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This vision paper redefines the post-training landscape by arguing that distributed learning must evolve from a privacy add-on to a foundational paradigm for LLMs, addressing critical issues like data sovereignty and representational equity. It identifies specific technical hurdles such as reference drift and preference heterogeneity, proposing a research agenda to make decentralized alignment tractable for diverse domains like healthcare and finance. By challenging the centralization assumption inherent in current RLHF and DPO methods, the work aims to prevent brittle, legally noncompliant models at scale.
Vision Paper: Why Distributed LLM Post-Training and Decentralized Alignment Are Now Essential
arXiv:2611.05841v1 Announce Type: new Abstract: This vision paper argues that distributed learning, including federated and decentralized, should become a first-class paradigm in LLM post-training, not merely a privacy add-on. Current methods like RLHF and DPO rest on an invisible assumption: preference data should be centralized. We contend this assumption is scientifically unjustified, practically unsustainable, and ethically problematic. As LLMs deploy into healthcare, law, finance, and multilingual communities, preference data originates from populations that cannot or will not centralize. We develop a three-pillar argument grounded in (1) privacy and data sovereignty, (2) pluralism and representational equity, and (3) robustness and scalability. We formalize key technical challenges unique to this intersection, reference drift, preference heterogeneity, and the DP-utility trade-off for contrastive losses, and propose concrete algorithmic directions, and sketch a research agenda. Our analysis shows distributed post-training is not only necessary but tractable, and ignoring it risks AI that is legally noncompliant, representationally narrow, and brittle at scale.
Abstract:This vision paper argues that distributed learning, including federated and decentralized, should become a first-class paradigm in LLM post-training, not merely a privacy add-on. Current methods like RLHF and DPO rest on an invisible assumption: preference data should be centralized. We contend this assumption is scientifically unjustified, practically unsustainable, and ethically problematic. As LLMs deploy into healthcare, law, finance, and multilingual communities, preference data originates from populations that cannot or will not centralize. We develop a three-pillar argument grounded in (1) privacy and data sovereignty, (2) pluralism and representational equity, and (3) robustness and scalability. We formalize key technical challenges unique to this intersection, reference drift, preference heterogeneity, and the DP-utility trade-off for contrastive losses, and propose concrete algorithmic directions, and sketch a research agenda. Our analysis shows distributed post-training is not only necessary but tractable, and ignoring it risks AI that is legally noncompliant, representationally narrow, and brittle at scale.
Artificial Intelligence (cs.AI)
arXiv:2611.05841 [cs.AI]
(or arXiv:2611.05841v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2611.05841 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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