Maestro uses outcome-based RL to train a lightweight policy that orchestrates ensembles of frozen expert models and skills, reporting 70.1% average accuracy across ten multimodal benchmarks and outperforming GPT-5 and Gemini-2.5-Pro while generalizing to unseen components.
Humaneval-v: benchmarking high-level visual reasoning with complex diagrams in coding tasks
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R1-Onevision turns images into structured text for multimodal reasoning, trains on a custom dataset with RL, and claims SOTA results on an educational benchmark.
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Maestro: Reinforcement Learning to Orchestrate Hierarchical Model-Skill Ensembles
Maestro uses outcome-based RL to train a lightweight policy that orchestrates ensembles of frozen expert models and skills, reporting 70.1% average accuracy across ten multimodal benchmarks and outperforming GPT-5 and Gemini-2.5-Pro while generalizing to unseen components.
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R1-Onevision: Advancing Generalized Multimodal Reasoning through Cross-Modal Formalization
R1-Onevision turns images into structured text for multimodal reasoning, trains on a custom dataset with RL, and claims SOTA results on an educational benchmark.