1 citations · 1 across the 3 of their papers we have counts for
4 papers
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…