PAFT: A Parallel Training Paradigm for Effective LLM Fine-Tuning

PAFT: A Parallel Training Paradigm for Effective LLM Fine-Tuning

25 June 2024

Shiva K. Pentyala

Regunathan Radhakrishnan

Cheng

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Papers citing "PAFT: A Parallel Training Paradigm for Effective LLM Fine-Tuning"

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Title
Mixup Model Merge: Enhancing Model Merging Performance through Randomized Linear Interpolation Yue Zhou Yi-Ju Chang Yuan Wu MoMe 122 3 0 21 Feb 2025
UFT: Unifying Fine-Tuning of SFT and RLHF/DPO/UNA through a Generalized Implicit Reward Function Zhichao Wang Bin Bi Z. Zhu Xiangbo Mao Jun Wang Shiyu Wang CLL 79 1 0 28 Oct 2024
SummAct: Uncovering User Intentions Through Interactive Behaviour Summarisation Guanhua Zhang Mohamed Ahmed Zhiming Hu Andreas Bulling AI4TS 46 1 0 10 Oct 2024
Anchored Preference Optimization and Contrastive Revisions: Addressing Underspecification in Alignment Karel DÓosterlinck Winnie Xu Chris Develder Thomas Demeester A. Singh Christopher Potts Douwe Kiela Shikib Mehri 80 17 0 12 Aug 2024
Right Now, Wrong Then: Non-Stationary Direct Preference Optimization under Preference Drift Seongho Son William Bankes Sayak Ray Chowdhury Brooks Paige Ilija Bogunovic 122 4 0 26 Jul 2024