Adaptive Thinking: Large Language Models Know When to Think in Latent Space
Apple's research reveals LLMs can self-optimize thinking budgets—cutting inference costs while maintaining performance.
Why it matters
Apple researchers demonstrate that LLMs can adaptively allocate compute for chain-of-thought reasoning based on query complexity, potentially unlocking significant inference cost savings. This addresses a critical gap in understanding compute-optimal inference across different capability levels.
The key facts
6 to knowTest-time computing capability for intermediate chain-of-thought reasoning
Self-consistency used as proxy for thinking necessity
Adaptive budget allocation based on query complexity
Smooth performance improvements with increased thinking budget
Compute-optimal inference optimization
Published by Apple Machine Learning Research
Go to the source
Apple Machine Learningmachinelearning.apple.com
Publisher excerpt: Recent advances in large language models (LLMs) test-time computing have introduced the capability to perform intermediate chain-of-thought (CoT) reasoning (thinking) before generating answers. While increasing the thinking budget yields smooth performance improvements at inference time, the…