There is little evidence that the human brain uses an equivalent of taking the transpose of a weight matrix for learning, suggesting alternatives to backpropagation must exist.

Noam Brown believes that models trained on the human-generated data subspace can use test-time compute and recursive self-improvement to access the full solution space.

François believes it is improbable that current AI models will be able to sample the entire solution space beyond human-generated data with any feasible amount of test-time compute or recursive self-improvement.