Our technical blog, "Inside Step 5 Preview: Architecture and Training for Agentic Work," covers how architecture and training were co-designed for longer, more complex agent tasks:
- 92-layer narrow-and-deep architecture for implicit multi-hop reasoning
- Sparse GQA to cut million-token context cost, with 8-token block indexing reducing indexing and top-k overhead to 1/8
- FP32 precision on key states plus fine-grained gradient clipping for stable training
- Million-scale verifiable environments
- RL for long-horizon tasks with context compression built into policy training