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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/2604.00824 |
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| _version_ | 1866917385335734272 |
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| author | CodeArts Model Team Ye, Yang Tan, Jingyuan Jiang, Tianyue Ye, Ruizhe He, Qiankun Yang, Jiarui Dong, Jian Liang, Sicong Yue, Chongjian Xu, Peibai Lu, Lufan Pang, Shiguan Qian, Taotao Hu, Junbao Hao, Yuechan Shi, Ensheng Zhang, Qi Hao, Yi Fan, Na Tan, Xin Yao, Shuai Shen, Zhiwei Li, Zongchen Wang, Yanlin Chen, Chong Ma, Yuchi |
| author_facet | CodeArts Model Team Ye, Yang Tan, Jingyuan Jiang, Tianyue Ye, Ruizhe He, Qiankun Yang, Jiarui Dong, Jian Liang, Sicong Yue, Chongjian Xu, Peibai Lu, Lufan Pang, Shiguan Qian, Taotao Hu, Junbao Hao, Yuechan Shi, Ensheng Zhang, Qi Hao, Yi Fan, Na Tan, Xin Yao, Shuai Shen, Zhiwei Li, Zongchen Wang, Yanlin Chen, Chong Ma, Yuchi |
| contents | Training effective software engineering agents requires large volumes of task-specific trajectories, incurring substantial data construction costs. Inspired by the "Less-Is-More" hypothesis in mathematical reasoning, we investigate its extension to agentic scenarios and propose an end-to-end training framework that achieves superior agentic capabilities with fewer but higher-quality training trajectories. This is achieved via STITCH (Sliding-memory Trajectory Inference and Task Chunking Heuristic), a coarse-to-fine mechanism that filters low-value noise and retains decision-critical tokens to maximize training signal quality. We conduct experiments across multiple agent frameworks (e.g., mini-SWE-agent, MSWE-agent), model scales (30B to 355B), and multilingual settings (Python, Java, and ArkTS). On SWE-bench Verified, models trained with STITCH achieve up to 63.16% relative improvement over base models. On Multi-SWE-bench (Java), MiniMax-M2.5-STITCH achieves 43.75% with our CodeArts Agent scaffold (+16.67%). On HarmonyOS (ArkTS), GLM-4.7-STITCH improves the compilation pass rate to 61.31% (+43.34%) with less than 1K training trajectories. Our results confirm that the "Less-Is-More" paradigm generalizes effectively to complex agentic tasks across diverse languages and model scales. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_00824 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Yet Even Less Is Even Better For Agentic, Reasoning, and Coding LLMs CodeArts Model Team Ye, Yang Tan, Jingyuan Jiang, Tianyue Ye, Ruizhe He, Qiankun Yang, Jiarui Dong, Jian Liang, Sicong Yue, Chongjian Xu, Peibai Lu, Lufan Pang, Shiguan Qian, Taotao Hu, Junbao Hao, Yuechan Shi, Ensheng Zhang, Qi Hao, Yi Fan, Na Tan, Xin Yao, Shuai Shen, Zhiwei Li, Zongchen Wang, Yanlin Chen, Chong Ma, Yuchi Software Engineering Training effective software engineering agents requires large volumes of task-specific trajectories, incurring substantial data construction costs. Inspired by the "Less-Is-More" hypothesis in mathematical reasoning, we investigate its extension to agentic scenarios and propose an end-to-end training framework that achieves superior agentic capabilities with fewer but higher-quality training trajectories. This is achieved via STITCH (Sliding-memory Trajectory Inference and Task Chunking Heuristic), a coarse-to-fine mechanism that filters low-value noise and retains decision-critical tokens to maximize training signal quality. We conduct experiments across multiple agent frameworks (e.g., mini-SWE-agent, MSWE-agent), model scales (30B to 355B), and multilingual settings (Python, Java, and ArkTS). On SWE-bench Verified, models trained with STITCH achieve up to 63.16% relative improvement over base models. On Multi-SWE-bench (Java), MiniMax-M2.5-STITCH achieves 43.75% with our CodeArts Agent scaffold (+16.67%). On HarmonyOS (ArkTS), GLM-4.7-STITCH improves the compilation pass rate to 61.31% (+43.34%) with less than 1K training trajectories. Our results confirm that the "Less-Is-More" paradigm generalizes effectively to complex agentic tasks across diverse languages and model scales. |
| title | Yet Even Less Is Even Better For Agentic, Reasoning, and Coding LLMs |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2604.00824 |