To Code, or Not To Code? Exploring Impact of Code in Pre-training
Authors: name: Viraat Aryabumi, name: Yixuan Su, name: Raymond Ma, name: Adrien Morisot, name: Ivan Zhang, name: Acyr Locatelli, name: Marzieh Fadaee, name: Ahmet Üstün, name: Sara Hooker

Including code in the pre-training data mixture, even for models not
specifically designed for code, has become a common practice in LLMs
pre-training. While there has been anecdotal consensus among practitioners that
code data plays a vital role in general LLMs' performance, there is only
limited work analyzing the precise impact of code on non-code tasks. In this
work, we systematically investigate the impact of code data on general
performance. We ask "what is the impact of code data used in pre-training on a
large variety of downstream tasks beyond code generation". We conduct extensive
ablations and evaluate across a broad range of natural language reasoning
tasks, world knowledge tasks, code benchmarks, and LLM-as-a-judge win-rates for
models with sizes ranging from 470M to 2.8B parameters. Across settings, we
find a consistent results that code is a critical building block for
generalization far beyond coding tasks and improvements to code quality have an
outsized impact across all tasks. In particular, compared to text-only
pre-training, the addition of code results in up to relative increase of 8.2%
in natural language (NL) reasoning, 4.2% in world knowledge, 6.6% improvement
in generative win-rates, and a 12x boost in code performance respectively. Our
work suggests investments in code quality and preserving code during
pre-training have positive impacts.