# EffiLearner: Enhancing Efficiency of Generated Code via Self-Optimization

arXiv: https://arxiv.org/abs/2405.15189 · HTML: https://mlsanity.com/paper/2405.15189/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 48 of its 73 references that are in the map.
- First posted on arXiv 24 May 2024.

## Builds on (most cited first, 30 of 48)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Reflexion: Language Agents with Verbal Reinforcement Learning](https://mlsanity.com/paper/2303.11366/) (2023)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [Program Synthesis with Large Language Models](https://mlsanity.com/paper/2108.07732/) (2021)
- [Self-Refine: Iterative Refinement with Self-Feedback](https://mlsanity.com/paper/2303.17651/) (2023)
- [Self-Instruct: Aligning Language Models with Self-Generated Instructions](https://mlsanity.com/paper/2212.10560/) (2022)
- [Code Llama: Open Foundation Models for Code](https://mlsanity.com/paper/2308.12950/) (2023)
- [Competition-Level Code Generation with AlphaCode](https://mlsanity.com/paper/2203.07814/) (2022)
- [Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation](https://mlsanity.com/paper/2305.01210/) (2023)
- [DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence](https://mlsanity.com/paper/2401.14196/) (2024)
- [WizardLM: Empowering large pre-trained language models to follow complex instructions](https://mlsanity.com/paper/2304.12244/) (2023)
- [Teaching Large Language Models to Self-Debug](https://mlsanity.com/paper/2304.05128/) (2023)
- [CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation](https://mlsanity.com/paper/2109.00859/) (2021)
- [CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis](https://mlsanity.com/paper/2203.13474/) (2022)
- [CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing](https://mlsanity.com/paper/2305.11738/) (2023)
- [WizardCoder: Empowering Code Large Language Models with Evol-Instruct](https://mlsanity.com/paper/2306.08568/) (2023)
- [Textbooks Are All You Need](https://mlsanity.com/paper/2306.11644/) (2023)
- [Textbooks Are All You Need II: phi-1.5 technical report](https://mlsanity.com/paper/2309.05463/) (2023)
- [Improving alignment of dialogue agents via targeted human judgements](https://mlsanity.com/paper/2209.14375/) (2022)
- [InCoder: A Generative Model for Code Infilling and Synthesis](https://mlsanity.com/paper/2204.05999/) (2022)
- [Unsupervised Translation of Programming Languages](https://mlsanity.com/paper/2006.03511/) (2020)
- [Magicoder: Empowering Code Generation with OSS-Instruct](https://mlsanity.com/paper/2312.02120/) (2023)
- [OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement](https://mlsanity.com/paper/2402.14658/) (2024)
- [OctoPack: Instruction Tuning Code Large Language Models](https://mlsanity.com/paper/2308.07124/) (2023)
- [Large Language Models for Software Engineering: Survey and Open Problems](https://mlsanity.com/paper/2310.03533/) (2023)
- [Few-shot training LLMs for project-specific code-summarization](https://mlsanity.com/paper/2207.04237/) (2022)

