Dimple: Discrete Diffusion Multimodal Large Language Model with Parallel Decoding

Understand

In this work, we propose Dimple, the first Discrete Diffusion Multimodal Large Language Model (DMLLM).

• We observe that training with a purely discrete diffusion approach leads to significant training instability, suboptimal performance, and severe length bias issues.
• To address these challenges, we design a novel training paradigm that combines an initial autoregressive phase with a subsequent diffusion phase.
• This approach yields the Dimple-7B model, trained on the same dataset and using a similar training pipeline as LLaVA-NEXT.