📢 Excited to share our work "Active Preference Optimization for Sample Efficient RLHF" accepted at #ICML2024 Theoretical Foundations of Foundation Models (@tf2m_workshop) Workshop! Joint work with @Sayakrayc@SOURADIPCHAKR18@aldopacchiano
https://t.co/l5ZCq42pRD
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Excited to share that our paper “Provably Robust DPO: Aligning Language Models with Noisy Feedback” has been accepted at #ICML2024!
We introduce Robust DPO, an unbiased estimate of the DPO loss that is robust to preference noise in the data.
https://t.co/2ByitjmzIL
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