A continuous-time RL algorithm that treats diffusion scores as actions fine-tunes text-to-image models with a Girsanov-based KL regularizer, showing stability across different denoising step counts.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Score as Action: Fine-Tuning Diffusion Generative Models by Continuous-time Reinforcement Learning
A continuous-time RL algorithm that treats diffusion scores as actions fine-tunes text-to-image models with a Girsanov-based KL regularizer, showing stability across different denoising step counts.