3 papers
cs.LG2026
AMARIS: A Memory-Augmented Rubric Improvement System for Rubric-Based Reinforcement Learning
Peilin Wu, Xinlu Zhang, Kun Wan +4
Rubric-based reward shaping provides interpretable and editable reward signals for fine-tuning LLMs via reinforcement learning (RL), but existing adaptive rubric methods typically…
cs.CL2025
Search Wisely: Mitigating Sub-optimal Agentic Searches By Reducing Uncertainty
Peilin Wu, Mian Zhang, Xinlu Zhang +2
Agentic Retrieval-Augmented Generation (RAG) systems enhance Large Language Models (LLMs) by enabling dynamic, multi-step reasoning and information retrieval. However, these system…
cs.CL2025
Do Retrieval-Augmented Language Models Adapt to Varying User Needs?
Peilin Wu, Xinlu Zhang, Wenhao Yu +3
Recent advancements in Retrieval-Augmented Language Models (RALMs) have demonstrated their efficacy in knowledge-intensive tasks. However, existing evaluation benchmarks often assu…