Scaling Coding Agents via Atomic Skills
Summary: arXiv:2604.05013v1 Announce Type: cross
Abstract: Current LLM coding agents are predominantly trained on composite benchmarks (e.g., bug fixing), which often leads to task-specific overfitting and limited generalization. To address this, we propose a novel scaling paradigm that shifts the focus from task-level optimization to atomic skill mastery.
Introduction
The rapid advancement of coding agents powered by large language models (LLMs) has revolutionized software development practices. However, existing models often struggle with generalization due to their training on composite tasks. These tasks, while useful, can lead to overfitting, making the agents less adaptable to unseen scenarios.
Atomic Skills: A New Approach
In our research, we identify and formalize five fundamental atomic skills crucial for effective software engineering:
- Code Localization: Identifying relevant sections of code for a given task.
- Code Editing: Modifying code efficiently to meet specified requirements.
- Unit-Test Generation: Creating tests to ensure code correctness.
- Issue Reproduction: Reproducing reported issues to facilitate debugging.
- Code Review: Analyzing code for quality and compliance with standards.
These atomic skills serve as foundational building blocks, enabling more complex software engineering tasks to be executed with enhanced efficacy.
Scaling Through Joint Reinforcement Learning
To enhance coding agents, we employ a joint reinforcement learning (RL) approach that focuses on the simultaneous improvement of these atomic skills. By doing so, we aim to eliminate negative interference that often arises when training on composite tasks. This methodology allows for consistent enhancement of each skill without compromising the others.
Generalization and Performance Improvement
Our experimental results indicate that improvements in atomic skills significantly enhance performance on various unseen composite tasks, such as:
- Bug fixing
- Code refactoring
- Machine learning engineering
- Code security
We observed an impressive average performance increase of 18.7% across five atomic skills and five composite tasks, providing strong evidence for our proposed scaling paradigm.
Conclusion
The shift from composite task optimization to atomic skill mastery represents a promising direction for the development of coding agents. By focusing on the building blocks of software engineering, we enable more robust and adaptable coding agents capable of tackling a wider array of challenges. Future work will involve exploring additional atomic skills and refining our joint RL methodology to further enhance the capabilities of these agents.
References
For further reading, please refer to the original paper: Scaling Coding Agents via Atomic Skills.