Google targets October launch for self-improving model

Operational recursive self-improvement would mark the definitive shift from human-constrained data scaling to autonomous, compounding algorithmic progress. Compounding self-improvement loops could break through current data wall bottlenecks and drastically compress generational training cycles. An impending October 5 release targets direct preemption of OpenAI's GPT-5.5 and Anthropic's Claude Opus in the frontier model race. The viability of self-recursive architectures depends heavily on avoiding synthetic drift, model collapse, and reward hacking during self-training iterations.