FrogNano: Training a 4B Coding Agent via Online Task Synthesis

2026-09-07

AI Summary

FrogNano通过在线任务合成方法，在资源受限环境下有效解决软件工程任务，仅使用合成任务训练4B编码代理。

Abstract

We present FrogNano, a 4B coding agent designed to tackle software engineering (SWE) tasks efficiently and effectively, even under resource-constrained environments. It is post-trained exclusively via RL on around 1,500 SWE environments with synthetic tasks. A key ingredient for improving performance is an online task synthesis pipeline that creates tasks calibrated to the frontier of learnability for the current checkpoint. This report provides evidence that competitive small coding agents can be trained with synthetic tasks alone, without traditional distillation from larger models, and that generating tasks at the learnability frontier of the current agent is important. We report details on the training methodology, evaluations across diverse environments, and in-depth analyses, serving as a foundation for our ongoing exploration of lightweight yet capable coding agents that can run on minimal hardware.

Problem

• coding agent
• resource-constrained environments
• online task synthesis
• software engineering tasks

Innovation

• Online Task Synthesis
• Learnability Frontier
• Reinforcement Learning
• Synthetic Tasks