title: AM-Thinking-v1: Advancing the Frontier of Reasoning at 32B Scale
summary: 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}.
paper_authors._id: 682404584254a325ece48c4e
paper_authors.name: Yunjie Ji
paper_authors._id: 682404584254a325ece48c4f
paper_authors.name: Xiaoyu Tian
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-21T09:03:53.821Z
paper_authors._id: 621499d72be42a56cca7afad
paper_authors.avatarUrl: /avatars/3cea70d1a55c8096b4270093c69e4a5e.svg
paper_authors.fullname: TianXiaoyu
paper_authors.type: user
paper_authors.user: Emperorizzis
paper_authors._id: 682404584254a325ece48c50
paper_authors.name: Sitong Zhao
paper_authors._id: 682404584254a325ece48c51
paper_authors.name: Haotian Wang
paper_authors._id: 682404584254a325ece48c52
paper_authors.name: Shuaiting Chen
paper_authors._id: 682404584254a325ece48c53
paper_authors.name: Yiping Peng
paper_authors._id: 682404584254a325ece48c54
paper_authors.name: Han Zhao
paper_authors._id: 682404584254a325ece48c55
paper_authors.name: Xiangang Li
paper_title: AM-Thinking-v1: Advancing the Frontier of Reasoning at 32B Scale
paper_summary: 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}.
paper_ai_summary: AM-Thinking-v1, a 32B dense language model, achieves state-of-the-art performance in mathematical and coding tasks by leveraging supervised fine-tuning and reinforcement learning, demonstrating the capabilities of mid-scale open-source models.
paper_ai_keywords: dense language model Mixture-of-Experts Qwen3-235B-A22B Seed1.5-Thinking AIME LiveCodeBench post-training pipeline supervised fine-tuning reinforcement learning