English

xGen-small Technical Report

Computation and Language 2025-05-13 v1 Artificial Intelligence

Abstract

We introduce xGen-small, a family of 4B and 9B Transformer decoder models optimized for long-context applications. Our vertically integrated pipeline unites domain-balanced, frequency-aware data curation; multi-stage pre-training with quality annealing and length extension to 128k tokens; and targeted post-training via supervised fine-tuning, preference learning, and online reinforcement learning. xGen-small delivers strong performance across various tasks, especially in math and coding domains, while excelling at long context benchmarks.

Cite

@article{arxiv.2505.06496,
  title  = {xGen-small Technical Report},
  author = {Erik Nijkamp and Bo Pang and Egor Pakhomov and Akash Gokul and Jin Qu and Silvio Savarese and Yingbo Zhou and Caiming Xiong},
  journal= {arXiv preprint arXiv:2505.06496},
  year   = {2025}
}
R2 v1 2026-06-28T23:27:55.947Z