ZGCM-1: A Fully Open and Extremely Efficient Foundation Model for Math and Agentic Search
In this work, we present ZGCM-1, a fully open 7B dense foundation model trained from scratch with extreme data, system, and algorithmic efficiency. ZGCM-1 is founded on a core premise: compact models cannot passively memorize the open web, but can overcome parametric capacity limits by coupling deliberate internal thinking with active external tool use. To support this paradigm across a 256K context, we develop an end-to-end, high-efficiency open training recipe: Architecture & System Co-design: interleaved gated sliding-window and full attention, and a stable FP8 Muon optimizer; Progressive Curriculum & MDP Mid-Training: context scaling across 16K, 64K, and 256K, and the reformulation of...
| Authors | Jiyan He, Guang Liang, Hao Liu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 11 September 2026 |
| Last updated at source | 20 September 2026 |
Models and datasets referencing this paper
- zgcagi/ZGCM-1-DataDataset
- zgcagi/ZGCM-1-7BModel
- Kaleto/ZGCM-1-7B-NVFP4Model
- 0xtb/ZGCM-1-7B-GGUFModel
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