Large Language Models (LLMs) require high quality instruction data for effective alignment, particularly in code generation tasks where expert curated datasets are expensive to produce. We present Genetic-Instruct, a scalable algorithm for synthesizing large-scale, high quality coding instructions using evolutionary principles. Starting from a small set of seed instructions, Genetic-Instruct generates diverse and challenging instruction-code pairs by leveraging an Instructor-LLM for generation, a Coder-LLM for code synthesis, and a Judge-LLM for automatic quality evaluation. Our proposed approach is highly parallelizable and effective even with a small seed data and weaker generator models. We generated more than 7.5 million coding instructions with the proposed approach. Then we evaluated it by fine-tuning LLMs with the synthetic samples and demonstrated a significant improvement in their code generation capability compared to the other synthetic generation approaches and publicly available datasets. Our results highlight the efficiency, scalability, and generalizability of the Genetic-Instruct framework.
@article{arxiv.2407.21077,
title = {Genetic Instruct: Scaling up Synthetic Generation of Coding Instructions for Large Language Models},
author = {Somshubra Majumdar and Vahid Noroozi and Mehrzad Samadi and Sean Narenthiran and Aleksander Ficek and Wasi Uddin Ahmad and Jocelyn Huang and Jagadeesh Balam and Boris Ginsburg},
journal= {arXiv preprint arXiv:2407.21077},
year = {2025}
}