Recursive Synthesis for Long-Horizon Terminal Tasks
High-quality long-horizon training data for terminal agents is expensive to produce, often costing hundreds to thousands of dollars per task, because each task must keep the instruction, environment, reference solution, and verifier mutually consistent. Human authoring does not scale, and direct generation with large language models (LLMs) often breaks these dependencies. We present Recursive Synthetic Terminal Tasks (RST), a recursive verified synthesis framework for constructing long-horizon t
Record details
Published: 4 August 2026
Source: HuggingFace Daily Papers
Category: Research
Topics: Agents & autonomy · Environment
Retrieved: 7 August 2026
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ethics.ai (4 August 2026), “Recursive Synthesis for Long-Horizon Terminal Tasks,” evidence record 17039, https://ethics.ai/record/17039 (originally published by HuggingFace Daily Papers).
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