Daniel Ritter

Research Focus

Ritter is interested in developing sample-efficient reinforcement learning algorithms, primarily for post-training large generative models. Sampling from large models is costly, so finding more efficient ways to use data we’ve already generated both makes current post-training applications faster, and opens up new applications in domains with limited data.