arxiv_ai 80% Match Research Paper AI researchers,ML engineers,Developers of multimodal AI systems,Researchers in LLMs and diffusion models 2 weeks ago

UniRL-Zero: Reinforcement Learning on Unified Models with Joint Language Model and Diffusion Model Experts

reinforcement-learning › multi-agent

📄 Abstract

Abstract: We present UniRL-Zero, a unified reinforcement learning (RL) framework that boosts, multimodal language model understanding and reasoning, diffusion model multimedia generation, and their beneficial interaction capabilities within a unified model. Our work defines six scenarios for unified model reinforcement learning, providing systematic baselines for reinforcement learning of unified understanding and generation model. Our code is available at https://github.com/G-U-N/UniRL.

Authors (5)

Fu-Yun Wang

Han Zhang

Michael Gharbi

Hongsheng Li

Taesung Park

Submitted

October 20, 2025

arXiv Category

cs.LG

arXiv PDF Code

Key Contributions

UniRL-Zero presents a unified reinforcement learning framework designed to enhance both multimodal language model understanding/reasoning and diffusion model multimedia generation. It defines six scenarios for unified model RL, providing systematic baselines for training models that excel in both understanding and generation tasks, fostering beneficial interactions between modalities.

Business Value

Paves the way for more versatile and powerful AI systems capable of both understanding complex information and generating rich multimedia content, leading to enhanced creative tools and more intelligent agents.

Paper Metadata

Innovation Type

Framework

Deployment Feasibility

Moderate. Requires significant computational resources for training unified models and RL agents.

Limitations Addressed

Addresses the siloed development of language models and diffusion models by proposing a unified framework. Aims to improve the synergistic capabilities and interaction between these two powerful AI paradigms.

Performance Gains

Provides systematic baselines for evaluating improvements in unified multimodal understanding and generation capabilities.

View Code on GitHub

Technical Tags

reinforcement learning (RL)unified modelslanguage modelsdiffusion modelsmultimodal understandingmultimedia generationRL frameworkjoint trainingunderstanding and generationmultimodal interaction

Research Topics

Reinforcement LearningMultimodal AILarge Language ModelsGenerative ModelsAI Integration

Methods & Architectures

Reinforcement Learning (RL)Joint Training of Language and Diffusion ModelsUnified Framework Design Unified ModelsLanguage ModelsDiffusion Models

Applications & Tasks

AI Assistants Content Creation Robotics Human-Computer Interaction Multimodal UnderstandingMultimodal GenerationUnified AI CapabilitiesImproving LLM and Diffusion Model Interaction Enhancing multimodal understanding and reasoning in LLMsImproving multimedia generation with diffusion modelsEnabling beneficial interaction between language and diffusion modelsDeveloping a unified RL framework for multimodal AI

Related Fields

Artificial IntelligenceMachine LearningDeep LearningNatural Language ProcessingComputer VisionReinforcement Learning

Keywords

reinforcement learningunified modelslanguage modelsdiffusion modelsmultimodalunderstandinggenerationRL frameworkAIdeep learningmultimediainteractionreasoningzero-shot

Academic Context

#Reinforcement Learning#Multimodal AI#Large Language Models#Generative Models#AI Integration

Commercial Potential

Potential Products

Advanced creative AI toolsMultimodal AI assistantsRobotic agents with enhanced perception and action capabilitiesAI systems for interactive storytelling

Target Industries

Media and EntertainmentTechnologyGamingRoboticsEducation

Use Case Examples

An AI agent that can understand a user'.s request and generate a corresponding image or videoA system that can reason about visual input and generate descriptive textRobots that can understand complex commands and perform actions involving both language and visual perception

Competitive Edge

Offers a novel unified framework for RL applied to multimodal models, aiming to bridge the gap between language understanding and generative capabilities, potentially leading to more integrated and powerful AI systems.

Market Opportunity

Rapidly expanding market for generative AI and multimodal AI solutions.

Revenue Models

API accessspecialized model developmentplatform licensing.

Resource Requirements

Compute Needs

High, for training unified models and RL agents.

Data Requirements

Requires diverse multimodal datasets suitable for training both language and diffusion models, and for RL tasks.

Deployment Constraints

Complexity of training and deploying unified multimodal RL agents.

Scalability

Scalability depends on the underlying language and diffusion models and the efficiency of the RL algorithm.

Production Readiness

Maturity Level

Research

Time to Market

3-5 years

Licensing

Open Source (Code available)

Patent Potential

Moderate, for the unified RL framework and specific training methodologies.

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