While Large Language Models (LLMs) excel at short-term tasks, scaling them to long-horizon agentic workflows remains challenging. The core bottleneck lies in the scarcity of training data that captures authentic long-dependency structures and cross-stage evolutionary dynamics--existing synthesis methods either confine to single-feature scenarios constrained by model distribution, or incur prohibitive human annotation costs, failing to provide scalable, high-quality supervision. We address this by reconceptualizing data synthesis through the lens of real-world software evolution. Our key insight: Pull Request (PR) sequences naturally embody the supervision signals for long-horizon learning. They decompose complex objectives into verifiable submission units, maintain functional coherence across iterations, and encode authentic refinement patterns through bug-fix histories. Building on this, we propose daVinci-Agency, which systematically mines structured supervision from chain-of-PRs through three interlocking mechanisms: (1) progressive task decomposition via continuous commits, (2) long-term consistency enforcement through unified functional objectives, and (3) verifiable refinement from authentic bug-fix trajectories. Unlike synthetic trajectories that treat each step independently, daVinci-Agency's PR-grounded structure inherently preserves the causal dependencies and iterative refinements essential for teaching persistent goal-directed behavior and enables natural alignment with project-level, full-cycle task modeling. The resulting trajectories are substantial--averaging 85k tokens and 116 tool calls--yet remarkably data-efficient: fine-tuning GLM-4.6 on 239 daVinci-Agency samples yields broad improvements across benchmarks, notably achieving a 47% relative gain on Toolathlon. Beyond benchmark performance, our analysis confirms...

翻译：尽管大型语言模型（LLMs）在短期任务上表现出色，但将其扩展至长程智能体工作流仍面临挑战。核心瓶颈在于缺乏能够捕捉真实长程依赖结构与跨阶段演化动态的训练数据——现有合成方法要么受限于模型分布约束而局限于单特征场景，要么需要高昂的人工标注成本，均无法提供可扩展的高质量监督。我们通过软件工程视角重构数据合成范式来解决这一问题。核心洞见在于：代码拉取请求（PR）序列天然蕴含长程学习所需的监督信号。它们将复杂目标分解为可验证的提交单元，在迭代过程中保持功能连贯性，并通过缺陷修复历史编码真实的优化模式。基于此，我们提出daVinci-Agency框架，通过三重联动机制从PR链中系统化挖掘结构化监督：（1）通过连续提交实现渐进式任务分解，（2）借助统一功能目标实施长期一致性约束，（3）基于真实缺陷修复轨迹进行可验证的迭代优化。与将各步骤独立处理的合成轨迹不同，daVinci-Agency基于PR的结构本质保留了因果依赖关系与迭代优化过程，这对于教授持续目标导向行为至关重要，并能自然契合项目级全周期任务建模。生成的轨迹规模显著——平均包含8.5万词元与116次工具调用——同时保持卓越的数据效率：使用239个daVinci-Agency样本对GLM-4.6进行微调，在多项基准测试中实现广泛性能提升，尤其在Toolathlon基准上取得47%的相对增益。除基准性能外，我们的分析进一步证实...