@inproceedings{chen-etal-2026-beyond-quantity,
    title = "Beyond Quantity: Trajectory Diversity Scaling for Code Agents",
    author = "Chen, Guhong  and
      Sun, Chenghao  and
      Fu, Cheng  and
      Wang, Qiyao  and
      Huang, Zhihong  and
      Wei, ChaoPeng  and
      Chen, Guangxu  and
      Fang, Feiteng  and
      Argha, Ahmadreza  and
      Zhao, Bing  and
      Xu, Xander  and
      Han, Qi  and
      Alinejad-Rokny, Hamid  and
      Qu, Qiang  and
      Li, Binhua  and
      Ni, Shiwen  and
      Yang, Min  and
      Wei, HU  and
      Li, Yongbin",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.768/",
    pages = "15676--15691",
    ISBN = "979-8-89176-395-1",
    abstract = "As code large language models (LLMs) evolve into tool-interactive agents via the Model Context Protocol (MCP), their generalization is increasingly limited by low-quality synthetic data and the diminishing returns of quantity scaling; moreover, quantity-centric scaling exhibits an early bottleneck that underutilizes trajectory data. We propose TDScaling, a Trajectory Diversity Scaling-based data synthesis framework for code agents that scales performance through diversity rather than raw volume. Moreover, TDScaling is more data-efficient: under a fixed training budget, increasing trajectory diversity yields larger gains than adding more trajectories, improving the performance-cost trade-off for agent training. TDScaling integrates four innovations: (1) a Business Cluster mechanism that captures real-service logical dependencies; (2) a Blueprint-driven multi-agent paradigm that enforces trajectory coherence; (3) an adaptive evolution mechanism that steers synthesis toward long-tail scenarios using Domain Entropy, Reasoning Mode Entropy, and Cumulative Action Complexity to prevent mode collapse; and (4) a sandboxed code tool that mitigates catastrophic forgetting of intrinsic coding capabilities. Experiments on general tool-use benchmarks (BFCL, $\tau^2$-Bench) and code agent tasks (RebenchT, CodeCI, BIRD) demonstrate a win-win outcome: TDScaling improves both tool-use generalization and inherent coding proficiency. Crucially, we show that trajectory diversity scaling attains a substantially higher performance ceiling than quantity scaling, establishing a resource-efficient paradigm for training robust code agents under data bottlenecks."
}