AM-Thinking-v1：在320亿规模上推进推理前沿

摘要

我们推出AM-Thinking-v1，这是一款32B密集语言模型，它推动了推理技术的前沿，体现了开源创新的协作精神。AM-Thinking-v1在性能上超越了DeepSeek-R1，并与领先的专家混合模型（MoE）如Qwen3-235B-A22B和Seed1.5-Thinking相媲美，在AIME 2024上取得了85.3分，在AIME 2025上取得了74.4分，在LiveCodeBench上取得了70.3分，展示了在相似规模的开源模型中顶尖的数学和编程能力。 AM-Thinking-v1完全基于开源Qwen2.5-32B基础模型和公开可用的查询构建，通过精心设计的后训练流程——结合了监督微调和强化学习——提供了卓越的推理能力。这项工作表明，开源社区能够在32B规模上实现高性能，这是一个适合部署和微调的实用甜点。通过在顶级性能和实际可用性之间取得平衡，我们希望AM-Thinking-v1能激发更多合作努力，利用中等规模模型，推动推理边界，同时将可访问性置于创新的核心。我们已在Hugging Face平台上开源了我们的模型，网址为https://huggingface.co/a-m-team/AM-Thinking-v1。

English

We present AM-Thinking-v1, a 32B dense language model that advances the frontier of reasoning, embodying the collaborative spirit of open-source innovation. Outperforming DeepSeek-R1 and rivaling leading Mixture-of-Experts (MoE) models like Qwen3-235B-A22B and Seed1.5-Thinking, AM-Thinking-v1 achieves impressive scores of 85.3 on AIME 2024, 74.4 on AIME 2025, and 70.3 on LiveCodeBench, showcasing state-of-the-art mathematical and coding capabilities among open-source models of similar scale. Built entirely from the open-source Qwen2.5-32B base model and publicly available queries, AM-Thinking-v1 leverages a meticulously crafted post-training pipeline - combining supervised fine-tuning and reinforcement learning - to deliver exceptional reasoning capabilities. This work demonstrates that the open-source community can achieve high performance at the 32B scale, a practical sweet spot for deployment and fine-tuning. By striking a balance between top-tier performance and real-world usability, we hope AM-Thinking-v1 inspires further collaborative efforts to harness mid-scale models, pushing reasoning boundaries while keeping accessibility at the core of innovation. We have open-sourced our model on https://huggingface.co/a-m-team/AM-Thinking-v1{Hugging Face}.

AM-Thinking-v1：在320亿规模上推进推理前沿

AM-Thinking-v1: Advancing the Frontier of Reasoning at 32B Scale

摘要

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