1 citations · 2 across the 9 of their papers we have counts for
5 papers · 1 filter
Lossy communication constrains iterated learning
Ben Prystawski, Dilip Arumugam, Noah D. Goodman
Humans' distinctive role in the world can largely be attributed to our capacity for iterated learning, a process by which knowledge is expanded and refined over generations. A rang…
Demystifying the Mechanisms Behind Emergent Exploration in Goal-conditioned RL
Mahsa Bastankhah, Grace Liu, Dilip Arumugam +2
In this work, we take a first step toward elucidating the mechanisms behind emergent exploration in unsupervised reinforcement learning. We study Single-Goal Contrastive Reinforcem…
Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems
Jiayi Geng, Howard Chen, Dilip Arumugam +1
Using AI to create autonomous researchers has the potential to accelerate scientific discovery. A prerequisite for this vision is understanding how well an AI model can identify th…
Using Reinforcement Learning to Train Large Language Models to Explain Human Decisions
Jian-Qiao Zhu, Hanbo Xie, Dilip Arumugam +2
A central goal of cognitive modeling is to develop models that not only predict human behavior but also provide insight into the underlying cognitive mechanisms. While neural netwo…
Toward Efficient Exploration by Large Language Model Agents
Dilip Arumugam, Thomas L. Griffiths
A burgeoning area within reinforcement learning (RL) is the design of sequential decision-making agents centered around large language models (LLMs). While autonomous decision-maki…