paper finds you do not: across 11 generator configurations (gpt-oss-20B/120B, Gemma-4 12B/31B, GLM-5/5.2, GPT-5-mini variants), downstream factual accuracy stayed within a narrow 0.405-0.422 band, above the 0.396 paraphrase baseline, and was uncorrelated with the generator's size or its own factual accuracy.
At what model size do auxiliary views start helping?
In OLMo-2 experiments, the benefit emerges from 7B parameters upward and widens through 13B and 32B; the 1B model derived little advantage, and the distinctive layer-wise parameter-change pattern also appeared only at 7B and above.
About this review
This is an AI-generated preliminary review by Evolve.Pub, produced from the openly available text of the paper. It is not peer review and does not represent the authors' views. The canonical record of the paper is at the publisher link above.
- Figure panels are extracted only as axis-tick glyph runs, so per-point curve values in Figures 1-5 and 7-12 cannot be read from the text; all referenced table values are present and were used instead.
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