Code-Optimise: Self-Generated Preference Data for Correctness and Efficiency (2025.findings-naacl)
Copied to clipboard
| Challenge: | Existing studies have shown that CLMs can generate accurate solutions with no regard for runtime, but at a substantial cost to correctness (down by up to 30%) |
| Approach: | They propose a framework that incorporates correctness and runtime as learning signals via self-generated preference data. |
| Outcome: | The proposed framework reduces the baseline runtimes by 6% and the average length of the generated solutions is reduced by up to 48% on MBPP and 23% on HumanEval. |
Similar Papers
CodeDPO: Aligning Code Models with Self Generated and Verified Source Code (2025.acl-long)
Copied to clipboard
| Challenge: | Existing training methods for code generation do not improve code correctness and efficiency. |
| Approach: | They propose a framework that integrates preference learning into code generation to improve code correctness and efficiency. |
| Outcome: | The proposed framework improves code correctness and efficiency by integrating preference learning into code generation. |
ECCO: Can We Improve Model-Generated Code Efficiency Without Sacrificing Functional Correctness? (2024.emnlp-main)
Copied to clipboard
| Challenge: | Current methods for optimizing program efficiency improve performance measured by execution time, but they often come at the cost of severely decreasing the functional correctness. |
| Approach: | They propose a reproducible benchmark for evaluating program efficiency via two paradigms: natural language (NL) based code generation and history-based code editing. |
| Outcome: | The proposed approach improves performance while maintaining correctness while adding execution information. |
Teaching Your Models to Understand Code via Focal Preference Alignment (2025.emnlp-main)
Copied to clipboard
Jie Wu, Haoling Li, Xin Zhang, Xiao Liu, Yangyu Huang, Jianwen Luo, Yizhen Zhang, Zuchao Li, Ruihang Chu, Yujiu Yang, Scarlett Li
| Challenge: | Existing methods for supervised fine-tuning focus on unit test feedback to construct preference pairs. |
| Approach: | They propose a preference alignment framework that mimics human iterative debugging to refine Code LLMs. |
| Outcome: | Experiments show that Preference Learning improves on BigCodeBench and BigCodeBind tasks. |
CodeArena: Evaluating and Aligning CodeLLMs on Human Preference (2025.emnlp-main)
Copied to clipboard
Jian Yang, Jiaxi Yang, Wei Zhang, Jin Ke, Yibo Miao, Lei Zhang, Liqun Yang, Zeyu Cui, Yichang Zhang, Zhoujun Li, Binyuan Hui, Junyang Lin
| Challenge: | Code large language models (codeLLMs) focus on synthesizing the correct code snippet, ignoring the alignment with human preferences. |
| Approach: | They propose a benchmark code-based on 40 categories and 44 programming languages to emulate real-world coding tasks. |
| Outcome: | The proposed benchmarks show that open-source code LLMs perform better than open-sourced ones. |
Integrating Symbolic Execution into the Fine-Tuning of Code-Generating LLMs (2025.naacl-srw)
Copied to clipboard
| Challenge: | Code-generating Large Language Models (LLMs) have become essential tools in modern software development, enhancing productivity and accelerating development. |
| Approach: | They propose to use Reinforcement Learning and Direct Preference Optimization to fine-tune code-generating Large Language Models (LLMs) by enhancing the training data with symbolic execution techniques. |
| Outcome: | The proposed model improves on the CodeRL benchmark and shows that it is more accurate and objective than the baseline model. |
Thinking Before Running! Efficient Code Generation with Thorough Exploration and Optimal Refinement (2025.findings-acl)
Copied to clipboard
| Challenge: | Recent research indicates that large language models (LLMs) have demonstrated remark-able capabilities in various programming-related domains, such as code generation and code refinement. |
| Approach: | They propose a framework that combines exploration with refinement to reduce test-time computation overhead. |
| Outcome: | The proposed framework outperforms SOTA and AgentCoder on humanEval and MBPP benchmarks while reducing test-time computation overhead and scalability. |
Self-Edit: Fault-Aware Code Editor for Code Generation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing Large language models (LLMs) have low pass rates and accuracy on competitive programming tasks. |
| Approach: | They propose a generate-and-edit approach that uses execution results of generated code from LLMs to improve code quality on competitive programming tasks. |
| Outcome: | The proposed method improves pass@1 by 89% on APPS-dev, 31% on apps-test, and 48% on HumanEval over nine popular code generation LLMs with parameter sizes ranging from 110M to 175B. |
On Sample-Efficient Code Generation (2023.emnlp-industry)
Copied to clipboard
Hojae Han, Yu Jin Kim, Byoungjip Kim, Youngwon Lee, Kyungjae Lee, Kyungmin Lee, Moontae Lee, Kyunghoon Bae, Seung-won Hwang
| Challenge: | Existing approaches to code generation rely on rejection sampling to generate multiple code snippets then select the best. |
| Approach: | They propose a framework that prioritizes sampling on test problems that models can solve. |
| Outcome: | The proposed framework reduces sampling costs while maintaining comparable code generation performance. |
FrontCoder: Scaling Visual Fidelity in Front-End Code Generation (2026.findings-acl)
Copied to clipboard
Jun Feng, Jian Yang, Wei Zhang, Jing Wang, Keyi Chen, Xiaokun Yang, Weicheng Gu, Yihang Lou, Yan Bai, Xianglong Liu
| Challenge: | Existing work on front-end code generation fails to provide visual fidelity and rendering quality for front- end developers. |
| Approach: | They propose a three-stage pipeline to enhance front-end code generation capabilities in LLMs . they use synthetic data, quality-controlled supervised fine-tuning, and reinforcement learning . |
| Outcome: | The proposed model achieves competitive performance with frontier models while maintaining generation efficiency. |
Learning More from Less: Exploiting Counterfactuals for Data-Efficient Chart Understanding (2026.acl-long)
Copied to clipboard
Jianzhu Bao, Haozhen Zhang, Kuicai Dong, Bozhi Wu, Sarthak Ketanbhai Modi, Zi Pong Lim, Yon Shin Teo, Wenya Wang
| Challenge: | Chart understanding is a critical capability for vision-language models, serving as a cornerstone for automated data analysis, document understanding, and scientific research. |
| Approach: | They propose a chart-efficient training framework to enhance counterfactual sensitivity by code modification and a similarity-based data selection strategy. |
| Outcome: | The proposed framework achieves superior or comparable performance to strong chart-specific VLMs while using significantly less training data. |