I think the key part of the bitter lesson is that (scalable) ability to learn from data should be favored over built-in biases.

There are at least three major built-in biases in GPT-O1:

- specific reasoning heuristics hard coded in the RL decision making

- the architectural split between pre-trained LLM and what appears to be a symbolic agent calling it

- the reliance on one-time SGD driven learning (common to all these pre-trained transformers)

IMO search (reasoning) should be an emergent behavior of a predictive architecture capable of continual learning - chained what-if prediction.