The MiniMax-M2 Series: Mini Activations Unleashing Max Real-World Intelligence
- Published
- Source
- arXiv
- Paper number
- 241
- Field
- AI / General
- arXiv ID
- 2605.26494
Key points
- A MoE architecture with about 10 billion active parameters achieves both cost efficiency and competitive performance.
- It builds a large synthetic-data pipeline for a wide range of agent tool environments, including code, search, spreadsheet, and slide generation.
- Interleaved-thinking SFT trains trajectories where reasoning and action alternate, enabling the model to handle complex agent loops.
- CISPO policy optimization and agent RL strengthen agent capability by modeling interaction with the environment as an MDP.
- It demonstrates strong performance across practical domains such as financial analysis, document management, and role playing.
- A three-axis scaling strategy for reasoning data, across query side, response side, and training side, improves out-of-distribution generalization.
Paper links
External research summaries. These are not HDATF publications or measured product results.