4 ms·
S1: Simple Test-Time Scaling
- randomcatuser 2y agoreally cool! i wonder what happens when you teach it to use tools inside the reasoning as well. could be even better!
- mncharity 2y ago> This is similar to the "Superficial Alignment Hypothesis" presented in LIMA (Zhou et al., 2023), where the authors find that 1,000 examples can be sufficient to align a model to adhere to user preferences. Link: LIMA: Less Is More for Alignment https://proceedings.neurips.cc/paper_files/paper/2023/file/ac662d74829e4407ce1d126477f4a03a-Paper-Conference.pdf https://proceedings.neurips.cc/paper_files/paper/2023/file/a... , 1k cites: https://scholar.google.com/scholar?cites=16428434404746917806&as_sdt=5,33&sciodt=0,33&hl=en https://scholar.google.com/scholar?cites=1642843440474691780...
- artifishy_intel 2y agoThe diff between budget forcing on and off is all within the (surprisingly large) confidence intervals of evaluation datasets. Why spend more compute for no significant gain? Seems to distract from the high-value minimal reasoning ft set Also - In the main/first figure, why are r1 and o1 (the best performing models in Table 1) omitted? If you collect 59K and then pick the best 1K, is it really fair to say your approach is simple? Sifting through 59K examples doesn't seem simple. Good stuff though, cool to see how minimal we can get to distill good models (esp. at the manageable 32 size).