9 papers
Lookahead Routing for Large Language Models
Canbin Huang, Tianyuan Shi, Yuhua Zhu +2
Large language model (LLM) routers improve the efficiency of multi-model systems by directing each query to the most appropriate model while leveraging the diverse strengths of het…
ProactiveEval: A Unified Evaluation Framework for Proactive Dialogue Agents
Tianjian Liu, Fanqi Wan, Jiajian Guo +1
Proactive dialogue has emerged as a critical and challenging research problem in advancing large language models (LLMs). Existing works predominantly focus on domain-specific or ta…
Mutual-Taught for Co-adapting Policy and Reward Models
Tianyuan Shi, Canbin Huang, Fanqi Wan +5
During the preference optimization of large language models (LLMs), distribution shifts may arise between newly generated model samples and the data used to train the reward model…
ThinkSwitcher: When to Think Hard, When to Think Fast
Guosheng Liang, Longguang Zhong, Ziyi Yang +1
Large reasoning models (LRMs) excel at solving complex tasks by leveraging long chain-of-thought (CoT) reasoning. However, this often leads to overthinking on simple tasks, resulti…
FuseRL: Dense Preference Optimization for Heterogeneous Model Fusion
Longguang Zhong, Fanqi Wan, Ziyi Yang +3
Heterogeneous model fusion enhances the performance of LLMs by integrating the knowledge and capabilities of multiple structurally diverse models. However, existing approaches ofte…
FuseChat-3.0: Preference Optimization Meets Heterogeneous Model Fusion
Ziyi Yang, Fanqi Wan, Longguang Zhong +3
We introduce FuseChat-3.0, a suite of large language models (LLMs) developed by integrating the strengths of heterogeneous source LLMs into more compact target LLMs. Our source mod…