@inproceedings{yang-etal-2026-perfcoder,
title = "{P}erf{C}oder: Large Language Models for Interpretable Code Performance Optimization",
author = "Yang, Jiuding and
Lu, Shengyao and
Liu, Hongxuan and
Bagi, Shayan Shirahmad Gale and
Fazel, Zahra and
Czajkowski, Tomasz and
Niu, Di",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.675/",
pages = "13807--13823",
ISBN = "979-8-89176-395-1",
abstract = "Large language models (LLMs) have achieved remarkable progress in automatic code generation, yet their ability to produce high-performance code remains limited, despite its importance in real-world software systems. We argue that this limitation stems not only from data scarcity, but more fundamentally from the lack of supervision that guides interpretable and effective performance improvements. We introduce PerfCoder, a family of LLMs designed to generate performance-enhanced code through interpretable and customized optimization strategies. PerfCoder is fine-tuned on curated real-world optimization trajectories with human-readable annotations and further aligned via reinforcement fine-tuning using runtime feedback, enabling it to generate input-specific strategies and apply them directly without iterative refinement. On the PIE code performance benchmark, PerfCoder outperforms all existing models in both runtime speedup and effective optimization rate, demonstrating that code performance optimization requires strategy awareness rather than scale alone. Moreover, PerfCoder produces interpretable feedback that can guide larger LLMs in a planner{--}optimizer workflow, substantially improving the performance of 32B models and GPT-5 on code optimization."
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<abstract>Large language models (LLMs) have achieved remarkable progress in automatic code generation, yet their ability to produce high-performance code remains limited, despite its importance in real-world software systems. We argue that this limitation stems not only from data scarcity, but more fundamentally from the lack of supervision that guides interpretable and effective performance improvements. We introduce PerfCoder, a family of LLMs designed to generate performance-enhanced code through interpretable and customized optimization strategies. PerfCoder is fine-tuned on curated real-world optimization trajectories with human-readable annotations and further aligned via reinforcement fine-tuning using runtime feedback, enabling it to generate input-specific strategies and apply them directly without iterative refinement. On the PIE code performance benchmark, PerfCoder outperforms all existing models in both runtime speedup and effective optimization rate, demonstrating that code performance optimization requires strategy awareness rather than scale alone. Moreover, PerfCoder produces interpretable feedback that can guide larger LLMs in a planner–optimizer workflow, substantially improving the performance of 32B models and GPT-5 on code optimization.</abstract>
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%0 Conference Proceedings
%T PerfCoder: Large Language Models for Interpretable Code Performance Optimization
%A Yang, Jiuding
%A Lu, Shengyao
%A Liu, Hongxuan
%A Bagi, Shayan Shirahmad Gale
%A Fazel, Zahra
%A Czajkowski, Tomasz
%A Niu, Di
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F yang-etal-2026-perfcoder
%X Large language models (LLMs) have achieved remarkable progress in automatic code generation, yet their ability to produce high-performance code remains limited, despite its importance in real-world software systems. We argue that this limitation stems not only from data scarcity, but more fundamentally from the lack of supervision that guides interpretable and effective performance improvements. We introduce PerfCoder, a family of LLMs designed to generate performance-enhanced code through interpretable and customized optimization strategies. PerfCoder is fine-tuned on curated real-world optimization trajectories with human-readable annotations and further aligned via reinforcement fine-tuning using runtime feedback, enabling it to generate input-specific strategies and apply them directly without iterative refinement. On the PIE code performance benchmark, PerfCoder outperforms all existing models in both runtime speedup and effective optimization rate, demonstrating that code performance optimization requires strategy awareness rather than scale alone. Moreover, PerfCoder produces interpretable feedback that can guide larger LLMs in a planner–optimizer workflow, substantially improving the performance of 32B models and GPT-5 on code optimization.
%U https://aclanthology.org/2026.findings-acl.675/
%P 13807-13823
Markdown (Informal)
[PerfCoder: Large Language Models for Interpretable Code Performance Optimization](https://aclanthology.org/2026.findings-acl.675/) (Yang et al., Findings 2026)
ACL
- Jiuding Yang, Shengyao Lu, Hongxuan Liu, Shayan Shirahmad Gale Bagi, Zahra Fazel, Tomasz Czajkowski, and Di Niu. 2026. PerfCoder: Large Language Models for Interpretable Code Performance Optimization. In Findings of the Association for Computational Linguistics: ACL 2026, pages 13807–13823, San Diego, California, United States. Association for Computational Linguistics.