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For each directive, NexForge automatically retrieves or constructs the required files, dependencies, and runtime configurations, and finally synthesizes expert rollouts and produces training t","title":"NexForge: Scaling Agent Capabilities through Requirement-Driven Task Synthesis for LLMs","url":"https://arxiv.org/abs/2607.14186","vendor":"arxiv_cs_ai"},"summary":"arXiv:2607.14186v4 Announce Type: replace-cross \nAbstract: Scaling executable agent training data for LLM post-training is bottlenecked by substrate-bound methods that tie task generation to predefined tools, repositories, or skill graphs: expanding coverage requires manual substrate engineering, each new domain demands a bespoke pipeline, and the resulting task distributions often reflect substrate biases rather than real-world demand. We introduce NexForge, a requirement-driven framework that takes high-level capability requirements as input and synthesizes diverse, executable agent tasks and expert trajectories for SFT. 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