3 papers
cs.LG2026
Exposure-Based Reinforcement Learning to Rank
Harrie Oosterhuis, Rolf Jagerman, Zhen Qin +1
Reinforcement learning (RL) methods for learning-to-rank (LTR) can optimize (almost) any ranking goal, e.g., from precision or discounted cumulative gain to fairness-of-exposure or…
cs.IR2025
Optimizing Compound Retrieval Systems
Harrie Oosterhuis, Rolf Jagerman, Zhen Qin +1
Modern retrieval systems do not rely on a single ranking model to construct their rankings. Instead, they generally take a cascading approach where a sequence of ranking models are…
cs.CL2025
Inference Scaling for Long-Context Retrieval Augmented Generation
Zhenrui Yue, Honglei Zhuang, Aijun Bai +7
The scaling of inference computation has unlocked the potential of long-context large language models (LLMs) across diverse settings. For knowledge-intensive tasks, the increased c…