# Focused-DPO: Enhancing Code Generation Through Focused Preference Optimization on Error-Prone Points

arXiv: https://arxiv.org/abs/2502.11475 · HTML: https://mlsanity.com/paper/2502.11475/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning.
- Builds on 4 of its 20 references that are in the map.
- First posted on arXiv 17 Feb 2025.

## Placed in

- LLM reasoning / [Reasoning for agents, planning and code](https://mlsanity.com/lr/reasoning-for-agents-planning-and-code/)

## Builds on (most cited first, 4 of 4)

- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code](https://mlsanity.com/paper/2403.07974/) (2024)
- [Step-DPO: Step-wise Preference Optimization for Long-chain Reasoning of LLMs](https://mlsanity.com/paper/2406.18629/) (2024)
- [Critical Tokens Matter: Token-Level Contrastive Estimation Enhances LLM's Reasoning Capability](https://mlsanity.com/paper/2411.19943/) (2024)

## Built on by (most cited first, 14 of 14)

- [Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models](https://mlsanity.com/paper/2503.09567/) (2025)
- [A Survey on Test-Time Scaling in Large Language Models: What, How, Where, and How Well?](https://mlsanity.com/paper/2503.24235/) (2025)
- [Afterburner: Reinforcement Learning Facilitates Self-Improving Code Efficiency Optimization](https://mlsanity.com/paper/2505.23387/) (2025)
- [Exploring the Potential of Diffusion Large Language Models in Code Generation](https://mlsanity.com/paper/2509.11252/) (2025)
- [From Code Foundation Models to Agents and Applications: A Comprehensive Survey and Practical Guide to Code Intelligence](https://mlsanity.com/paper/2511.18538/) (2025)
- [Alignment with Fill-In-the-Middle for Enhancing Code Generation](https://mlsanity.com/paper/2508.19532/) (2025)
- [MARTI-MARS2: Scaling Multi-Agent Self-Search via Reinforcement Learning for Code Generation](https://mlsanity.com/paper/2602.07848/) (2026)
- [Computational Thinking Reasoning in Large Language Models](https://mlsanity.com/paper/2506.02658/) (2025)
- [Learning to Align Human Code Preferences](https://mlsanity.com/paper/2507.20109/) (2025)
- [SemPA: Improving Sentence Embeddings of Large Language Models through Semantic Preference Alignment](https://mlsanity.com/paper/2601.05075/) (2026)
- [Self-Improving Code Generation via Semantic Entropy and Behavioral Consensus](https://mlsanity.com/paper/2603.29292/) (2026)
- [Decomposing the Delta: What Do Models Actually Learn from Preference Pairs?](https://mlsanity.com/paper/2604.08723/) (2026)
- [SignDPO: Multi-level Direct Preference Optimisation for Skeleton-based Gloss-free Sign Language Translation](https://mlsanity.com/paper/2604.18034/) (2026)
- [Function-Level Execution Feedback for Code Preference Optimization](https://mlsanity.com/paper/2608.23632/) (2026)

## Abstract (arXiv)

Code generation models have shown significant potential for automating programming tasks. However, the challenge of generating accurate and reliable code persists due to the highly complex and long-reasoning nature of the task. Even state-of-the-art models often fail in code generation due to small errors, which can drastically affect the overall functionality of code. Our study identifies that current models tend to produce errors concentrated at specific error-prone points, which significantly impacts the accuracy of the generated code. To address this issue, we introduce Focused-DPO, a framework that enhances code generation by directing preference optimization towards these critical error-prone areas. This approach builds on Direct Preference Optimization, emphasizing accuracy in parts prone to errors. Additionally, we develop a method called Error-Point Identification, which constructs a dataset that targets these problematic points without requiring costly human annotations. Our experiments on benchmarks such as HumanEval(+), MBPP(+), and LiveCodeBench demonstrate that Focused-DPO significantly improves the precision and reliability of code generation, reducing common errors and enhancing overall code quality. By focusing on error-prone points, Focused-DPO advances the accuracy and functionality of model-generated code.