Rohan Paul
@rohanpaul_ai
A 4B coding agent reached 61.5% on SWE-bench Verified without frontier-model distillation by combining a simpler tool interface with synthetic tasks that keep adapting to what the model can currently learn.
FrogNano starts from Qwen3.5-4B and is trained with RL on about 1,500 synthetic software-engineering tasks.
The important part is how those tasks are chosen.
As the model improves, the system generates fresh problems that are challenging but still learnable, so the curriculum improves with the agent.
The interface matters just as much.
Switching to a simpler 5-tool setup moved the base model from 8.3% to 37.2% on SWE-bench Verified.
After 5 rounds, FrogNano reached 61.5%.
– arxiv. org/abs/2609.07925
Title: "FrogNano: Training a 4B Coding Agent via Online Task Synthesis"
