2 citations · 2 across the 3 of their papers we have counts for
8 papers · 1 filter
Alternating Reinforcement Learning for Rubric-Based Reward Modeling in Non-Verifiable LLM Post-Training
Ran Xu, Tianci Liu, Zihan Dong +6
Standard reward models typically predict scalar scores that fail to capture the multifaceted nature of response quality in non-verifiable domains, such as creative writing or open-…
Incentivizing Agentic Reasoning in LLM Judges via Tool-Integrated Reinforcement Learning
Ran Xu, Jingjing Chen, Jiayu Ye +4
Large Language Models (LLMs) are widely used as judges to evaluate response quality, providing a scalable alternative to human evaluation. However, most LLM judges operate solely o…
OpenRubrics: Towards Scalable Synthetic Rubric Generation for Reward Modeling and LLM Alignment
Tianci Liu, Ran Xu, Tony Yu +4
Reward modeling lies at the core of reinforcement learning from human feedback (RLHF), yet most existing reward models rely on scalar or pairwise judgments that fail to capture the…
AceSearcher: Bootstrapping Reasoning and Search for LLMs via Reinforced Self-Play
Ran Xu, Yuchen Zhuang, Zihan Dong +7
Search-augmented LLMs often struggle with complex reasoning tasks due to ineffective multi-hop retrieval and limited reasoning ability. We propose AceSearcher, a cooperative self-p…
Retrieval-augmented GUI Agents with Generative Guidelines
Ran Xu, Kaixin Ma, Wenhao Yu +4
GUI agents powered by vision-language models (VLMs) show promise in automating complex digital tasks. However, their effectiveness in real-world applications is often limited by sc…
RAG in the Wild: On the (In)effectiveness of LLMs with Mixture-of-Knowledge Retrieval Augmentation
Ran Xu, Yuchen Zhuang, Yue Yu +3
Retrieval-augmented generation (RAG) enhances large language models (LLMs) by integrating external knowledge retrieved at inference time. While RAG demonstrates strong performance…