Papers › Coder Reviewer Reranking for Code Generation
Coder Reviewer Reranking for Code Generation
Tianyi Zhang, Tao Yu, Tatsunori B. Hashimoto, Mike Lewis, Wen-tau Yih, Daniel Fried, Sida I. Wang
Sampling diverse programs from a code language model and reranking with model likelihood is a popular method for code generation but it is prone to preferring degenerate solutions. Inspired by collaborative programming, we propose Coder-Reviewer reranking. We augment Coder language models from past work, which generate programs given language instructions, with Reviewer models, which evaluate the likelihood of the instruction given the generated programs. We perform an extensive study across six datasets with eight models from three model families. Experimental results show that Coder-Reviewer reranking leads to consistent and significant improvement (up to 17% absolute accuracy gain) over reranking with the Coder model only. When combined with executability filtering, Coder-Reviewer reranking can often outperform the minimum Bayes risk method. Coder-Reviewer reranking is easy to implement by prompting, can generalize to different programming languages, and works well with off-the-shelf hyperparameters.
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Code
Syntology Ran 2 of 2 code samples harvested from 1 repository linked to this paper; 0 have no recorded run. Of those that ran: 1 ran · honoured contract; 1 ran · violated contract.
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Code Syntology ran Syntology
2 samples harvested; 2 ran; 1 honoured the contract we drafted; 0 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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64d47c9d4e0d0c5e · report
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Code Generation | MBPP | code-davinci-002 175B + Reviewer | Accuracy | 66.9 | #36 of 99 | Archive leaderboard | report |
| Code Generation | MBPP | code-davinci-002 175B + Coder-Reviewer | Accuracy | 66.4 | #37 of 99 | Archive leaderboard | report |
| Code Generation | MBPP | code-davinci-002 175B + MBR-Exec | Accuracy | 63 | #42 of 99 | Archive leaderboard | report |
| Code Generation | MBPP | code-cushman-001 12B + MBR-Exec | Accuracy | 48.3 | #66 of 99 | Archive leaderboard | report |
| Code Generation | MBPP | CodeGen 16B + MBR-Exec | Accuracy | 47.3 | #69 of 99 | Archive leaderboard | report |
| Code Generation | MBPP | CodeGen 16B + Coder-Reviewer | Accuracy | 46.2 | #73 of 99 | Archive leaderboard | report |
| Code Generation | MBPP | CodeGen 16B + Reviewer | Accuracy | 44.1 | #78 of 99 | Archive leaderboard | report |
| Code Generation | MBPP | InCoder 6.7B + MBR-Exec | Accuracy | 26.7 | #92 of 99 | Archive leaderboard | report |
| Code Generation | MBPP | InCoder 6.7B + Coder-Reviewer | Accuracy | 26.1 | #93 of 99 | Archive leaderboard | report |
| Code Generation | MBPP | InCoder 6.7B + Reviewer | Accuracy | 24.4 | #94 of 99 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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