Core Concepts
D3PO method fine-tunes diffusion models using human feedback without a reward model, proving to be efficient and cost-effective.
Abstract
Reinforcement learning with human feedback (RLHF) enhances diffusion models.
Direct Preference Optimization (DPO) eliminates the need for a reward model.
D3PO method directly fine-tunes diffusion models using human feedback data.
Experimental results show improved image quality and alignment with prompts.
D3PO reduces image distortion rates and enhances image safety without robust reward models.
Stats
報酬モデルを必要とせずに拡散モデルを直接微調整する方法。
画像の品質とプロンプトとの整合性を向上させる実験結果。
D3POは、堅牢な報酬モデルなしに画像の歪み率を低減し、画像の安全性を向上させます。
Quotes
"Using reinforcement learning with human feedback (RLHF) has shown significant promise in fine-tuning diffusion models."
"D3PO omits training a reward model, effectively functioning as the optimal reward model trained using human feedback data."
"Our method uses the relative scale of objectives as a proxy for human preference, delivering comparable results to methods using ground-truth rewards."