Thorsten Joachims

Training early-stage rankers in retrieval systems doesn't have to be a high-variance nightmare: CA-PG offers a stable, faster-converging alternative to vanilla policy gradients.

Counterintuitively, throwing more user prompts at prompt optimization can backfire, especially on diverse datasets, but a clever filtering strategy can unlock surprisingly strong generalization from just a handful of examples.