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Nanbeige4.1-3B represents a significant advancement in the field of artificial intelligence by delivering robust performance in reasoning, coding, and agentic behaviors within a compact 3-billion parameter architecture. To achieve this versatility, the researchers employed a sophisticated post-training pipeline that enhances general capabilities through a sequential application of point-wise and pair-wise reinforcement learning, ensuring that the model's responses are both high-quality and aligned with human preferences. The development process also featured specialized training for complex domains, including a two-stage coding optimization strategy that rewards both functional correctness and algorithmic efficiency, as well as a deep search training regimen utilizing synthetic data to enable long-horizon problem-solving over hundreds of steps. Consequently, empirical evaluations demonstrate that Nanbeige4.1-3B not only surpasses other open-source models of similar size but also frequently outperforms much larger models in demanding tasks such as mathematics, competitive programming, and multi-step tool use. https://arxiv.org/pdf/2602.13367 https://huggingface.co/Nanbeige/Nanbe...