Special thanks to John Schulman for a lot of super valuable feedback and direct edits on this post.
Test time compute (Graves et al. 2016, Ling, et al. 2017, Cobbe et al. 2021) and Chain-of-thought (CoT) (Wei et al. 2022, Nye et al. 2021), have led to significant improvements in model performance, while raising many research questions. This post aims to review recent developments in how to effectively use test-time compute (i.e. “thinking time”) and why it helps.
Enabling models to think for longer can be motivated in a few different ways.
The core idea is deeply connected to how humans think. We humans cannot immediately provide the answer for "What's 12345 times 56789?". Rather, it is natural to spend time pondering and analyzing before getting to the result, especially for complex problems. In Thinking, Fast and Slow (Kahneman, 2013), Daniel Kahneman characterizes human thinking into two modes, through the lens of the dual process theory :
Source: Lilian Weng - Lil'Log — Read the full article on the original site ↗