Papers target better training data for terminal agents
RST and CalibForge synthesize verified terminal tasks for long-horizon agent training.
Why it matters
The work addresses a key bottleneck in agent development: producing executable, verifiable tasks that are both consistent and useful for learning. It suggests terminal-agent training may rely more on automated task generation and solver-calibrated curricula than expensive manual authoring.
The key points
- 1.RST generated 37,484 verified terminal tasks across 15 recursive rounds.
- 2.CalibForge produced 5,431 tasks calibrated against solver behavior.
- 3.Both papers focus on scalable supervision for long-horizon terminal agents.
Two new papers describe systems for generating training tasks for terminal agents. Recursive Synthetic Terminal Tasks (RST) recursively expands verified seed tasks, producing 37,484 synthesized tasks across 15 rounds at roughly $0.05 per task, with later rounds becoming harder by solution length, command count and solver pass rate. CalibForge builds 5,431 calibrated terminal tasks by using verified solver behavior to revise candidates toward learnable difficulty, with reported gains on Terminal-Bench 2.0, SWE-bench Pro and Doc2Repo.
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Sources & original reporting
This brief summarizes and links to reporting from the publishers below.
- arXiv cs.AIRecursive Synthesis for Long-Horizon Terminal TasksAug 7, 12:00 PM↗
- arXiv cs.LGRecursive Synthesis for Long-Horizon Terminal TasksAug 7, 12:00 PM↗
- arXiv cs.LGCalibForge: Adversarial Solver Calibration for Scaling Learnable Terminal TasksAug 7, 12:00 PM↗
- arXiv cs.CLCalibForge: Adversarial Solver Calibration for Scaling Learnable Terminal TasksAug 7, 12:00 PM↗
- HF Daily PapersCalibForge: Adversarial Solver Calibration for Scaling Learnable Terminal TasksAug 6, 4:00 AM↗
- HF Daily PapersRecursive Synthesis for Long-Horizon Terminal TasksAug 5, 4:00 AM↗
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