论文库

原文摘要

Within the landscape of inference-time scaling methods for foundation models, a width-based approach to scaling--which involves the insertion of <pause> tokens in the input stream to delay model responses--offers a unique advantage by increasing model expressivity while remaining highly parallelizable at both training and inference. The existing literature on training models to utilize <pause> tokens relies on the standard cross-entropy objective in which the model output is read out and evaluated only at the final step of a pause sequence. This approach provides no mechanism for the model to regulate its own processing or to signal readiness to respond, treating the additional compute steps as a barrier to skip over rather than a resource to be used adaptively. We propose a supervised loss, Catch Your Breath (CYB), framed as a sequential-decision problem, that trains a model to dynamically and autonomously scale the number of compute steps used for each input token. The model indicates the need for additional compute steps by emitting a special <don't know> output, delaying its response via a pause. The model can abstain multiple times to obtain longer delays. Our experiments demonstrate that when <pause> tokens are included in the input stream, pretraining or fine-tuning with CYB significantly outperforms standard cross-entropy, reducing perplexity and enhancing downstream accuracy with no additional computational or memory cost.