Nearly Optimal Algorithms for Contextual Dueling Bandits from Adversarial Feedback

The introduction of the robust contextual dueling bandits algorithm marks a significant advancement in the field of machine learning, particularly in scenarios where adversarial feedback can distort human preferences. This algorithm utilizes uncertainty-weighted maximum likelihood estimation to achieve a nearly optimal regret bound of $ ilde O(d ext{sqrt}{T}/ ext{kappa}+dC/ ext{kappa})$, where $T$ represents the number of rounds and $C$ the total adversarial feedback. This development is particularly important as it addresses the negative impact adversaries can have on the effectiveness of learning from human feedback, which is essential for aligning generative models. The research demonstrates that the algorithm is the first to achieve nearly minimax optimal regret in the presence of adversarial feedback, underscoring its potential to enhance the reliability of outputs from large language models and other generative systems.