Sleep-time Compute: Beyond Inference Scaling at Test-time
Deep Papers
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Optimizing AI Response Through Sleep-Time Compute
This chapter explores the innovative 'sleep-time compute' method, which enhances AI models' real-time responses by leveraging learned context during idle moments. The discussion focuses on the use of heavy reasoning models and lighter agents to synthesize information efficiently, reducing token usage while maintaining accuracy. It also examines the implications of this approach on computational costs and scalability, highlighting considerations for effective implementation.
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