A Simple and Effective Reinforcement Learning Method for Text-to-Image Diffusion Fine-tuning
This addresses the computational and hyperparameter sensitivity issues in RL-based fine-tuning for diffusion models, offering a more practical solution for researchers and practitioners, though it is incremental as it builds on existing methods.
The paper tackled the problem of fine-tuning text-to-image diffusion models with reinforcement learning by proposing LOOP, a method that combines variance reduction from REINFORCE with the robustness of PPO, achieving a better balance between computational efficiency and performance on various black-box objectives.
Reinforcement learning (RL)-based fine-tuning has emerged as a powerful approach for aligning diffusion models with black-box objectives. Proximal policy optimization (PPO) is the most popular choice of method for policy optimization. While effective in terms of performance, PPO is highly sensitive to hyper-parameters and involves substantial computational overhead. REINFORCE, on the other hand, mitigates some computational complexities such as high memory overhead and sensitive hyper-parameter tuning, but has suboptimal performance due to high-variance and sample inefficiency. While the variance of the REINFORCE can be reduced by sampling multiple actions per input prompt and using a baseline correction term, it still suffers from sample inefficiency. To address these challenges, we systematically analyze the efficiency-effectiveness trade-off between REINFORCE and PPO, and propose leave-one-out PPO (LOOP), a novel RL for diffusion fine-tuning method. LOOP combines variance reduction techniques from REINFORCE, such as sampling multiple actions per input prompt and a baseline correction term, with the robustness and sample efficiency of PPO via clipping and importance sampling. Our results demonstrate that LOOP effectively improves diffusion models on various black-box objectives, and achieves a better balance between computational efficiency and performance.