@kazani: Google taught LLMs to reason like Bayesians via supervised fine-tuning

Google researchers report that they compressed a classical symbolic Bayesian model into a neural network using supervised fine-tuning. Models trained only on synthetic flight data transferred their probabilistic reasoning to different domains, including hotel recommendations and real-world web shopping, which the post says suggests they internalized general Bayesian principles. The post states that demonstrations of how to reason, rather than correct answers alone, can unlock capabilities that prompting cannot. It also cites three other research examples, including QED-Nano, a 4B parameter model trained to write Olympiad-level proofs.