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cs.LG2025
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…
cs.LG2025★ 1 cited
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…
cs.LG2025
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…