> Rather than standard benchmarks (e.g., math problems), we adopt controllable puzzle environments that let us vary complexity systematically
Very clever, I must say. Kudos to folks who made this particular choice.
> we identify three performance regimes: (1) low complexity tasks where standard models surprisingly outperform LRMs, (2) medium-complexity tasks where additional thinking in LRMs demonstrates advantage, and (3) high-complexity tasks where both models experience complete collapse.
This is fascinating! We need more "mapping" of regimes like this!
What I would love to see (not sure if someone on here has seen anything to this effect) is how these complexity regimes might map to economic value of the task.
For that, the eval needs to go beyond puzzles but the complexity of the tasks still need to be controllable.
Is (1) that surprising? If I ask someone a simple question but tell them to "think really hard about it", they'll be more likely to treat it as a trick question and look for a non-obvious answer. Overthinking it, basically.
It is hard to compare models with humans so not sure how to answer it for both. :)
But, for models, this is an interesting finding because a lot of LRMs are LLMs with a _bunch_ of post-training done on top. We know this about DeepSeek R1 (one of the models evaluated in the Apple paper) for sure. They write extensively about how they took DeepSeek-V3-Base and made R1 with it. [1]
If the post-training is resulting in lower performance on simpler tasks then it ought to inspire more research on how to make it so that it doesn't -- i.e., with more training (of any kind), we should be gaining more capabilities. This has been a problem with DNNs historically, btw. We had these issues when fine-tuning text/image classifiers as well. Some weight changes can be destructive. So, it has to be done with a _lot_ of care. And, I am sure folks are working on it, to be honest. Maybe some of them will say something here. :-)
Very clever, I must say. Kudos to folks who made this particular choice.
> we identify three performance regimes: (1) low complexity tasks where standard models surprisingly outperform LRMs, (2) medium-complexity tasks where additional thinking in LRMs demonstrates advantage, and (3) high-complexity tasks where both models experience complete collapse.
This is fascinating! We need more "mapping" of regimes like this!
What I would love to see (not sure if someone on here has seen anything to this effect) is how these complexity regimes might map to economic value of the task.
For that, the eval needs to go beyond puzzles but the complexity of the tasks still need to be controllable.