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| Main Authors: | , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.03219 |
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| _version_ | 1866917258970791936 |
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| author | Chen, Guhong Sun, Chenghao Fu, Cheng Wang, Qiyao Huang, Zhihong Wei, Chaopeng Chen, Guangxu Fang, Feiteng Argha, Ahmadreza Zhao, Bing Xu, Xander Han, Qi Alinejad-Rokny, Hamid Qu, Qiang Li, Binhua Ni, Shiwen Yang, Min Wei, Hu Li, Yongbin |
| author_facet | Chen, Guhong Sun, Chenghao Fu, Cheng Wang, Qiyao Huang, Zhihong Wei, Chaopeng Chen, Guangxu Fang, Feiteng Argha, Ahmadreza Zhao, Bing Xu, Xander Han, Qi Alinejad-Rokny, Hamid Qu, Qiang Li, Binhua Ni, Shiwen Yang, Min Wei, Hu Li, Yongbin |
| contents | 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. 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. We plan to release the full codebase and the synthesized dataset (including 30,000+ tool clusters) upon publication. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_03219 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Beyond Quantity: Trajectory Diversity Scaling for Code Agents Chen, Guhong Sun, Chenghao Fu, Cheng Wang, Qiyao Huang, Zhihong Wei, Chaopeng Chen, Guangxu Fang, Feiteng Argha, Ahmadreza Zhao, Bing Xu, Xander Han, Qi Alinejad-Rokny, Hamid Qu, Qiang Li, Binhua Ni, Shiwen Yang, Min Wei, Hu Li, Yongbin Artificial Intelligence 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. 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. We plan to release the full codebase and the synthesized dataset (including 30,000+ tool clusters) upon publication. |
| title | Beyond Quantity: Trajectory Diversity Scaling for Code Agents |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2602.03219 |