Researchers have introduced CoRAL, a novel hybrid framework designed to overcome the limitations of both autoregressive models (ARMs) and diffusion language models (DLMs) in multimodal reasoning. CoRAL employs an Asymmetric Planner-Executor (APE) architecture, where a DLM Planner generates multiple reasoning paths in parallel, and a large ARM Executor selects the best path and synthesizes the final answer. To enhance the Planner's training, a new technique called Reward-Guided Denoising for CoT (RDC) integrates outcome-based rewards into the diffusion process. Experiments on vision-language benchmarks demonstrate that CoRAL achieves superior efficiency and quality compared to existing diffusion-based methods and performs competitively against strong ARMs. AI
IMPACT This hybrid framework could improve efficiency and quality in multimodal reasoning tasks, potentially impacting applications requiring complex understanding of both text and images.
RANK_REASON This is a research paper describing a new framework and training technique for multimodal reasoning.
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- ARM Executor
- arXiv
- Asymmetric Planner-Executor
- autoregressive model
- Collaborative Reasoning Asymmetric Learning
- CoRAL
- Diffusion language models
- DLM Planner
- Hugging Face
- RDC
- Reward-Guided Denoising for CoT
- Sequentiality Bottleneck
- vision-language benchmarks
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