<ul><li>Theoretical analysis of noisy labels in offline alignment, focusing on privacy and adversarial robustness.</li><li>Unified analysis covering RLHF and DPO under different privacy-corruption scenarios.</li><li>Utilization of a reduction framework to link the problem to parameter estimation in logistic regression.</li><li>Demonstration of LTC presenting greater challenges than CTL in offline alignment under linear models.</li></ul>

A Unified Theoretical Analysis of Private and Robust Offline Alignment: from RLHF to DPO

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