Parcae: Doing more with fewer parameters using stable looped models

Parcae is a novel, stable architecture for looped language models that achieves the performance of a Transformer model twice its size while maintaining clean and predictable training, offering an efficient alternative for memory-constrained on-device models by increasing recurrence rather than data. Traditional scaling laws suggest that improved performance requires more parameters or data, but Parcae challenges this by using looped architectures, which increase computational efficiency by passing activations through the same layers multiple times, addressing instability issues through a new design that maintains stability conditions. Parcae demonstrates better performance than previous looped models, achieving up to 6.3% lower validation perplexity and matching the quality of larger Transformer models with significantly fewer parameters. It offers predictable scaling, establishing the first scaling laws for looping, and proves to be robust against hyperparameter variations. The model's structure divides layers into prelude, recurrent, and coda blocks, and it has been tested to outperform parameter- and data-matched Transformers, suggesting a promising future for parameter efficiency and the exploration of layer looping in reducing inference costs.

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