AM-Thinking-v1: Advancing the Frontier of Reasoning at 32B Scale
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}.
682404584254a325ece48c4e
Yunjie Ji
682404584254a325ece48c4f
Xiaoyu Tian
claimed_verified
2025-05-21T09:03:53.821Z
621499d72be42a56cca7afad
/avatars/3cea70d1a55c8096b4270093c69e4a5e.svg
TianXiaoyu
user
Emperorizzis
682404584254a325ece48c50
Sitong Zhao
682404584254a325ece48c51
Haotian Wang
682404584254a325ece48c52
Shuaiting Chen
682404584254a325ece48c53
Yiping Peng
682404584254a325ece48c54
Han Zhao
682404584254a325ece48c55
Xiangang Li
AM-Thinking-v1: Advancing the Frontier of Reasoning at 32B Scale
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, 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.
dense language model Mixture-of-Experts Qwen3-235B-A22B Seed1.5-Thinking AIME LiveCodeBench post-training pipeline supervised fine-tuning reinforcement learning