Original title: Knowledge Acquisition During Pre-training? Large Language Models Learn Better With Auxiliary Views
Joseph Lee, Yidi Huang, Dokyoon Kim, Shu Yang +1 · September 2026
We still do not fully understand how large language models gain knowledge when they train on text. Researchers tested whether seeing information rewritten in new ways helps models learn better. They ran controlled tests to find out. First, they showed that repeating facts is necessary to learn them, but paraphrasing only helps when training with smaller batch sizes. Next, they kept the total number of words fixed. Swapping simple repetition for these rewritten views actually improved learning, even for remembering basic facts. The helper model writing these views did not need to be especially strong to help. Finally, providing contextual and foundational knowledge helped fill prior knowledge gaps. In short, seeing diverse rewrites helps models learn much better.
A student prepares for an exam by only rereading the same notes repeatedly. They might memorize the exact words but fail to understand the concepts for new questions. In this paper, AI models similarly learn from repeated data, but when the information is reformulated into different views, they improve at recalling and applying facts.
This helps AI builders understand why varied training data leads to better models. It could make AI training more efficient, affecting the tools we use daily.
The abstract does not specify the exact models or datasets used, so it's unclear how broadly these findings apply. The study focuses only on the pre-training phase and does not address how auxiliary views affect later training stages or real-world tasks.
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Knowledge Acquisition During Pre-training? Large Language Models Learn Better With Auxiliary Views — Joseph Lee, Yidi Huang, Dokyoon Kim et al.
Abstract licence: CC0
Explanation generated by Synthesis from the abstract — it is not the original paper. This is a preprint, so it hasn't necessarily been peer reviewed.