  Selecting high-quality data for pre-training is crucial in shaping the
downstream task performance of language models. A major challenge lies in
identifying this optimal subset, a problem generally considered intractable,
thus necessitating scalable and effective heuristics. In this work, we propose
a data selection method, CoLoR-Filter (Conditional Loss Reduction Filtering),
which leverages an empirical Bayes-inspired approach to derive a simple and
computationally efficient selection criterion based on the relative loss values
of two auxiliary models.
  In addition to the modeling rationale, we evaluate CoLoR-Filter empirically
on two language modeling tasks: (1) selecting data from C4 for domain
adaptation to evaluation on Books and (2) selecting data from C4 for a suite of
downstream multiple-choice question answering tasks. We demonstrate favorable
scaling both as we subselect more aggressively and using small auxiliary models
to select data for large target models. As one headline result, CoLoR-Filter
data selected using a pair of 150m parameter auxiliary models can train a 1.2b
parameter target model to match a 1.2b parameter model trained on 25b randomly
selected tokens with 25x less data for Books and 11x less data for the
downstream tasks.
  Code: https://github.com/davidbrandfonbrener/color-filter-olmo
  Filtered data:
https://huggingface.co/datasets/davidbrandfonbrener/color-filtered-c4
