164 citations · 483 across the 35 of their papers we have counts for
6 papers · 1 filter
Black-Box On-Policy Distillation of Large Language Models
Tianzhu Ye, Li Dong, Zewen Chi +3
Black-box distillation creates student large language models (LLMs) by learning from a proprietary teacher model's text outputs alone, without access to its internal logits or para…
The Era of Agentic Organization: Learning to Organize with Language Models
Zewen Chi, Li Dong, Qingxiu Dong +4
We envision a new era of AI, termed agentic organization, where agents solve complex problems by working collaboratively and concurrently, enabling outcomes beyond individual intel…
Towards Stable and Effective Reinforcement Learning for Mixture-of-Experts
Di Zhang, Xun Wu, Shaohan Huang +6
Recent advances in reinforcement learning (RL) have substantially improved the training of large-scale language models, leading to significant gains in generation quality and reaso…
On-Policy RL with Optimal Reward Baseline
Yaru Hao, Li Dong, Xun Wu +3
Reinforcement learning algorithms are fundamental to align large language models with human preferences and to enhance their reasoning capabilities. However, current reinforcement…
Think Only When You Need with Large Hybrid-Reasoning Models
Lingjie Jiang, Xun Wu, Shaohan Huang +7
Recent Large Reasoning Models (LRMs) have shown substantially improved reasoning capabilities over traditional Large Language Models (LLMs) by incorporating extended thinking proce…
Reward Reasoning Model
Jiaxin Guo, Zewen Chi, Li Dong +4
Reward models play a critical role in guiding large language models toward outputs that align with human expectations. However, an open challenge remains in effectively utilizing t…