3 papers
cs.LG2026
SCALAR: Learning and Composing Skills through LLM Guided Symbolic Planning and Deep RL Grounding
Renos Zabounidis, Yue Wu, Simon Stepputtis +4
LM-based agents excel when given high-level action APIs but struggle to ground language into low-level control. Prior work has LLMs generate skills or reward functions for RL, but…
cs.AI2024
AgentKit: Structured LLM Reasoning with Dynamic Graphs
Yue Wu, Yewen Fan, So Yeon Min +6
We propose an intuitive LLM prompting framework (AgentKit) for multifunctional agents. AgentKit offers a unified framework for explicitly constructing a complex "thought process" f…
cs.LG2023
SmartPlay: A Benchmark for LLMs as Intelligent Agents
Yue Wu, Xuan Tang, Tom M. Mitchell +1
Recent large language models (LLMs) have demonstrated great potential toward intelligent agents and next-gen automation, but there currently lacks a systematic benchmark for evalua…