RADLADS: Dropping the cost of AI architecture experiment by 250x

Large AI research labs often prioritize scaling over architecture innovation due to the high costs and risks associated with validating new architectures at scale, which can easily amount to hundreds of millions of dollars. However, a new approach called RADLADS (Rapid Attention Distillation to Linear Attention Decoders at Scale) is revolutionizing this paradigm by significantly reducing the cost of experimenting with and validating novel AI architectures. RADLADS enables the transformation of existing massive transformer models into new models with alternative attention mechanisms at a fraction of the original cost, enabling researchers to run extensive iterations quickly and efficiently. This method aligns hidden states, distills output behavior, and fine-tunes for long-context performance, allowing rapid testing of new attention mechanisms and hybrid designs. The approach has already led to advancements in various model architectures, including Transformers and State Space models, and is part of a broader mission to accelerate AI research and make personalized reliable AI and eventually AGI a reality.