4 citations · 6 across the 2 of their papers we have counts for
6 papers
Multi-Agent Teams Hold Experts Back
Aneesh Pappu, Batu El, Hancheng Cao +4
Multi-agent LLM systems are increasingly deployed as autonomous collaborators, where agents interact freely rather than execute fixed, pre-specified workflows. In such settings, ef…
Apple Intelligence Foundation Language Models
Tom Gunter, Zirui Wang, Chong Wang +152
We present foundation language models developed to power Apple Intelligence features, including a ~3 billion parameter model designed to run efficiently on devices and a large serv…
DynaWeb: Model-Based Reinforcement Learning of Web Agents
Hang Ding, Peidong Liu, Junqiao Wang +7
The development of autonomous web agents, powered by Large Language Models (LLMs) and reinforcement learning (RL), represents a significant step towards general-purpose AI assistan…
COMPASS: Benchmarking Constrained Optimization in LLM Agents
Tian Qin, Felix Bai, Ting-Yao Hu +8
Human decision-making often involves constrained optimization. As LLM agents are deployed to assist with real-world tasks like travel planning, shopping, and scheduling, they must…
Checklists Are Better Than Reward Models For Aligning Language Models
Vijay Viswanathan, Yanchao Sun, Shuang Ma +4
Language models must be adapted to understand and follow user instructions. Reinforcement learning is widely used to facilitate this -- typically using fixed criteria such as "help…
SCAR: Shapley Credit Assignment for More Efficient RLHF
Meng Cao, Shuyuan Zhang, Xiao-Wen Chang +1
Reinforcement Learning from Human Feedback (RLHF) is a widely used technique for aligning Large Language Models (LLMs) with human preferences, yet it often suffers from sparse rewa…