PRDP: Proximal Reward Difference Prediction for Large-Scale Reward
  Finetuning of Diffusion Models
Reward finetuning has emerged as a promising approach to aligning foundation
models with downstream objectives. Remarkable success has been achieved in the
language domain by using reinforcement learning (RL) to maximize rewards that
reflect human preference. However, in the vision domain, existing RL-based
reward finetuning methods are limited by their instability in large-scale
training, rendering them incapable of generalizing to complex, unseen prompts.
In this paper, we propose Proximal Reward Difference Prediction (PRDP),
enabling stable black-box reward finetuning for diffusion models for the first
time on large-scale prompt datasets with over 100K prompts. Our key innovation
is the Reward Difference Prediction (RDP) objective that has the same optimal
solution as the RL objective while enjoying better training stability.
Specifically, the RDP objective is a supervised regression objective that tasks
the diffusion model with predicting the reward difference of generated image
pairs from their denoising trajectories. We theoretically prove that the
diffusion model that obtains perfect reward difference prediction is exactly
the maximizer of the RL objective. We further develop an online algorithm with
proximal updates to stably optimize the RDP objective. In experiments, we
demonstrate that PRDP can match the reward maximization ability of
well-established RL-based methods in small-scale training. Furthermore, through
large-scale training on text prompts from the Human Preference Dataset v2 and
the Pick-a-Pic v1 dataset, PRDP achieves superior generation quality on a
diverse set of complex, unseen prompts whereas RL-based methods completely
fail.
65cda11815927632e844a903
Fei Deng
65cda11815927632e844a904
Qifei Wang
65cda11815927632e844a905
Wei Wei
65cda11815927632e844a906
Matthias Grundmann
admin_assigned
2024-02-15T10:03:53.191Z
60fa5a5f8b6cceb9a84297d0
/avatars/3a59609db54687cc2e1e373b897e7f5c.svg
Matthias Grundmann
user
grundman
65cda11815927632e844a907
Tingbo Hou
admin_assigned
2024-02-15T10:04:02.476Z
655846d7ed8df83128f5826a
/avatars/d7ce174d7d1b8614d5f6f071225c0057.svg
Hou
user
Tingbo
PRDP: Proximal Reward Difference Prediction for Large-Scale Reward
  Finetuning of Diffusion Models
Reward finetuning has emerged as a promising approach to aligning foundation
models with downstream objectives. Remarkable success has been achieved in the
language domain by using reinforcement learning (RL) to maximize rewards that
reflect human preference. However, in the vision domain, existing RL-based
reward finetuning methods are limited by their instability in large-scale
training, rendering them incapable of generalizing to complex, unseen prompts.
In this paper, we propose Proximal Reward Difference Prediction (PRDP),
enabling stable black-box reward finetuning for diffusion models for the first
time on large-scale prompt datasets with over 100K prompts. Our key innovation
is the Reward Difference Prediction (RDP) objective that has the same optimal
solution as the RL objective while enjoying better training stability.
Specifically, the RDP objective is a supervised regression objective that tasks
the diffusion model with predicting the reward difference of generated image
pairs from their denoising trajectories. We theoretically prove that the
diffusion model that obtains perfect reward difference prediction is exactly
the maximizer of the RL objective. We further develop an online algorithm with
proximal updates to stably optimize the RDP objective. In experiments, we
demonstrate that PRDP can match the reward maximization ability of
well-established RL-based methods in small-scale training. Furthermore, through
large-scale training on text prompts from the Human Preference Dataset v2 and
the Pick-a-Pic v1 dataset, PRDP achieves superior generation quality on a
diverse set of complex, unseen prompts whereas RL-based methods completely
fail.
Proximal Reward Difference Prediction enables stable large-scale reward finetuning of diffusion models in vision, outperforming RL-based methods on complex prompts.
reward finetuning reinforcement learning diffusion models reward difference prediction denoising trajectories Proximal Reward Difference Prediction Human Preference Dataset Pick-a-Pic Dataset