The results show that targeted RL fine-tuning on high-quality, task-specific data can close — and reverse — a 231-billion-parameter gap in model size, at a training cost under $500, on a real financial reasoning benchmark.
Developers building on or integrating OpenClaw should be aware of its high-volume security advisory pipeline and the active foundation governance model shaping its roadmap and stability.