Nanbeige4.1-3B: A Small General Model that Reasons, Aligns, and Acts
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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866918337760460800 |
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| author | Yang, Chen Peng, Guangyue Zhu, Jiaying Le, Ran Feng, Ruixiang Zhang, Tao Xu, Xiyun Song, Yang Jia, Yiming Wen, Yuntao Xu, Yunzhi Wang, Zekai An, Zhenwei Sun, Zhicong Chen, Zongchao |
| author_facet | Yang, Chen Peng, Guangyue Zhu, Jiaying Le, Ran Feng, Ruixiang Zhang, Tao Xu, Xiyun Song, Yang Jia, Yiming Wen, Yuntao Xu, Yunzhi Wang, Zekai An, Zhenwei Sun, Zhicong Chen, Zongchao |
| contents | We present Nanbeige4.1-3B, a unified generalist language model that simultaneously achieves strong agentic behavior, code generation, and general reasoning with only 3B parameters. To the best of our knowledge, it is the first open-source small language model (SLM) to achieve such versatility in a single model. To improve reasoning and preference alignment, we combine point-wise and pair-wise reward modeling, ensuring high-quality, human-aligned responses. For code generation, we design complexity-aware rewards in Reinforcement Learning, optimizing both correctness and efficiency. In deep search, we perform complex data synthesis and incorporate turn-level supervision during training. This enables stable long-horizon tool interactions, allowing Nanbeige4.1-3B to reliably execute up to 600 tool-call turns for complex problem-solving. Extensive experimental results show that Nanbeige4.1-3B significantly outperforms prior models of similar scale, such as Nanbeige4-3B-2511 and Qwen3-4B, even achieving superior performance compared to much larger models, such as Qwen3-30B-A3B. Our results demonstrate that small models can achieve both broad competence and strong specialization simultaneously, redefining the potential of 3B parameter models. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2602_13367 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Nanbeige4.1-3B: A Small General Model that Reasons, Aligns, and Acts Yang, Chen Peng, Guangyue Zhu, Jiaying Le, Ran Feng, Ruixiang Zhang, Tao Xu, Xiyun Song, Yang Jia, Yiming Wen, Yuntao Xu, Yunzhi Wang, Zekai An, Zhenwei Sun, Zhicong Chen, Zongchao Artificial Intelligence Computation and Language We present Nanbeige4.1-3B, a unified generalist language model that simultaneously achieves strong agentic behavior, code generation, and general reasoning with only 3B parameters. To the best of our knowledge, it is the first open-source small language model (SLM) to achieve such versatility in a single model. To improve reasoning and preference alignment, we combine point-wise and pair-wise reward modeling, ensuring high-quality, human-aligned responses. For code generation, we design complexity-aware rewards in Reinforcement Learning, optimizing both correctness and efficiency. In deep search, we perform complex data synthesis and incorporate turn-level supervision during training. This enables stable long-horizon tool interactions, allowing Nanbeige4.1-3B to reliably execute up to 600 tool-call turns for complex problem-solving. Extensive experimental results show that Nanbeige4.1-3B significantly outperforms prior models of similar scale, such as Nanbeige4-3B-2511 and Qwen3-4B, even achieving superior performance compared to much larger models, such as Qwen3-30B-A3B. Our results demonstrate that small models can achieve both broad competence and strong specialization simultaneously, redefining the potential of 3B parameter models. |
| title | Nanbeige4.1-3B: A Small General Model that Reasons, Aligns, and Acts |
| topic | Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2602.13367 |