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Found 3 papers, 2 papers with code
Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback
Reinforcement learning from human feedback (RLHF) is a technique for training AI systems to align with human goals.
Interpretable by Design: Learning Predictors by Composing Interpretable Queries
There is a growing concern about typically opaque decision-making with high-performance machine learning algorithms.
AdaLead: A simple and robust adaptive greedy search algorithm for sequence design
In this work, we implement an open-source Fitness Landscape EXploration Sandbox (FLEXS: github. com/samsinai/FLEXS) environment to test and evaluate these algorithms based on their optimality, consistency, and robustness.
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