10 citations · 18 across the 12 of their papers we have counts for
3 papers · 1 filter
How Deep Can LLMs Learn to Reason? Expressiveness Is Key
Tianle Wang, Zhaoyang Wang, Guangchen Lan +4
Reinforcement learning (RL) has been applied to improve large language model (LLM) reasoning, yet the systematic study of how training scales with task difficulty has been hampered…
Iterative Critique-and-Routing Controller for Multi-Agent Systems with Heterogeneous LLMs
Wenzhi Fang, Liangqi Yuan, Guangchen Lan +2
Multi-agent large language model (LLM) systems often rely on a controller to coordinate a pool of heterogeneous models, yet existing controllers are typically limited to one-shot r…
Contextual Integrity in LLMs via Reasoning and Reinforcement Learning
Guangchen Lan, Huseyin A. Inan, Sahar Abdelnabi +5
As the era of autonomous agents making decisions on behalf of users unfolds, ensuring contextual integrity (CI) -- what is the appropriate information to share while carrying out a…