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arXiv · 2505.06496

xGen-small Technical Report

Erik Nijkamp·Bo Pang·Egor Pakhomov·Akash Gokul·Jin Qu·Silvio Savarese·Yingbo Zhou·Caiming Xiong
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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.

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Erik Nijkamp, Bo Pang, Egor Pakhomov, Akash Gokul, Jin Qu, Silvio Savarese, Yingbo Zhou, Caiming Xiong. 2025-05-10. xGen-small Technical Report. https://arxiv.org/abs/2505.06496

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