#f-divergence News & Analysis

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

2 articles

AIBullisharXiv – CS AI · Feb 277/109

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Towards a Sharp Analysis of Offline Policy Learning for $f$-Divergence-Regularized Contextual Bandits

Researchers achieved breakthrough sample complexity improvements for offline reinforcement learning algorithms using f-divergence regularization, particularly for contextual bandits. The study demonstrates optimal O(ε⁻¹) sample complexity under single-policy concentrability conditions, significantly improving upon existing bounds.

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AINeutralarXiv – CS AI · May 116/10

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$f$-Divergence Regularized RLHF: Two Tales of Sampling and Unified Analyses

Researchers present a unified theoretical framework for f-divergence regularized Reinforcement Learning from Human Feedback (RLHF), moving beyond the standard reverse KL approach. The work introduces two novel algorithms with provable efficiency guarantees, achieving O(log T) regret bounds and establishing the first theoretical performance guarantees for online RLHF under general f-divergence regularization.