## Built on by (most cited first, 30 of 30)

- [From System 1 to System 2: A Survey of Reasoning Large Language Models](https://mlsanity.com/paper/2502.17419/) (2025)
- [MR-Ben: A Meta-Reasoning Benchmark for Evaluating System-2 Thinking in LLMs](https://mlsanity.com/paper/2406.13975/) (2024)
- [AI Agentic Programming: A Survey of Techniques, Challenges, and Opportunities](https://mlsanity.com/paper/2508.11126/) (2025)
- [EffiBench-X: A Multi-Language Benchmark for Measuring Efficiency of LLM-Generated Code](https://mlsanity.com/paper/2505.13004/) (2025)
- [Afterburner: Reinforcement Learning Facilitates Self-Improving Code Efficiency Optimization](https://mlsanity.com/paper/2505.23387/) (2025)
- [Controlled Self-Evolution for Algorithmic Code Optimization](https://mlsanity.com/paper/2601.07348/) (2026)
- [Measuring the Influence of Incorrect Code on Test Generation](https://mlsanity.com/paper/2409.09464/) (2024)
- [LLM4EFFI: Leveraging Large Language Models to Enhance Code Efficiency and Correctness](https://mlsanity.com/paper/2502.18489/) (2025)
- [ProofWright: Towards Agentic Formal Verification of CUDA](https://mlsanity.com/paper/2511.12294/) (2025)
- [EffiSkill: Agent Skill Based Automated Code Efficiency Optimization](https://mlsanity.com/paper/2603.27850/) (2026)
- [Learning Game-Playing Agents with Generative Code Optimization](https://mlsanity.com/paper/2508.19506/) (2025)
- [Scaling Coding Agents via Atomic Skills](https://mlsanity.com/paper/2604.05013/) (2026)
- [Tuning LLM-based Code Optimization via Meta-Prompting: An Industrial Perspective](https://mlsanity.com/paper/2508.01443/) (2025)
- [SuperCoder: Assembly Program Superoptimization with Large Language Models](https://mlsanity.com/paper/2505.11480/) (2025)
- [CodeScaler: Scaling Code LLM Training and Test-Time Inference via Reward Models](https://mlsanity.com/paper/2602.17684/) (2026)
- [Analyzing Message-Code Inconsistency in AI Coding Agent-Authored Pull Requests](https://mlsanity.com/paper/2601.04886/) (2026)
- [MaxCode: A Max-Reward Reinforcement Learning Framework for Automated Code Optimization](https://mlsanity.com/paper/2601.05475/) (2026)
- [PerfCoder: Large Language Models for Interpretable Code Performance Optimization](https://mlsanity.com/paper/2512.14018/) (2025)
- [Industrial LLM-based Code Optimization under Regulation: A Mixture-of-Agents Approach](https://mlsanity.com/paper/2508.03329/) (2025)
- [MOSAIC: Modular Orchestration for Structured Agentic Intelligence and Composition](https://mlsanity.com/paper/2606.00708/) (2026)
- [SwiftSolve: A Self-Iterative, Complexity-Aware Multi-Agent Framework for Competitive Programming](https://mlsanity.com/paper/2510.22626/) (2025)
- [The New Compiler Stack: A Survey on the Synergy of LLMs and Compilers](https://mlsanity.com/paper/2601.02045/) (2026)
- [EffiPair: Improving the Efficiency of LLM-generated Code with Differential Execution Feedback](https://mlsanity.com/paper/2604.05137/) (2026)
- [STAB: Specification-driven Testing for Algorithmic Bottlenecks](https://mlsanity.com/paper/2605.27981/) (2026)
- [Rethinking Code Performance Benchmarks for LLMs](https://mlsanity.com/paper/2607.07619/) (2026)
- [SynH-Rank: Quality-Aware Code Search via Diverse Data Synthesis and Hierarchical Ranking Training](https://mlsanity.com/paper/2607.17139/) (2026)
- [Optimal Stopping of Self-Refining Foundation Models](https://mlsanity.com/paper/2608.10729/) (2026)
- [RESKILL: Explicit Failure Attribution and Structured Repair for Interactive Language Agents](https://mlsanity.com/paper/2609.15684/) (2026)
- [Improving the Energy-Efficiency of the Code Generated by LLMs through Effective Prompting](https://mlsanity.com/paper/2610.02571/) (2026)
- [Not All Correct Fixes Run Alike: An Empirical Study of Patch Performance in Repository-Level Issue Resolution](https://mlsanity.com/paper/2610.13563/) (2026)

## Abstract (arXiv)

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 the EffiBench, HumanEval, and MBPP 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% the 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% of total memory consumption during the execution process. The source code of EffiLearner was released in https://github.com/huangd1999/EffiLearner