# GA4GC: Greener Agent for Greener Code via Multi-Objective Configuration Optimization

arXiv: https://arxiv.org/abs/2510.04135 · HTML: https://mlsanity.com/paper/2510.04135/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 9 of its 14 references that are in the map.
- First posted on arXiv 5 Oct 2025.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 9 of 9)

- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Small Language Models are the Future of Agentic AI](https://mlsanity.com/paper/2506.02153/) (2025)
- [SWE-Perf: Can Language Models Optimize Code Performance on Real-World Repositories?](https://mlsanity.com/paper/2507.12415/) (2025)
- [Language Models for Code Optimization: Survey, Challenges and Future Directions](https://mlsanity.com/paper/2501.01277/) (2025)
- [Tuning LLM-based Code Optimization via Meta-Prompting: An Industrial Perspective](https://mlsanity.com/paper/2508.01443/) (2025)
- [Generating Energy-Efficient Code via Large-Language Models -- Where are we now?](https://mlsanity.com/paper/2509.10099/) (2025)
- [GREEN-CODE: Learning to Optimize Energy Efficiency in LLM-based Code Generation](https://mlsanity.com/paper/2501.11006/) (2025)
- [Investigating the Role of LLMs Hyperparameter Tuning and Prompt Engineering to Support Domain Modeling](https://mlsanity.com/paper/2507.14735/) (2025)
- [Industrial LLM-based Code Optimization under Regulation: A Mixture-of-Agents Approach](https://mlsanity.com/paper/2508.03329/) (2025)

## Built on by (most cited first, 3 of 3)

- [Comparing AI Coding Agents: A Task-Stratified Analysis of Pull Request Acceptance](https://mlsanity.com/paper/2602.08915/) (2026)
- [How Do Practitioners Build SE Agents? Insights from a Mixed-Methods Study](https://mlsanity.com/paper/2607.10856/) (2026)
- [COMPAS: Difficulty-Aware Joint Search for Optimizing Code Generation](https://mlsanity.com/paper/2608.04336/) (2026)

## Abstract (arXiv)

Coding agents powered by LLMs face critical sustainability and scalability challenges in industrial deployment, with single runs consuming over 100k tokens and incurring environmental costs that may exceed optimization benefits. This paper introduces GA4GC, the first framework to systematically optimize coding agent runtime (greener agent) and code performance (greener code) trade-offs by discovering Pareto-optimal agent hyperparameters and prompt templates. Evaluation on the SWE-Perf benchmark demonstrates up to 135x hypervolume improvement, reducing agent runtime by 37.7% while improving correctness. Our findings establish temperature as the most critical hyperparameter, and provide actionable strategies to balance agent sustainability with code optimization effectiveness in industrial deployment.