111 citations · 325 across the 16 of their papers we have counts for
18 papers
Agent Plasticity: Measuring Self-Improvement Through Experience
Harman Singh, Anton Bakhtin, Rulin Shao +8
AI agents increasingly operate in environments where they can diagnose failures and improve through experience, yet existing evaluations largely measure what an agent can do at a f…
Thinking Before Thinking: Scaling Agentic Inference Through Meta-Reasoning
Paras Dahal, Anton Bakhtin, Taco Cohen +9
As agents take on longer and more complex problems, controlling the execution becomes a task in its own right. Each step in the run brings new control choices, like which partial w…
Towards Measuring the Representation of Subjective Global Opinions in Language Models
Esin Durmus, Karina Nguyen, Thomas I. Liao +15
Large language models (LLMs) may not equitably represent diverse global perspectives on societal issues. In this paper, we develop a quantitative framework to evaluate whose opinio…
Mastering the Game of No-Press Diplomacy via Human-Regularized Reinforcement Learning and Planning
Anton Bakhtin, David J Wu, Adam Lerer +5
No-press Diplomacy is a complex strategy game involving both cooperation and competition that has served as a benchmark for multi-agent AI research. While self-play reinforcement l…
Self-Explaining Deviations for Coordination
Hengyuan Hu, Samuel Sokota, David Wu +4
Fully cooperative, partially observable multi-agent problems are ubiquitous in the real world. In this paper, we focus on a specific subclass of coordination problems in which huma…
Modeling Strong and Human-Like Gameplay with KL-Regularized Search
Athul Paul Jacob, David J. Wu, Gabriele Farina +5
We consider the task of building strong but human-like policies in multi-agent decision-making problems, given examples of human behavior. Imitation learning is effective at predic…