Comment by HarHarVeryFunny

They've developed a sparse attention mechanism (which they document and release source code for) to increase model efficiency with long context, as needed for fast & cost-effective extensive RL training for reasoning and agentic use

They've built a "stable & scalable" RL protocol - more capable RL training infrastructure

They've built a pipeline/process to generate synthetic data for reasoning and agentic training

These all combine to build an efficient model with extensive RL post-training for reasoning and agentic use, although they note work is still needed on both the base model (more knowledge) and post-training to match frontier performance.