This 'recurrent depth' is essentially what's in Sec. 5.3 of the 2015 paper: On Learning to Think: Algorithmic Information Theory for Novel C…

This "recurrent depth" is essentially what's in Sec. 5.3 of the 2015 paper: On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models https://arxiv.org/abs/1511.09249. This paper went beyond the inefficient millisecond by millisecond planning of my 1990 neural world models, addressing planning and reasoning in abstract concept spaces. The 2015 control network C is a prompt engineer that learns to create a chain of thought: to speed up decision making, C learns to query its separate neural world model for abstract reasoning. The prompts and the answers are internal self-generated sequences of vectors that don't have to represent natural language. OpenAI’s Astra AI uses a new reasoning approach called “recurrent depth.” Though it can help model costs and performance, researchers are concerned bc it obscures a model’s thinking process, making it more difficult to monitor. w/ @amir @rocketalignment https://www.theinformation…

Source: David Ha (X) | 2026-09-02