UniRL-Zero: Reinforcement Learning on Unified Models with Joint Language Model and Diffusion Model Experts
This work addresses the challenge of integrating and optimizing multimodal AI models for researchers and practitioners, though it appears incremental as it builds on existing unified model concepts.
The authors tackled the problem of enhancing multimodal language model understanding and reasoning alongside diffusion model multimedia generation within a unified reinforcement learning framework, resulting in the development of UniRL-Zero with systematic baselines for six scenarios.
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.