Pathway’s Baby Dragon Hatchling (BDH) is a brain-inspired architecture that reasons in latent space. It differs from transformer architectures by not emitting chain-of-thought tokens. Development and scaling of BDH is occurring on Amazon SageMaker HyperPod. BDH-CQ achieved a new cost-efficiency mark on the ARC-AGI-1 benchmark. This indicates a potential improvement in efficiency compared to other models on that benchmark. This approach allows for potentially more efficient model execution, particularly for complex reasoning tasks. The use of SageMaker HyperPod provides the necessary compute resources for training and experimentation. This development represents a shift in architectural design for AI models, moving away from traditional token-based approaches.
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