IterPref: Focal Preference Learning for Code Generation via Iterative
  Debugging
Preference learning enhances Code LLMs beyond supervised fine-tuning by
leveraging relative quality comparisons. Existing methods construct preference
pairs from
  candidates based on test case success, treating the higher pass rate sample
as positive and the lower as negative. However, this approach does not pinpoint
specific errors in the code, which prevents the model from learning more
informative error correction patterns, as aligning failing code as a whole
lacks the granularity needed to capture meaningful error-resolution
relationships. To address these issues, we propose IterPref, a new preference
alignment framework that mimics human iterative debugging to refine Code LLMs.
IterPref explicitly locates error regions and aligns the corresponding tokens
via a tailored DPO algorithm. To generate informative pairs, we introduce the
CodeFlow dataset, where samples are iteratively refined until passing tests,
with modifications capturing error corrections. Extensive experiments show that
a diverse suite of Code LLMs equipped with IterPref achieves significant
performance gains in code generation and improves on challenging tasks like
BigCodeBench. In-depth analysis reveals that IterPref yields fewer errors. Our
code and data will be made publicaly available.
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Jie Wu
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Haoling Li
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2025-03-05T10:59:05.817Z
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Haoling Li
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Ringo1110
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Xin Zhang
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2025-03-05T10:53:22.999Z
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Pluto
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CharonBony
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Jianwen Luo
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2025-03-05T11:37:40.552Z
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Jane Luo
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Luo2003
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Yangyu Huang
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yangyu huang
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yangyu90
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Ruihang Chu
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Ruihang Chu
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Ruihang
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Yujiu Yang
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2025-03-05T11:37:57.005Z
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Yujiu Yang
user
Thu-redrobot
67c82d2202935d02b0ce0df8
Scarlett Li
admin_assigned
2025-03-05T11:38:04.131Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/Bny5bUc6qFQ_RWpEKpRAW.jpeg
Scarlett Li
user
lisijia0504
IterPref: Focal Preference Learning for Code Generation via Iterative
  Debugging
Preference learning enhances Code LLMs beyond supervised fine-tuning by
leveraging relative quality comparisons. Existing methods construct preference
pairs from
  candidates based on test case success, treating the higher pass rate sample
as positive and the lower as negative. However, this approach does not pinpoint
specific errors in the code, which prevents the model from learning more
informative error correction patterns, as aligning failing code as a whole
lacks the granularity needed to capture meaningful error-resolution
relationships. To address these issues, we propose IterPref, a new preference
alignment framework that mimics human iterative debugging to refine Code LLMs.
IterPref explicitly locates error regions and aligns the corresponding tokens
via a tailored DPO algorithm. To generate informative pairs, we introduce the
CodeFlow dataset, where samples are iteratively refined until passing tests,
with modifications capturing error corrections. Extensive experiments show that
a diverse suite of Code LLMs equipped with IterPref achieves significant
performance gains in code generation and improves on challenging tasks like
BigCodeBench. In-depth analysis reveals that IterPref yields fewer errors. Our
code and data will be made publicaly available.
IterPref refines Code LLMs by aligning error regions through iterative debugging, improving performance and error reduction using the CodeFlow dataset.
preference learning Code LLMs supervised fine-tuning preference pairs error correction patterns iterative debugging error regions tokens DPO algorithm CodeFlow dataset code generation BigCodeBench