Scores as Actions: a framework of fine-tuning diffusion models by continuous-time reinforcement learning

• URL: http://arxiv.org/abs/2409.08400v1
• Date: Thu, 12 Sep 2024 21:12:21 GMT
• Title: Scores as Actions: a framework of fine-tuning diffusion models by continuous-time reinforcement learning
• Authors: Hanyang Zhao, Haoxian Chen, Ji Zhang, David D. Yao, Wenpin Tang,
• Abstract summary: Reinforcement Learning from human feedback (RLHF) has been shown a promising direction for aligning generative models with human intent. We formulate the task of fine-tuning diffusion models, with reward functions learned from human feedback, as an exploratory continuous-time control problem. We develop the corresponding continuous-time RL theory for policy optimization and regularization under assumptions of different equations.
• Score: 9.025671446527694
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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. In this work, we provide a rigorous treatment by formulating the task of fine-tuning diffusion models, with reward functions learned from human feedback, as an exploratory continuous-time stochastic control problem. Our key idea lies in treating the score-matching functions as controls/actions, and upon this, we develop a unified framework from a continuous-time perspective, to employ reinforcement learning (RL) algorithms in terms of improving the generation quality of diffusion models. We also develop the corresponding continuous-time RL theory for policy optimization and regularization under assumptions of stochastic different equations driven environment. Experiments on the text-to-image (T2I) generation will be reported in the accompanied paper.

Related papers

• Energy-Based Diffusion Language Models for Text Generation [126.23425882687195]
  Energy-based Diffusion Language Model (EDLM) is an energy-based model operating at the full sequence level for each diffusion step. Our framework offers a 1.3$times$ sampling speedup over existing diffusion models.
  arXiv  Detail & Related papers  (2024-10-28T17:25:56Z)
• Avoiding mode collapse in diffusion models fine-tuned with reinforcement learning [0.0]
  Fine-tuning foundation models via reinforcement learning (RL) has proven promising for aligning to downstream objectives. We exploit the hierarchical nature of diffusion models (DMs) and train them dynamically at each epoch with a tailored RL method. We show that models trained with HRF achieve better preservation of diversity in downstream tasks, thus enhancing the fine-tuning robustness and at uncompromising mean rewards.
  arXiv  Detail & Related papers  (2024-10-10T19:06:23Z)
• Reward-Directed Score-Based Diffusion Models via q-Learning [8.725446812770791]
  We propose a new reinforcement learning (RL) formulation for training continuous-time score-based diffusion models for generative AI. Our formulation does not involve any pretrained model for the unknown score functions of the noise-perturbed data distributions.
  arXiv  Detail & Related papers  (2024-09-07T13:55:45Z)
• Score Regularized Policy Optimization through Diffusion Behavior [25.926641622408752]
  Recent developments in offline reinforcement learning have uncovered the immense potential of diffusion modeling. We propose to extract an efficient deterministic inference policy from critic models and pretrained diffusion behavior models. Our method boosts action sampling speed by more than 25 times compared with various leading diffusion-based methods in locomotion tasks.
  arXiv  Detail & Related papers  (2023-10-11T08:31:26Z)
• ODE-based Recurrent Model-free Reinforcement Learning for POMDPs [15.030970899252601]
  We present a novel ODE-based recurrent model combines with model-free reinforcement learning framework to solve POMDPs. We experimentally demonstrate the efficacy of our methods across various PO continuous control and meta-RL tasks. Our experiments illustrate that our method is robust against irregular observations, owing to the ability of ODEs to model irregularly-sampled time series.
  arXiv  Detail & Related papers  (2023-09-25T12:13:56Z)
• Provable Guarantees for Generative Behavior Cloning: Bridging Low-Level Stability and High-Level Behavior [51.60683890503293]
  We propose a theoretical framework for studying behavior cloning of complex expert demonstrations using generative modeling. We show that pure supervised cloning can generate trajectories matching the per-time step distribution of arbitrary expert trajectories.
  arXiv  Detail & Related papers  (2023-07-27T04:27:26Z)
• Predictive Experience Replay for Continual Visual Control and Forecasting [62.06183102362871]
  We present a new continual learning approach for visual dynamics modeling and explore its efficacy in visual control and forecasting. We first propose the mixture world model that learns task-specific dynamics priors with a mixture of Gaussians, and then introduce a new training strategy to overcome catastrophic forgetting. Our model remarkably outperforms the naive combinations of existing continual learning and visual RL algorithms on DeepMind Control and Meta-World benchmarks with continual visual control tasks.
  arXiv  Detail & Related papers  (2023-03-12T05:08:03Z)
• Restoration based Generative Models [0.886014926770622]
  Denoising diffusion models (DDMs) have attracted increasing attention by showing impressive synthesis quality. In this paper, we establish the interpretation of DDMs in terms of image restoration (IR) We propose a multi-scale training, which improves the performance compared to the diffusion process, by taking advantage of the flexibility of the forward process. We believe that our framework paves the way for designing a new type of flexible general generative model.
  arXiv  Detail & Related papers  (2023-02-20T00:53:33Z)
• Training Generative Adversarial Networks by Solving Ordinary Differential Equations [54.23691425062034]
  We study the continuous-time dynamics induced by GAN training. From this perspective, we hypothesise that instabilities in training GANs arise from the integration error. We experimentally verify that well-known ODE solvers (such as Runge-Kutta) can stabilise training.
  arXiv  Detail & Related papers  (2020-10-28T15:23:49Z)
• Control as Hybrid Inference [62.997667081978825]
  We present an implementation of CHI which naturally mediates the balance between iterative and amortised inference. We verify the scalability of our algorithm on a continuous control benchmark, demonstrating that it outperforms strong model-free and model-based baselines.
  arXiv  Detail & Related papers  (2020-07-11T19:44:09Z)
• Nested-Wasserstein Self-Imitation Learning for Sequence Generation [158.19606942252284]
  We propose the concept of nested-Wasserstein distance for distributional semantic matching. A novel nested-Wasserstein self-imitation learning framework is developed, encouraging the model to exploit historical high-rewarded sequences.
  arXiv  Detail & Related papers  (2020-01-20T02:19:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.