6 papers
The PokeAgent Challenge: Competitive and Long-Context Learning at Scale
Seth Karten, Jake Grigsby, Tersoo Upaa +28
We present the PokeAgent Challenge, a large-scale benchmark for decision-making research built on Pokemon's multi-agent battle system and expansive role-playing game (RPG) environm…
MUSIC: MUlti-Step Instruction Contrast for Multi-Turn Reward Models
Wenzhe Li, Shujian Zhang, Wenxuan Zhou +5
Evaluating the quality of multi-turn conversations is crucial for developing capable Large Language Models (LLMs), yet remains a significant challenge, often requiring costly human…
LLM Economist: Large Population Models and Mechanism Design in Multi-Agent Generative Simulacra
Seth Karten, Wenzhe Li, Zihan Ding +3
We present the LLM Economist, a novel framework that uses agent-based modeling to design and assess economic policies in strategic environments with hierarchical decision-making. A…
PokéChamp: an Expert-level Minimax Language Agent
Seth Karten, Andy Luu Nguyen, Chi Jin
We introduce PokéChamp, a minimax agent powered by Large Language Models (LLMs) for Pokémon battles. Built on a general framework for two-player competitive games, PokéChamp lev…
Building Math Agents with Multi-Turn Iterative Preference Learning
Wei Xiong, Chengshuai Shi, Jiaming Shen +10
Recent studies have shown that large language models' (LLMs) mathematical problem-solving capabilities can be enhanced by integrating external tools, such as code interpreters, and…
Two-Timescale Gradient Descent Ascent Algorithms for Nonconvex Minimax Optimization
Tianyi Lin, Chi Jin, Michael. I. Jordan
We provide a unified analysis of two-timescale gradient descent ascent (TTGDA) for solving structured nonconvex minimax optimization problems in the form of $\min_\textbf{x} \max_{…