Amortizing Bayesian Experimental Design via Information-Gain Rewards

This method trains reasoning LLMs to perform sequential Bayesian experimental design by optimizing for expected information gain. The approach decouples the experimental generative model from the LLM's internal beliefs, enabling targeted action selection through explicit reasoning training.