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Found 6 papers, 0 papers with code
Fine-Tuning Diffusion Generative Models via Rich Preference Optimization
We demonstrate the effectiveness of our pipeline and the resulting datasets in fine-tuning state-of-the-art diffusion models.
Score as Action: Fine-Tuning Diffusion Generative Models by Continuous-time Reinforcement Learning
Reinforcement learning from human feedback (RLHF), which aligns a diffusion model with input prompt, has become a crucial step in building reliable generative AI models.
RainbowPO: A Unified Framework for Combining Improvements in Preference Optimization
Recently, numerous preference optimization algorithms have been introduced as extensions to the Direct Preference Optimization (DPO) family.
Preference Tuning with Human Feedback on Language, Speech, and Vision Tasks: A Survey
Preference tuning is a crucial process for aligning deep generative models with human preferences.
Scores as Actions: a framework of fine-tuning diffusion models by continuous-time reinforcement learning
Reinforcement Learning from human feedback (RLHF) has been shown a promising direction for aligning generative models with human intent and has also been explored in recent works for alignment of diffusion generative models.
Trading under the Proof-of-Stake Protocol -- a Continuous-Time Control Approach
In particular, we show when a participant is risk-neutral or risk-seeking, corresponding to the risk-adjusted valuation being a martingale or a sub-martingale, the optimal strategy must be to either buy all the time, sell all the time, or first buy then sell, and with both buying and selling executed at full capacity.
