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CodeEvo: Interaction-Driven Synthesis of Code-centric Data through Hybrid and Iterative Feedback

Paper · 2026 · Qiushi Sun, Jingyang Gong, Lei Li et al.

Summary

Training code-generation LLMs requires large numbers of instruction–code pairs. Manual curation is expensive, and popular automatic methods such as Self-Instruct and Evol-Instruct depend on fixed prompting heuristics. As a result, they often produce vague tasks and code that fails to compile or pass its tests. CodeEvo addresses this with two cooperating LLM agents. A Coder writes solutions and unit tests and revises them. A Reviewer judges the output and creates new tasks. Before writing each new instruction, the Reviewer drafts a keyword-driven Schema, a plan for combining concepts and setting complexity, so instructions and code evolve together. The Schema can also simplify a task the Coder cannot solve, which yields graded difficulty. Candidates are kept only if they pass a hybrid check that combines compiler and execution signals with LLM semantic review. The pipeline needs only seed instructions, with no reference code or human labels. It produced CodeEvo-100K, and models fine-tuned on this data beat established synthesis baselines, including ones trained on several times more data. This suggests that pipeline design matters more than data volume.

Key points

  • Released dataset: CodeEvo-100K contains instruction–code pairs with stepped difficulty levels and full synthesis trajectories. Code and data are available at github.com/QiushiSun/CodeEvo.
  • Motivating problem: a pilot analysis of Evol-Instruct-style data generated with Qwen2.5-Coder-32B, DeepSeek-V3 and Llama-3.1-8B found many samples with compile errors or failing tests.
  • Instruction difficulty: in a human study, five programmers rated difficulty on a 1–5 scale. CodeEvo instructions scored highest (mean ≈ 3.5), ahead of Evol-Instruct and the original seeds, and also showed a higher lower bound.
  • Diversity: across 1,000 samples embedded with text-embedding-3-small, CodeEvo instructions had the lowest average pairwise cosine similarity. Pair-level diversity was comparable to OSS-Instruct, which is built from human-written code.
  • Survival rate and limitations: only a small share of candidates survives filtering, and that share falls with each synthesis round. The authors acknowledge dependence on the backbone model, slower throughput, and imperfect auto-generated tests.

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

 Institutions

🎓Carnegie Mellon UniversityPittsburgh · United States539 works · 179,533 citations🎓New York UniversityNew York · United States201 works · 100,180 citations🎓University of Hong KongPok Fu Lam · China89 works · 11,774 citations🔬Shanghai Artificial Intelligence LaboratoryShanghai · China117 works · 11,281 citations🎓Shanghai Innovation InstituteShanghai · China24 works · 262 citations

 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 Qiushi Sun, Jingyang Gong, Lei Li, Qipeng Guo and Fei Yuan. 2026. CodeEvo: Interaction-Driven Synthesis of Code-centric Data through Hybrid and Iterative Feedback. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.acl-long.438

@inproceedings{sun2026codeevo,
  title = {{CodeEvo: Interaction-Driven Synthesis of Code-centric Data through Hybrid and Iterative Feedback}},
  author = {Qiushi Sun, Jingyang Gong, Lei Li, Qipeng Guo and Fei Yuan},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.438},
  url = {https://doi.org/10.18653/v1/2026.acl-long.438}
}

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