Knowledge Acquisition During Pre-training? Large Language Models Learn Better With Auxiliary Views
A research paper on arXiv (cs.AI) titled 'Knowledge Acquisition During Pre-training? Large Language Models Learn Better With Auxiliary Views' was published on 2026-09-03. The paper presents controlled experiments showing that auxiliary views (reformulations of knowledge) are causally helpful for LLM learning during pre-training. Key findings include: repetition is necessary for acquisition; paraphrasing helps only at smaller batch sizes; allocating tokens from document repetition to auxiliary views improves learning even for factual recall; effectiveness is not contingent on teacher model strength; contextual and foundational knowledge aid learning with prior knowledge gaps; and effects manifest via layer-wise biases and compression.
The paper investigates how LLMs acquire knowledge during pre-training, focusing on the role of auxiliary views—reformulations of knowledge. Through controlled experiments, the authors confirm that repetition is necessary for acquisition and that paraphrasing helps only at smaller batch sizes. Holding token budget fixed, shifting tokens from document repetition to auxiliary views improves learning, even for factual recall. The effectiveness of auxiliary views does not depend on the strength of the teacher model generating them. The study identifies contextual and foundational knowledge forms that aid learning when prior knowledge gaps exist. Mechanistically, these effects are observed through layer-wise biases and compression. The findings suggest that auxiliary representations of knowledge, naturally present in large pre-training corpora, are a key factor in pre-training success and explain why data diversity matters.
The paper provides causal evidence that auxiliary views (reformulations of knowledge) improve LLM pre-training. It shows that under a fixed token budget, replacing document repetition with auxiliary views enhances learning, including factual recall. The effect is independent of teacher model strength, suggesting that the diversity of representations, not their quality, drives improvement. Mechanistic analysis reveals layer-wise biases and compression patterns associated with these effects. This implies that pre-training data curation should prioritize diverse reformulations over simple repetition, and that model architecture may be adapted to better leverage such auxiliary views.
This research suggests that data diversity, specifically through auxiliary views, is a critical lever for improving LLM pre-training efficiency. For AI labs and companies training large models, this implies that investing in data augmentation and reformulation pipelines could yield better performance per token than simply scaling up data volume. It also indicates that smaller batch sizes may benefit more from paraphrasing, which could influence training infrastructure decisions. The finding that teacher model strength is not crucial lowers the barrier for generating auxiliary views, potentially enabling more cost-effective data preparation.
For companies training LLMs, this research offers a potential efficiency gain: by reallocating token budget from repetition to auxiliary views, they may achieve better model performance without increasing compute costs. This could reduce training expenses and time-to-market. Data providers and annotation services could develop new offerings around generating high-quality auxiliary views. The finding that teacher model strength is not critical lowers the cost of generating such views, making the approach accessible to smaller players. Overall, it supports a business case for investing in data diversity rather than raw data volume.
Future research may explore automated generation of auxiliary views at scale and their integration into pre-training pipelines. We may see new data curation tools that identify and create diverse reformulations. The mechanistic insights could lead to architectural modifications that explicitly model auxiliary views. If these findings hold in larger-scale experiments, they could shift industry practices toward more diverse data strategies and away from simple repetition. Watch for follow-up studies on the optimal ratio of auxiliary views to original documents and their impact on downstream tasks.