Hourglass: Towards Better Unified Multimodal Modeling Through Hierarchical Sharing and Conflict-Aware Gradient Surgery

Unified multimodal models (UMMs) have attracted growing attention due to their unified task interfaces and potential for broader generalization. However, existing unified methods largely follow either partially shared designs with limited cross-task coordination or fully shared architectures that may induce capacity competition and gradient interference. To address these limitations, we propose Hourglass, a unified framework that facilitates beneficial collaboration at both the architectural and optimization levels. Motivated by the semantic complementarity and task heterogeneity of visual understanding and generation, Hourglass employs a hierarchical decoupled-shared-decoupled design, retaining task-specific processing in both shallow and deep stages while introducing a selective intermediate sharing mechanism. This structures a balanced trade-off that promotes beneficial cross-task collaboration while preserving task specialization. Nevertheless, jointly training the shared parameters may expose them to conflicting gradients stemming from the distinct natures of understanding and generation. To mitigate this, we further present CocoGrad, a gradient surgery method that dynamically partitions the shared-parameter coordinates into collaborative and conflicting subspaces, preserving common descent directions while coordinating conflicting updates. Extensive experiments across various visual understanding and generation benchmarks demonstrate that our method achieves state-of-the-art or highly competitive performance, providing a viable and scalable blueprint for UMMs.