myahioAnthropic's original take home assignment open sourced
I consider myself rather smart and good at what I do. It's nice to have a look at problems like these once in a while, to remind myself of how little I know, and how much closer I am to the average than to the top.
Generate instructions for their simulator to compute some numbers (hashes) in whatever is considered the memory of their "machine"¹. I didn't see any places where they actually disallow cheating b/c it says they only check the final state of the memory² so seems like if you know the final state you could just "load" the final state into memory. The cycle count is supposedly the LLM figuring out the fewest number of instructions to compute the final state but again, it's not clear what they're actually measuring b/c if you know the final state you can cheat & there is no way to tell how they're prompting the LLM to avoid the answers leaking into the prompt.
¹https://github.com/anthropics/original_performance_takehome/...
²https://github.com/anthropics/original_performance_takehome/...
My point still stands. I don't know what the LLM is doing so my guess is it's cheating unless there is evidence to the contrary.
Why do you assume it’s cheating?
Because it's a well know failure mode of neural networks & scalar valued optimization problems in general: https://www.nature.com/articles/s42256-020-00257-z
Shortcut learning in deep neural networks - Nature Machine Intelligence
Deep learning has resulted in impressive achievements, but under what circumstances does it fail, and why? The authors propose that its failures are a consequence of shortcut learning, a common characteristic across biological and artificial systems in which strategies that appear to have solved a problem fail unexpectedly under different circumstances.