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This paper introduces OmniThoughtVis, a scalable pipeline for distilling multimodal reasoning capabilities from large teacher models to smaller, deployment-oriented MLLMs. The method uses curated chain-of-thought data to significantly improve reasoning performance on benchmarks like MathVerse and MMMU-Pro for models ranging from 2B to 8B parameters.
This paper introduces On-Policy Data Evolution (ODE) and a visual-native agent harness to improve multimodal deep search agents. By enabling reusable visual evidence and closed-loop data generation, ODE significantly boosts the performance of Qwen3-VL agents across multiple benchmarks, surpassing Gemini 2.5 Pro.
The paper introduces PRISM, a method that inserts a distribution-alignment stage between supervised fine-tuning and reinforcement learning to mitigate distributional drift in multimodal models. It uses a black-box adversarial game with an MoE discriminator to improve RLVR performance on models like Qwen3-VL.