arXiv:2605.20696v1 Announce Type: new Abstract: Preference-based reinforcement learning (RL) is a key paradigm for aligning policies with human judgments, yet its theoretical behavior in distributed settings where preference data are fragmented across heterogeneous users remains poorly understood. Direct Preference Optimization (DPO) avoids explicit reward modeling but lacks convergence guarantees under federated and decentralized training, where communication constraints and non-IID preferences fundamentally alter optimization dynamics. We provide the first convergence and time-complexity ana
The proliferation of preference-based learning in AI necessitates robust theoretical underpinnings for distributed settings, especially as AI deployment scales across various, often fragmented, data sources.
This research addresses fundamental challenges in scaling AI alignment, particularly in scenarios where data is decentralized, which is crucial for the development of secure and efficient autonomous AI systems.
The development of convergence guarantees for distributed DPO opens pathways for more reliable and scalable deployment of preference-based AI systems, particularly in federated and edge computing environments.
- · AI developers
- · Federated learning platforms
- · Edge AI providers
- · Centralized data models
- · AI systems reliant on homogeneous data
Improved performance and reliability of AI models trained on distributed and heterogeneous data sources.
Accelerated adoption of AI in privacy-sensitive sectors due to enhanced distributed training capabilities.
Potential for new decentralized AI applications and services that leverage fragmented real-world preference data more effectively.
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Read at arXiv cs.LG