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Yuhao Qing

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6 papers

2 author rows

NeurIPS Conference 2025 Conference Paper

Afterburner: Reinforcement Learning Facilitates Self-Improving Code Efficiency Optimization

Mingzhe Du
Anh Tuan Luu
Yue Liu
Yuhao Qing
Dong Huang
Xinyi He
Qian Liu
Zejun Ma

Large Language Models (LLMs) generate functionally correct solutions but often fall short in code efficiency, a critical bottleneck for real-world deployment. In this paper, we introduce a novel test-time iterative optimization framework to address this, employing a closed-loop system where LLMs iteratively refine code based on empirical performance feedback from an execution sandbox. We explore three training strategies: Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Group Relative Policy Optimization~(GRPO). Experiments on our Venus dataset and the APPS benchmark show that SFT and DPO rapidly saturate in efficiency gains. In contrast, GRPO, using reinforcement learning (RL) with execution feedback, continuously optimizes code performance, significantly boosting both pass@1 (from 47% to 62%) and the likelihood of outperforming human submissions in efficiency (from 31% to 45%). Our work demonstrates effective test-time code efficiency improvement and critically reveals the power of RL in teaching LLMs to truly self-improve code efficiency. We released our code and data at https: //github. com/Elfsong/Afterburner.

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NeurIPS Conference 2025 Conference Paper

EffiBench-X: A Multi-Language Benchmark for Measuring Efficiency of LLM-Generated Code

Yuhao Qing
Boyu Zhu
Mingzhe Du
Zhijiang Guo
Terry Yue Zhuo
Qianru Zhang
Jie Zhang
Heming Cui

Existing code generation benchmarks primarily evaluate functional correctness, with limited attention to code efficiency, and they are often restricted to a single language such as Python. To address this gap, we introduce EffiBench‑X, the first large‑scale multi‑language benchmark specifically designed for robust efficiency evaluation of LLM‑generated code. EffiBench‑X supports Python, C++, Java, JavaScript, Ruby, and Go, and comprises competitive programming tasks paired with human‑expert solutions as efficiency baselines. Evaluating state‑of‑the‑art LLMs on EffiBench‑X reveals that while models frequently generate functionally correct code, they consistently underperform human experts in efficiency. Even the most efficient LLM‑generated solutions (e. g. , Qwen3‑32B) achieve only around 62% of human efficiency on average, with significant language‑specific variation: models tend to perform better in Python, Ruby, and JavaScript than in Java, C++, and Go (e. g. , DeepSeek‑R1’s Python code is markedly more efficient than its Java code). These findings highlight the need for research into optimization‑oriented methods to improve the efficiency of LLM‑generated code across diverse languages. The dataset and evaluation infrastructure are publicly available at https: //github. com/EffiBench/EffiBench-X. git and https: //huggingface. co/datasets/EffiBench/effibench-x.

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ICML Conference 2025 Conference Paper

EffiCoder: Enhancing Code Generation in Large Language Models through Efficiency-Aware Fine-tuning

Dong Huang 0005
Guangtao Zeng
Jianbo Dai
Meng Luo 0010
Han Weng
Yuhao Qing
Heming Cui
Zhijiang Guo

As large language models (LLMs) play an increasingly important role in code generation, enhancing both correctness and efficiency has become crucial. Current methods primarily focus on correctness, often overlooking efficiency. To address this gap, we introduce SWIFTCODE to improve both aspects by fine-tuning LLMs on a high-quality dataset comprising correct and efficient code samples. Our methodology involves leveraging multiple LLMs to generate diverse candidate code solutions for various tasks across different programming languages. We then evaluate these solutions by directly measuring their execution time and memory usage through local execution. The code solution with the lowest execution time and memory consumption is selected as the final output for each task. Experimental results demonstrate significant improvements when fine-tuning with SWIFTCODE. For instance, Qwen2. 5-Coder-7B-Instruct’s pass@1 score increases from 44. 8% to 57. 7%, while the average execution time for correct tasks decreases by 48. 4%. SWIFTCODE offers a scalable and effective solution for advancing AI-driven code generation, benefiting both software development and computational problem-solving.

