CoLVR uses latent contrastive objectives with angle-based perturbation and RL trajectory rewards to increase exploratory visual reasoning in MLLMs, delivering 5-8% gains on VSP, Jigsaw, and MMStar benchmarks.
Cambrian-1: A fully open, vision-centric exploration of multimodal llms
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SVSR trains multimodal models to verify and correct their own reasoning using a preference dataset, supervised fine-tuning, and semi-online DPO with a teacher model.
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CoLVR: Enhancing Exploratory Latent Visual Reasoning via Contrastive Optimization
CoLVR uses latent contrastive objectives with angle-based perturbation and RL trajectory rewards to increase exploratory visual reasoning in MLLMs, delivering 5-8% gains on VSP, Jigsaw, and MMStar benchmarks.
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SVSR: A Self-Verification and Self-Rectification Paradigm for Multimodal Reasoning
SVSR trains multimodal models to verify and correct their own reasoning using a preference dataset, supervised fine-tuning, and semi-online DPO with a teacher model.