# Language Models for Code Optimization: Survey, Challenges and Future Directions

arXiv: https://arxiv.org/abs/2501.01277 · HTML: https://mlsanity.com/paper/2501.01277/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jan 2025.

## Built on by (most cited first, 15 of 15)

- [FeatureBench: Benchmarking Agentic Coding for Complex Feature Development](https://mlsanity.com/paper/2602.10975/) (2026)
- [AI Agentic Programming: A Survey of Techniques, Challenges, and Opportunities](https://mlsanity.com/paper/2508.11126/) (2025)
- [A Survey of Inductive Reasoning for Large Language Models](https://mlsanity.com/paper/2510.10182/) (2025)
- [Tuning LLM-based Code Optimization via Meta-Prompting: An Industrial Perspective](https://mlsanity.com/paper/2508.01443/) (2025)
- [Evolving Excellence: Automated Optimization of LLM-based Agents](https://mlsanity.com/paper/2512.09108/) (2025)
- [How Do Agents Perform Code Optimization? An Empirical Study](https://mlsanity.com/paper/2512.21757/) (2025)
- [GA4GC: Greener Agent for Greener Code via Multi-Objective Configuration Optimization](https://mlsanity.com/paper/2510.04135/) (2025)
- [CktEvo: Repository-Level RTL Code Benchmark for Design Evolution](https://mlsanity.com/paper/2603.08718/) (2026)
- [LLM-Based Multi-Agent Systems for Code Generation: A Multi-Vocal Literature Review](https://mlsanity.com/paper/2604.16321/) (2026)
- [Beyond Local Code Optimization: Multi-Agent Reasoning for Software System Optimization](https://mlsanity.com/paper/2603.14703/) (2026)
- [The State of Generative AI in Software Development: Insights from Literature and a Developer Survey](https://mlsanity.com/paper/2603.16975/) (2026)
- [Rethinking LLM-Based RTL Code Optimization Via Timing Logic Metamorphosis](https://mlsanity.com/paper/2507.16808/) (2025)
- [Integrating Performance Tools in Model Reasoning for GPU Kernel Optimization](https://mlsanity.com/paper/2510.17158/) (2025)
- [LOOPRAG: Enhancing Loop Transformation Optimization with Retrieval-Augmented Large Language Models](https://mlsanity.com/paper/2512.15766/) (2025)
- [Lean Refactor: Multi-Objective Controllable Proof Optimization via Agentic Strategy Search](https://mlsanity.com/paper/2605.20244/) (2026)

## Abstract (arXiv)

Language models (LMs) built upon deep neural networks (DNNs) have recently demonstrated breakthrough effectiveness in software engineering tasks such as code generation, completion, and repair. This has paved the way for the emergence of LM-based code optimization techniques, which are crucial for enhancing the performance of existing programs, such as accelerating program execution time. However, a comprehensive survey dedicated to this specific application has been lacking. To fill this gap, we present a systematic literature review of over 50 primary studies, identifying emerging trends and addressing 11 specialized questions. Our findings reveal five critical open challenges, such as balancing model complexity with practical usability, cross-language/performance generalizability, and building trust in AI-driven solutions. Furthermore, we provide eight future research directions to facilitate more efficient, robust, and reliable LM-based code optimization. Thereby, this study aims to provide actionable insights and foundational references for both researchers and practitioners in this rapidly evolving field.