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OpenForgeRL reveals that decoupling training from inference can dramatically enhance the performance and reliability of AI agents in real-world applications.
AHR achieves state-of-the-art performance in text classification with a fraction of the parameters, revolutionizing how LLMs can adapt to limited data.
Forget toy tasks: scaling synthetic computer environments unlocks surprisingly effective training data for agents tackling month-long, real-world productivity workflows.
