Google Research· Research

Teaching LLMs to reason like Bayesians

Sjoerd van Steenkiste and Tal Linzen, Research Scientists, Google Research We teach LLMs to reason in a Bayesian manner by training them to mimic the predictions of an optimal Bayesian model. Quick links Paper Share Copy link × AI systems based on large language models (LLMs) are increasingly used as agents that interact with users and the world. To do this successfully, LLMs need to construct internal representations of the world and estimate the probability that each of these representations is accurate. Take personalized recommendations, for example: the LLM needs to gradually infer the user’s preferences from their choices over the course of multiple interactions. Bayesian inference defines the optimal way to perform such updates. By implementing this strategy, LLMs could optimize user interactions by updating their estimates of the user’s preferences as new info about the user arrives. But without specific training, LLMs often default to simple heuristics — like assuming everyone wants the cheapest option — instead of inferring a specific user's unique preferences. In “ …

Announced

04 Mar 2026 20:29 UTC

Detected

03 Aug 2026 21:12 UTC (+152d after)