AIBullisharXiv – CS AI · Mar 177/10
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MapReduce LoRA: Advancing the Pareto Front in Multi-Preference Optimization for Generative Models
Researchers introduce MapReduce LoRA and Reward-aware Token Embedding (RaTE) to optimize multiple preferences in generative AI models without degrading performance across dimensions. The methods show significant improvements across text-to-image, text-to-video, and language tasks, with gains ranging from 4.3% to 136.7% on various benchmarks.
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