Nanbeige4.1-3B: A Small General Model that Reasons, Aligns, and Acts
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.
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Chen Yang
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Guangyue Peng
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Jiaying Zhu
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Ran Le
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Ruixiang Feng
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Tao Zhang
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Xiyun Xu
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Yang Song
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Yiming Jia
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2026-03-02T09:39:51.645Z
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Yiming Jia
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Yuntao Wen
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2026-02-27T16:45:13.216Z
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Yuntao Wen
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Yunzhi Xu
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Zekai Wang
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Zhenwei An
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Zhicong Sun
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Zongchao Chen
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2026-03-13T08:15:28.407Z
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ChenZongchao
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chenzongchao
Nanbeige4.1-3B: A Small General Model that Reasons, Aligns, and Acts
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.
Nanbeige4.1-3B is a 3B-parameter unified language model that demonstrates superior performance in agentic behavior, code generation, and reasoning compared to larger models through advanced reward modeling and training techniques.
unified generalist language model reward modeling reinforcement learning tool-call turns deep search complex data synthesis turn-level supervision point-wise reward modeling pair-wise reward modeling code generation general reasoning agentic behavior human-aligned responses model optimization