Efficient Reinforcement for Visual-Textual Thinking with Discrete Diffusion Model
- lab arXiv
- model Discrete Diffusion Model
Researchers propose using discrete diffusion models as efficient alternatives to autoregressive models for reinforcement learning in multimodal visual-textual thinking and medical image translation.
A study published on arXiv[1] demonstrates that multimodal discrete diffusion models can perform efficient visual rollouts via localized visual editing, reducing computation by 26.9% compared to autoregressive baselines. The researchers also found that factorized reward assignment, which assigns rewards independently to text and vision segments, achieves an 11.2% improvement over joint reward assignment and a 38.04% improvement over the base model[1]. Meanwhile, another study on arXiv[2] introduces the Pixel Puzzling Diffusion Model (PPDM) for medical image-to-image translation. PPDM uses a reversible pixel puzzle-unpuzzle operator to reduce activation memory while preserving global context. The model also adopts a direct bridge diffusion formulation to improve efficiency and stability, and incorporates a puzzle-gradient loss to enforce spatial coherence and suppress grid-like artifacts[2]. These advancements in discrete diffusion models have the potential to improve both reinforcement learning and medical image translation.
research-paper
Background sources we checked (7)
- arxiv.org ↗ RL-based post-training has been widely adopted to enable interleaved visual and textual reasoning in unified multimodal models capable of both text and image generation. However, most existing approaches are built upon autoregressive (AR) unified models, which require full image …
- en.wikipedia.org ↗ A large language model (LLM) is a neural network trained on a vast amount of text for natural language processing tasks, especially language generation. LLMs can typically generate, summarize, translate, and analyze text in many contexts, and are a foundational technology behind …
- en.wikipedia.org ↗ In machine learning, a neural network (NN) or neural net, is a computational model inspired by the structure and functions of biological neural networks. A neural network consists of connected units or nodes called artificial neurons, which loosely model the neurons in the brain.…
- en.wikipedia.org ↗ This glossary of artificial intelligence is a list of definitions of terms and concepts relevant to the study of artificial intelligence (AI), its subdisciplines, and related fields. Related glossaries include Glossary of computer science, Glossary of robotics, Glossary of machin…
- en.wikipedia.org ↗ In machine learning, diffusion models, also known as diffusion-based generative models or score-based generative models, are a class of latent variable generative models. A diffusion model consists of two major components: the forward diffusion process, and the reverse sampling p…
- en.wikipedia.org ↗ In machine learning, discrete diffusion models are a class of diffusion models, which themselves are a class of latent variable generative models. Each discrete diffusion model consists of two major components: the forward jump diffusion process, and the reverse jump diffusion pr…
- en.wikipedia.org ↗ In robot learning, a vision–language–action model (VLA) is a class of multimodal foundation models that integrates vision, language and actions. Given an input image (or video) of the robot's surroundings and a text instruction, a VLA directly outputs low-level robot actions that…