4 papers
Learning Abstractions for Hierarchical Planning in Program-Synthesis Agents
Zergham Ahmed, Kazuki Irie, Joshua B. Tenenbaum +2
Humans learn abstractions and use them to plan efficiently to quickly generalize across tasks -- an ability that remains challenging for state-of-the-art large language model (LLM)…
LLM-Guided Probabilistic Program Induction for POMDP Model Estimation
Aidan Curtis, Hao Tang, Thiago Veloso +4
Partially Observable Markov Decision Processes (POMDPs) model decision making under uncertainty. While there are many approaches to approximately solving POMDPs, we aim to address…
Synthesizing world models for bilevel planning
Zergham Ahmed, Joshua B. Tenenbaum, Christopher J. Bates +1
Modern reinforcement learning (RL) systems have demonstrated remarkable capabilities in complex environments, such as video games. However, they still fall short of achieving human…
Testing the Limits of Fine-Tuning for Improving Visual Cognition in Vision Language Models
Luca M. Schulze Buschoff, Konstantinos Voudouris, Elif Akata +3
Pre-trained vision language models still fall short of human visual cognition. In an effort to improve visual cognition and align models with human behavior, we introduce visual st…