MiroMind-M1: An Open-Source Advancement in Mathematical Reasoning via
  Context-Aware Multi-Stage Policy Optimization
Authors: name: Xingxuan Li; name: Yao Xiao; name: Dianwen Ng; name: Hai Ye; name: Yue Deng; name: Xiang Lin; name: Bin Wang; name: Zhanfeng Mo; name: Chong Zhang; name: Yueyi Zhang; name: Zonglin Yang; name: Ruilin Li; name: Lei Lei; name: Shihao Xu; name: Han Zhao; name: Weiling Chen; name: Feng Ji; name: Lidong Bing
Published: 2025-07-19 16:21:23

Abstract: Large language models have recently evolved from fluent text generation to
advanced reasoning across diverse domains, giving rise to reasoning language
models. Among these domains, mathematical reasoning serves as a representative
benchmark as it requires precise multi-step logic and abstract reasoning, which
can be generalized to other tasks. While closed-source RLMs such as GPT-o3
demonstrate impressive reasoning capabilities, their proprietary nature limits
transparency and reproducibility. Although many open-source projects aim to
close this gap, most of them lack sufficient openness by omitting critical
resources such as datasets and detailed training configurations, which hinders
reproducibility. To contribute toward greater transparency in RLM development,
we introduce the MiroMind-M1 series, a set of fully open-source RLMs built on
the Qwen-2.5 backbone that match or exceed the performance of existing
open-source RLMs. Specifically, our models are trained in two stages: SFT on a
carefully curated corpus of 719K math-reasoning problems with verified CoT
trajectories, followed by RLVR on 62K challenging and verifiable problems. To
enhance the robustness and efficiency of the RLVR process, we introduce
Context-Aware Multi-Stage Policy Optimization, an algorithm that integrates
length-progressive training with an adaptive repetition penalty to encourage
context-aware RL training. Our model achieves state-of-the-art or competitive
performance and superior token efficiency among Qwen-2.5-based open-source 7B
and 32B models on the AIME24, AIME25, and MATH benchmarks. To facilitate
reproducibility, we release the complete stack: models (MiroMind-M1-SFT-7B,
MiroMind-M1-RL-7B, MiroMind-M1-RL-32B); datasets (MiroMind-M1-SFT-719K,
MiroMind-M1-RL-62K); and all training and evaluation configurations. We hope
these resources will support further research and foster community advancement.