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View Code? Open in Web Editor NEWLarge language models for PDDL domains
Large language models for PDDL domains
and try both
Unit tests taking 7 minutes yikes
It is hard for me to figure out how to run it successfully
Thank you!
I'm running into a weird case where hashing on my macOS laptop is different from hashing on the ubuntu cluster, even with PYTHONHASHSEED = 0 set on both. we should just change the way that LLM cache files are constructed to not use the built-in hash.
to see whether careful prompt engineering can succeed
and put them in order for the goal
to allow for prompting on a more complicated problem
main question will be whether the model is capable of reasoning through what is on the bottom.
to prevent "overfitting" in cases where the object names are the same in the train and test problems
rather than one query with num_completions set.
this matters because of the token rate limit
to save from redundant file reading
these are: airplane, openstack, parcprinter, psr-small
right now, the timeouts are only for searching within planning, not for grounding
if we continue to lean in the generalized planning direction
but note that PG3 has privileged access to the operators
currently the node results are favoring pyperplan, which is weird. see if pyperplan and FD match when the algorithm is the same.
currently we're doing ontable(b1)
but we should be doing (ontable b1)
. probably won't make a difference, but good to be consistent with standard PDDL. we'll have to check if this makes a difference.
would lead to more stability in testing, and could potentially should a greater difference between ours and baselines
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