---
title: "CodeDPO — paper by Zhang et al. (2025) — summary and ideas"
description: "Supervised fine-tuning helps code LLMs, but it does not reliably teach them to favor correct programs… 39 references, cited by 10 papers here."
authors: ["Kechi Zhang", "Ge Li", "Yihong Dong", "Jingjing Xu", "Jun Zhang", "Jing Su", "Yongfei Liu", "Zhi Jin"]
year: 2025
venue: "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2025.acl-long.771/"
doi: "10.18653/v1/2025.acl-long.771"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/zhang-2025-codedpo-aligning-code-models-self/"
---
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# CodeDPO: Aligning Code Models with Self Generated and Verified Source Code

**Authors:** [Kechi Zhang](https://md.research.elamur.ai/free/en/authors/kechi-zhang/), [Ge Li](https://md.research.elamur.ai/free/en/authors/ge-li/), [Yihong Dong](https://md.research.elamur.ai/free/en/authors/yihong-dong/), [Jingjing Xu](https://md.research.elamur.ai/free/en/authors/jingjing-xu/), [Jun Zhang](https://md.research.elamur.ai/free/en/authors/jun-zhang/), [Jing Su](https://md.research.elamur.ai/free/en/authors/jing-su/), Yongfei Liu and [Zhi Jin](https://md.research.elamur.ai/free/en/authors/zhi-jin/) · **Year:** 2025 · **Venue:** [Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2025](https://md.research.elamur.ai/free/en/venues/acl/2025/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2025.acl-long.771/) · [PDF](https://aclanthology.org/2025.acl-long.771.pdf) · [DOI 10.18653/v1/2025.acl-long.771](https://doi.org/10.18653/v1/2025.acl-long.771) · [OpenAlex](https://openalex.org/W4412945242)

**Citations:** 10 citations (OpenAlex, 2026-10-14) · 10 in our library, of the 149,439 papers whose bibliographies we read

## Summary
Supervised fine-tuning helps code LLMs, but it does not reliably teach them to favor correct programs over flawed ones. It also does nothing to reward faster code. CodeDPO addresses both gaps with preference learning.

Programming prompts are derived, OSS-Instruct style, from concepts mined in real functions taken from The Stack. The model then produces candidate solutions and test cases for each prompt and executes them against each other. The method rests on two assumptions:
- A test that many solutions can run is more trustworthy.
- A solution that passes many trustworthy tests is probably correct.

A PageRank-inspired procedure iteratively refines these mutual credibility scores. Execution time measured on the most credible tests then ranks solutions by efficiency. The resulting balanced preference pairs are used for DPO training.

Because the pipeline needs no external test suites or strong teacher models such as GPT-4, it scales cheaply. Across five benchmarks it improved both correctness and speed of generated code, and a 6.7B model reached 83.5% on HumanEval.

## Key points
- **Evaluation:** HumanEval, HumanEval+, MBPP, MBPP+ and DS-1000.
- **DeepSeekCoder-1.3B-base results:** HumanEval rose from 31.53 to 42.07 and MBPP+ from 48.67 to 53.43. Using only 25–75% of the data gave smaller gains.
- **Seed data:** about 100k functions from The Stack. Post-sampling decontamination dropped fewer than 30 samples.
- **Overlap with HumanEval:** average cosine similarity was 0.109, versus 0.169 for Self-Instruct, 0.131 for Evol-Instruct and 0.105 for OSS-Instruct.
- **Comparison with prior work:** PLUM depends on GPT-4 tests, must use KTO because its data is imbalanced, and ignores efficiency. Code-Optimize uses only 384 MBPP problems.

*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

- [Peking University](https://md.research.elamur.ai/free/en/universities/peking-university/) — University, Beijing, China · 244 works · 39,359 citations
- [ByteDance](https://md.research.elamur.ai/free/en/companies/bytedance/) — Company, Beijing, China · 41 works · 4,180 citations

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🧭 Topics (1): [💻 Code & programming languages](https://md.research.elamur.ai/free/en/topics/code-and-programming/) · 129 works

## Cite

Cite as: Kechi Zhang, Ge Li, Yihong Dong, Jingjing Xu, Jun Zhang, Jing Su, Yongfei Liu and Zhi Jin. 2025. CodeDPO: Aligning Code Models with Self Generated and Verified Source Code. Proceedings of the 63rd 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.

```bibtex
@inproceedings{zhang2025codedpo,
  title = {{CodeDPO: Aligning Code Models with Self Generated and Verified Source Code}},
  author = {Kechi Zhang, Ge Li, Yihong Dong, Jingjing Xu, Jun Zhang, Jing Su, Yongfei Liu and Zhi Jin},
  booktitle = {Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2025},
  doi = {10.18653/v1/2025.acl-long.771},
  url = {https://doi.org/10.18653/v1/2025.acl-long.771}
}
```

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36. Kechi Zhang, Ge Li, Huangzhao Zhang, and Zhi Jin. 2024b. Hirope: Length extrapolation for code models using hierarchical position. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 13615–13627. [find](https://scholar.google.com/scholar?q=Kechi+Zhang%2C+Ge+Li%2C+Huangzhao+Zhang%2C+and+Zhi+Jin%2E+2024b%2E+Hirope%3A+Length+extrapolation+for+code+models+using+hierarchical+position%2E+In+Proceedings+of+the+62nd+Annual+Meeting+of+the+Association+for+Comp)
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*Updated 31 October 2026*