ZGCM-1: Open 7B foundation model claiming extreme efficiency and 256K context for math and agentic search
The arXiv submission (arXiv:2609.13356v1) presents ZGCM-1, a fully open 7B dense foundation model trained from scratch with an emphasis on data, system, and algorithmic efficiency and long-context operation (up to 256K). The authors report that ZGCM-1-7B is competitive with larger models on general benchmarks and on challenging mathematical reasoning and agentic search suites, claim a ≈4.2× improvement in 16K pre-training time-to-loss, describe a training recipe (including interleaved gated sliding-window/full attention and an FP8 Muon optimizer), and open-source weights, checkpoints, code, data recipes, and W&B logs.