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Beyond the Sequentiality Bottleneck: A Hybrid Diffusion-Autoregressive Framework for Efficient Multimodal Reasoning

Wei Cai, Jian Zhao, Yuchen Yuan, Xuelong Li · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.28584 · cited by 0 works (OpenAlex) · open-access PDF

German

Autoregressive Modelle (ARMs) stehen einem „Sequentiality Bottleneck“ in der Argumentation gegenüber, während Diffusion-Sprachmodelle (DLMs) Parallelen bieten, aber keine semantische Kohärenz haben. Um beide Einschränkungen anzugehen, schlagen wir Collaborative Reasoning Asymmetric Learning (CoRAL) vor, ein Hybrid-Framework über eine Asymmetric Planner-Executor (APE) -Architektur: Ein DLM-Planer erzeugt parallel verschiedene Argumentationstrassen K, während ein großer ARM-Executor den optimalen Plan auswählt und die endgültige Antwort synthetisiert. Um den Planer effektiv zu trainieren, führen wir Reward-Guided Denoising für CoT (RDC) ein, das

Machine translation (Meta M2M100); the English original follows.

Abstract

Autoregressive models (ARMs) face a "Sequentiality Bottleneck" in reasoning, while diffusion language models (DLMs) offer parallelism but lack semantic coherence. To addresses both limitations, we propose Collaborative Reasoning Asymmetric Learning (CoRAL), a hybrid framework via an Asymmetric Planner-Executor (APE) architecture: a DLM Planner generates K diverse reasoning trajectories in parallel, while a large ARM Executor selects the optimal plan and synthesizes the final answer. To train the Planner effectively, we introduce Reward-Guided Denoising for CoT (RDC), which integrates outcome-based rewards into the diffusion process to steer it toward goal-directed reasoning. Experiments on vision-language benchmarks show that CoRAL significantly outperforms prior diffusion-based methods and achieves competitive performance against strong ARMs, achieving both superior efficiency and quality.

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Topics: Computer science, Artificial intelligence, Planner, Process (computing), Face (sociological concept), Plan (archaeology)

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