Auto-Rubric as Reward: From Implicit Preferences to Explicit Multimodal Generative Criteria
Paper • 2605.08354 • Published • 21
HouJiadong
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📢 Awesome Multimodal Modeling
We introduce Awesome Multimodal Modeling, a curated repository tracing the architectural evolution of multimodal intelligence—from foundational fusion to native omni-models.
🔹 Taxonomy & Evolution:
Traditional Multimodal Learning – Foundational work on representation, fusion, and alignment.
Multimodal LLMs (MLLMs) – Architectures connecting vision encoders to LLMs for understanding.
Unified Multimodal Models (UMMs) – Models unifying Understanding + Generation via Diffusion, Autoregressive, or Hybrid paradigms.
Native Multimodal Models (NMMs) – Models trained from scratch on all modalities; contrasts early vs. late fusion under scaling laws.
💡 Key Distinction:
UMMs unify tasks via generation heads; NMMs enforce interleaving through joint pre-training.
🔗 Explore & Contribute: https://github.com/OpenEnvision/Awesome-Multimodal-Modeling
We introduce Awesome Multimodal Modeling, a curated repository tracing the architectural evolution of multimodal intelligence—from foundational fusion to native omni-models.
🔹 Taxonomy & Evolution:
Traditional Multimodal Learning – Foundational work on representation, fusion, and alignment.
Multimodal LLMs (MLLMs) – Architectures connecting vision encoders to LLMs for understanding.
Unified Multimodal Models (UMMs) – Models unifying Understanding + Generation via Diffusion, Autoregressive, or Hybrid paradigms.
Native Multimodal Models (NMMs) – Models trained from scratch on all modalities; contrasts early vs. late fusion under scaling laws.
💡 Key Distinction:
UMMs unify tasks via generation heads; NMMs enforce interleaving through joint pre-training.
🔗 Explore & Contribute: https://github.com/OpenEnvision/Awesome-Multimodal-Modeling
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