40亿参数代码模型用合成任务在线训练，不依赖大模型蒸馏。
FrogNano: Training a 4B Coding Agent via Online Task Synthesis
- 通过在线生成任务，动态匹配当前模型学习能力边界。
- 仅用1500个环境的合成数据训练出性能接近大模型的代码代理。
- 适合资源受限场景，可部署在低算力设备上运行。
我们提出FrogNano，一个40亿参数的代码代理，能在资源受限环境下高效完成软件工程任务。该模型仅通过强化学习在约1500个合成任务的SWE环境中进行后训练。核心创新在于在线任务生成流水线，能根据当前检查点的学习能力前沿动态生成任务。实验表明，仅使用合成任务即可训练出具有竞争力的小型代码代理，无需依赖大型模型的蒸馏。本文详细介绍了训练方法、多环境评估及深入分析，为后续探索轻量但高性能的代码代理提供了基础，这些代理可在最低硬件条件下运行。
原文摘要 · Abstract (English)
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.
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