南阁4.1-3B：具备推理、对齐与行动能力的小型通用模型

摘要

我們推出南貝格4.1-3B，這是一個統一的通用語言模型，僅以30億參數就同時實現了強大的智能體行為、代碼生成與通用推理能力。據我們所知，這是首個在單一模型中實現如此多樣化能力的開源小型語言模型（SLM）。為提升推理能力與偏好對齊，我們結合點對點和配對獎勵建模技術，確保生成高質量且符合人類偏好的回應。在代碼生成方面，我們於強化學習中設計複雜度感知獎勵機制，同步優化正確性與效率。針對深度搜索任務，我們進行複雜數據合成並在訓練中引入回合級監督，使南貝格4.1-3B能穩定執行長達600次工具調用回合的複雜問題求解。大量實驗結果表明，南貝格4.1-3B顯著超越同規模模型（如Nanbeige4-3B-2511和Qwen3-4B），甚至優於參數量大十倍的Qwen3-30B-A3B模型。本研究證明小型模型可同時實現廣泛能力與深度專業化，重新定義了30億參數模型的潛力邊界。

English

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.

南阁4.1-3B：具备推理、对齐与行动能力的小型通用模型

Nanbeige4.1-3B: A Small General Model that Reasons, Aligns, and Acts

摘要

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