2 papers
cs.LG2026
Question-Adaptive Graph Learning for Multi-hop Retrieval Augmented Generation
Yuchen Yan, Peiyan Zhang, Zhihua Liu +4
Retrieval-augmented generation (RAG) has demonstrated its ability to enhance Large Language Models (LLMs) by integrating external knowledge sources. However, multi-hop questions, w…
cs.LG2026
ReCast: Recasting Learning Signals for Reinforcement Learning in Generative Recommendation
Peiyan Zhang, Hanmo Liu, Chengxuan Tong +3
Generic group-based RL assumes that sampled rollout groups are already usable learning signals. We show that this assumption breaks down in sparse-hit generative recommendation, wh…