arXiv:2607.03748v1 Announce Type: new Abstract: Unified multi-modal models (UMMs) have shown promising interleaved text-image reasoning capabilities, yet effectively optimizing such multi-turn generation via reinforcement learning (RL) remains an open challenge. Existing approaches apply RL exclusively to text steps, relegating image generation to supervised surrogates, preventing policy gradients from propagating through the full interleaved trajectory across heterogeneous modalities. This leaves the potential of RL for UMMs largely untapped. In the paper, we introduce \textbf{BRAID} (\textbf
The continuous evolution of multi-modal AI models and the increasing focus on agentic systems necessitate more effective training paradigms, making RL for complex interleaved tasks a timely area of research.
This research addresses a critical limitation in training unified multi-modal models, potentially enabling more sophisticated and coherent AI behaviors across text and image generation.
The ability to propagate policy gradients across both text and image generation steps means that reinforcement learning can more effectively optimize the entire interleaved decision process of multi-modal AI.
- · AI model developers
- · Multi-modal AI applications
- · Reinforcement learning researchers
- · AI models reliant solely on supervised learning for image generation
Improved performance and coherence in multi-modal generative AI models, especially in multi-turn reasoning tasks.
Accelerated development of more capable AI agents that can seamlessly integrate text and visual reasoning in complex environments.
Enhanced automation of tasks requiring dynamic cross-modal interaction, leading to new service models and workflows.
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Read at arXiv cs.AI