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New Training Method Boosts AI Multimodal Reasoning with Smaller, Smarter Datasets

Researchers at MiroMind AI and partner universities introduced OpenMMReasoner, a two‑stage training framework that first fine‑tunes a base vision‑language model on a curated, high‑quality dataset and then applies reinforcement learning to sharpen multimodal reasoning. The approach achieves state‑of‑the‑art performance on benchmarks such as WeMath and MathVista while using far fewer training samples than competing methods. All assets, including a 7‑billion‑parameter model, are open‑source, allowing enterprises to deploy locally and retain full data control. The work highlights how targeted data diversity and token‑budgeted reasoning can boost accuracy and efficiency simultaneously.