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AAAI Conference 2025 Conference Paper

Integrating Low-Level Visual Cues for Enhanced Unsupervised Semantic Segmentation

Yuhao Qing
Dan Zeng
Shaorong Xie
Kaer Huang
Yueying Wang

Unsupervised semantic segmentation algorithms aim to identify meaningful semantic groups without annotations. Recent approaches leveraging self-supervised transformers as pre-training backbones have successfully obtained high-level dense features that effectively express semantic coherence. However, these methods often overlook local semantic coherence and low-level features such as color and texture. We propose integrating low-level visual cues to complement high-level visual cues derived from self-supervised pre-training branches. Our findings indicate that low-level visual cues provide a more coherent recognition of color-texture aspects, ensuring the continuity of spatial structures within classes. This insight led us to develop IL2Vseg, an unsupervised semantic segmentation method that leverages the complementation of low-level visual cues. The core of IL2Vseg is a spatially-constrained fuzzy clustering algorithm based on color affinities, which preserves the intra-class affinity of spatially-adjacent and similarly-colored pixels in low-level visual cues. Additionally, to effectively couple low-level and high-level visual cues, we introduce a feature similarity loss function to optimize the feature representation of fused visual cues. To further enhance consistent feature learning, we incorporate contrast loss functions based on color invariance and luminosity invariance, which improve the learning of features from different semantic categories. Extensive experiments on multiple datasets, including COCO-Stuff-27, Cityscapes, Potsdam, and MaSTr1325, demonstrate that IL2Vseg achieves state-of-the-art results.

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NeurIPS Conference 2024 Conference Paper

EffiBench: Benchmarking the Efficiency of Automatically Generated Code

Dong Huang
Yuhao Qing
Weiyi Shang
Heming Cui
Jie M. Zhang

Code generation models have increasingly become integral to aiding software development. Although current research has thoroughly examined the correctness of the code produced by code generation models, a vital aspect that plays a pivotal role in greencomputing and sustainability efforts — the efficiency of the generated code — has often been neglected. This paper presents Effibench, a benchmark with 1, 000 efficiency-critical coding problems to assess the efficiency of code generated by code generation models. EffiBench contains a diverse set of LeetCode coding problems. Each problem is paired with an executable human-written canonical solution, which obtains the SOTA efficiency on the LeetCode solution leaderboard. With EffiBench, we empirically examine the ability of 42 large language models (35 open-source and 7 closed-source) to generate efficient code. Our evaluation results demonstrate that the efficiency of the code generated by LLMs is generally worse than the efficiency of human-written canonical solutions. For example, GPT-4 generated code has an average \textbf{3. 12} times execution time that of the human-written canonical solutions. In the most extreme cases, the execution time and total memory usage of GPT-4 code are \textbf{13. 89} and \textbf{43. 92} times that of the canonical solutions. The source code of EffiBench is released on https: //github. com/huangd1999/EffiBench. We also provide the LeaderBoard in https: //huggingface. co/spaces/EffiBench/effibench-leaderboard.

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NeurIPS Conference 2024 Conference Paper

EffiLearner: Enhancing Efficiency of Generated Code via Self-Optimization

Dong Huang
Jianbo Dai
Han Weng
Puzhen Wu
Yuhao Qing
Heming Cui
Zhijiang Guo
Jie M. Zhang

Large language models (LLMs) have shown remarkable progress in code generation, but their generated code often suffers from inefficiency, resulting in longer execution times and higher memory consumption. To address this issue, we propose EffiLearner, a self-optimization framework that utilizes execution overhead profiles to improve the efficiency of LLM-generated code. EffiLearner first generates code using an LLM, then executes it locally to capture execution time and memory usage profiles. These profiles are fed back to the LLM, which then revises the code to reduce overhead. To evaluate the effectiveness of EffiLearner, we conduct extensive experiments on EffiBench and two commonly used code generation benchmarks with 16 open-source and 6 closed-source models. Our evaluation results demonstrate that through iterative self-optimization, EffiLearner significantly enhances the efficiency of LLM-generated code. For example, the execution time (ET) of StarCoder2-15B for the EffiBench decreases from 0. 93 (s) to 0. 12 (s) which reduces 87. 1\% execution time requirement compared with the initial code. The total memory usage (TMU) of StarCoder2-15B also decreases from 22. 02 (Mb s) to 2. 03 (Mb s), which decreases 90. 8\% total memory consumption during the execution process.

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