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#dueling-bandits News & Analysis

2 articles tagged with #dueling-bandits. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

2 articles
AIBullisharXiv – CS AI · Jun 26/10
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T-POP: Test-Time Personalization with Online Preference Feedback

Researchers introduce T-POP, a novel algorithm that personalizes large language models in real-time by learning from user preference feedback during text generation, without requiring parameter updates or extensive pre-existing user data. The method combines test-time alignment with dueling bandits to efficiently balance exploration and exploitation, addressing the cold-start problem in LLM personalization.

AINeutralarXiv – CS AI · May 276/10
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Linear and Neural Dueling Bandits with Delayed Feedback

Researchers propose novel algorithms (LDB-DF and NDB-DF) for contextual dueling bandits that handle delayed feedback—a critical real-world constraint in recommender systems and LLM alignment. The breakthrough involves an Inverse Probability Weighting mechanism that eliminates bias from delayed observations, achieving theoretical regret bounds of O(d√T) for linear settings.