LLM Data Curation Insights: Data Rebound and Mid-training

On July 13, @gabriberton posted a systematic summary of the core viewpoints from the podcast "The Information Bottleneck" regarding LLM data curation, calling it one of the most interesting conversations recently. These points revolve around data quality, data filtering, training epochs, and "mid-training." Notably, the judgment that "pre-training data supply has not dried up and available text volume has rebounded" contrasts sharply with current anxieties over the "data wall" and is highly noteworthy.

Pre-training Data is Not Exhausted

Addressing the industry's concern that LLM training data will plateau in the long term, @gabriberton relayed the podcast's view: although sites like Reddit once tightened scraping restrictions, making it seem like training data volume would be locked down, this did not happen. The volume of accessible text data has now returned to levels approaching the "pre-LLM era."

Mid-training Connects Small Datasets

Regarding data quality assessment and adaptation, the podcast suggested a "mid-training" approach: datasets like Wikipedia and GitHub are individually too small to train a model from scratch. Therefore, the common practice is to pre-train on messier, noisier data first, then fine-tune a model for each specific target dataset (mid-training), before finally proceeding to post-training. This phase is seen as a crucial bridge between pre-training and post-training.

Why LLMs Train for Only One Epoch

To explain why language models typically train for only one epoch while vision models train for many, the podcast offered the explanation that vision tasks benefit from better data augmentation methods, which can support multi-epoch training.

2026-07-13 ~ 2026-07-13 · 6 related posts