Agentic data science with the Data Agent Kit
cloud.google.com·7h ago
AI Research
arXiv CS.AI·Aug 7·5 min read
Recursive Synthesis for Long-Horizon Terminal Tasks
TL;DR
Recursive Synthetic Terminal Tasks (RST) is a framework that generates long-horizon tasks for terminal agents at scale, producing 37,484 tasks at a low cost. This method significantly improves the performance of models like Qwen3.5 through fine-tuning on synthesized tasks.
✦ Why It Matters
Implement RST to efficiently generate training data for terminal agents and enhance model performance today.
Key Takeaways
- Utilize RST for scalable task generation in AI training.
- Expect increased task complexity with each synthesis round.
- Fine-tune models on synthesized tasks for better performance.
- Monitor synthesis yield and validation rates for ongoing improvements.
How It Works
RST begins with verified seed tasks and recursively extends them by realigning the reference solution, verifier, and instructions. Each iteration validates the new tasks in a sandbox environment, ensuring consistency and quality.
This recursive approach allows for the generation of a large number of tasks while maintaining the necessary dependencies.