From the 1 of 6 linked papers with an AI index.
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Towards Reinforcement Learning from Neural Feedback: Mapping fNIRS Signals to Agent Performance
Julia Santaniello, Matthew Russell, Benson Jiang +3
Reinforcement Learning from Human Feedback (RLHF) is a methodology that aligns agent behavior with human preferences by integrating user feedback into the agent's training process.…
Creative Problem Solving in Large Language and Vision Models -- What Would it Take?
Lakshmi Nair, Evana Gizzi, Jivko Sinapov
We advocate for a strong integration of Computational Creativity (CC) with research in large language and vision models (LLVMs) to address a key limitation of these models, i.e., c…
Logical Specifications-guided Dynamic Task Sampling for Reinforcement Learning Agents
Yash Shukla, Tanushree Burman, Abhishek Kulkarni +3
Reinforcement Learning (RL) has made significant strides in enabling artificial agents to learn diverse behaviors. However, learning an effective policy often requires a large numb…