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Seed-Coder: Let the Code Model Curate Data for Itself

Paper · 2025 · ByteDance Seed, Yuyu Zhang, Jing Su et al.

Summary

Open code LLMs usually build their pretraining corpora with human-designed heuristics, such as per-language filtering rules or quality classifiers trained on human labels. These are expensive to maintain across many languages, can conflict with one another, and carry subjective bias. Seed-Coder from ByteDance Seed replaces most of this manual work with a model-centric pipeline. After deduplication and a minimal set of basic rules, LLM-based quality scorers decide which data to keep. The pipeline covers GitHub files and repositories, GitHub commits, and code-heavy web documents, and its stages are decoupled so they can be rerun independently. It produced a corpus of 6 trillion tokens. This corpus was used to train three 8B models: a pretrained foundation version along with two derived variants, one tuned to follow instructions and one focused on reasoning. The instruct model uses LLM-generated synthetic SFT data followed by DPO. The reasoning model uses LongCoT reinforcement learning. The models lead open models of comparable size and beat some much larger ones on generation, completion, editing, reasoning, and software-engineering benchmarks. This suggests that automated, LLM-driven curation can scale better than rule-based curation.

Key points

  • Data pipeline and corpus: The pipeline uses LLM quality filters in place of rule sets like the 130+ hand-written filters in OpenCoder. It sorts data into four groups: file-level code, repository-level code, commits, and code-related web data. Training runs in two phases, regular and continued (adding high-quality and long-context data), with Fill-in-the-Middle (FIM) used in both.
  • LiveCodeBench (2410–2502): Seed-Coder-8B-Reasoning reaches 53.6% pass@1. This is 14.6 points above its distillation-only starting point, DeepSeek-R1-Distill-Seed-Coder-8B, at 39.0%. It surpasses DeepSeek-R1-Distill-Qwen-14B and narrowly exceeds Claude-3.7-Sonnet-Thinking.
  • Competitive programming: On IOI'2024 the reasoning model outscores QwQ-32B and DeepSeek-R1. On Codeforces it gets a rating of 1553, close to o1-mini. It still falls well short of the IOI bronze threshold and handles problems rated above 2000 poorly.
  • Stated limitations: No general web data was used, so broad language understanding is weaker. The 6T-token budget is much smaller than Qwen3's 36T, leaving less general and math knowledge.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Fields, topics and concepts

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksInformation Systems · 1,437 worksComputer Science Applications · 102 works

Cite

Cite as ByteDance Seed, Yuyu Zhang, Jing Su, Yifan Sun, Chenguang Xi, Xia Xiao, Shen Zheng, Anxiang Zhang, Kaibo Liu, Daoguang Zan, Tao Sun, Jinhua Zhu, Shulin Xin, Dong Huang, Yetao Bai, Lixin Dong, Chao Li, Jianchong Chen, Hanzhi Zhou, Yifan Huang, Guanghan Ning, Xierui Song, Jiaze Chen, Siyao Liu, Kai Shen, Liang Xiang and Yonghui Wu. 2025. Seed-Coder: Let the Code Model Curate Data for Itself. arXiv:2506.03524.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2506.03524 · arXiv 2506.03524

@misc{seed2025seed,
  title = {{Seed-Coder: Let the Code Model Curate Data for Itself}},
  author = {ByteDance Seed, Yuyu Zhang, Jing Su, Yifan Sun, Chenguang Xi, Xia Xiao, Shen Zheng, Anxiang Zhang, Kaibo Liu, Daoguang Zan, Tao Sun, Jinhua Zhu, Shulin Xin, Dong Huang, Yetao Bai, Lixin Dong, Chao Li, Jianchong Chen, Hanzhi Zhou, Yifan Huang, Guanghan Ning, Xierui Song, Jiaze Chen, Siyao Liu, Kai Shen, Liang Xiang and Yonghui Wu},
  year = {2025},
  doi = {10.48550/arxiv.2506.03524},
  eprint = {2506.03524},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2506.03524}
}

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