Researchers have developed Recursive Synthetic Terminal Tasks (RST), a framework designed to generate long-horizon training data for terminal agents at a significantly reduced cost. This method recursively synthesizes new tasks from verified seeds, extending solutions, realigning verifiers, and validating in sandboxes, ultimately producing over 37,000 tasks for approximately $0.05 each. The synthesized tasks increase substantially in difficulty over recursive rounds, leading to performance drops in models like DeepSeek-V4-Pro. Fine-tuning with data generated by RST has shown notable improvements for Qwen3.5 models on various benchmarks, demonstrating the framework's utility for enhancing agent capabilities. AI
IMPACT This framework could significantly reduce the cost of creating training data for AI agents, potentially accelerating development and deployment.
RANK_REASON The cluster describes a new research paper detailing a novel framework for generating AI training data.
Read on Hugging Face Daily Papers →
- DeepSeek V4-Pro
- Long-Horizon Terminal Bench
- Qwen 3.5
- Qwen3.5 122B A10B
- Qwen3.5-27B
- Recursive Synthetic Terminal Tasks
- Terminal-Bench Hard
- arXiv
- Hugging Face
- Terminal-Bench
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