120 citations · 208 across the 25 of their papers we have counts for
4 papers · 1 filter
FineVerify: Scaling Test-Time Compute with Fine-Grained Self-Verification for Agentic Search
James Xu Zhao, Hui Chen, Bryan Hooi +1
Agentic search requires language model agents to explore many sources and answer complex information-seeking questions. Scaling test-time compute is a promising way to improve thes…
ConvApparel: A Benchmark Dataset and Validation Framework for User Simulators in Conversational Recommenders
Ofer Meshi, Krisztian Balog, Sally Goldman +5
The promise of LLM-based user simulators to improve conversational AI is hindered by a critical "realism gap," leading to systems that are optimized for simulated interactions, but…
Collaborative Multi-Agent Test-Time Reinforcement Learning for Reasoning
Zhiyuan Hu, Yunhai Hu, Juncheng Liu +9
Multi-agent systems have evolved into practical LLM-driven collaborators for many applications, gaining robustness from diversity and cross-checking. However, multi-agent RL (MARL)…
Rewarding the Rare: Uniqueness-Aware RL for Creative Problem Solving in LLMs
Zhiyuan Hu, Yucheng Wang, Yufei He +7
Reinforcement learning (RL) has become a central paradigm for post-training large language models (LLMs), particularly for complex reasoning tasks, yet it often suffers from explor…