32 lines
1.9 KiB
Markdown
32 lines
1.9 KiB
Markdown
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#
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## Related work
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### Tony's AF
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Ton'y original AF: ((Yuhuai et al.))[https://arxiv.org/abs/2205.12615]
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Tony's paper improve MiniF2F from: `29.6% to 35.2%`, by `5.6%`.
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Expert Iteration:
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- AF used: "We explore if one can improve neural theorem provers by training the neural models on proofs of automatically translated theorems".
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- they only translate **problem theorems** (nl_thm := "problem + answer") then use a prover to get the formal proof.
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- ExpIt algorithn:
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- `M_0 := Isabelle_Thor()`
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- `Search/Prover := Best_First_Search()` # TODO recall best first search
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- ExpIT.fine_tune := "train model to predict next proof_step/tactic given current proof_state and previous proof_step on successful proofs.
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- i.e., `<x=(proof_state_{t}, proof_step_{t-1}), y=(proof_step_{t})>` #TODO: I think, confirm with Albert https://twitter.com/messages/1253358235-1267913180153548800
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Base Model for Neural Theorem Prover (NTP):
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- Thor_GPT2 := "We use a pre-trained and fine-tuned Thor based on a GPT-2 with 700M non-embedding parameters." Note: ReProver used 299M parameters enc-dec.
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- fine-tuned on the PILE arxiv + github
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Neural Theorem Prover (NTP) for `M_0`:
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- Thor :=
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- The Thor agent is fine-tuned on the PISA dataset which consists of 2.49 million proof steps from the Isabelle/HOL library.
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- The model is trained with the objective to predict the next token in va proof step, given the proof state and the last proof step.
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- proof step := "tactic in Isabelle" #TODO confirm with Albert https://twitter.com/messages/1253358235-1267913180153548800
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Questions:
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- Q1: what is this: "we perform deduplication by problem statements" when does it matter? All MATH train are unique, so why would I care about this?
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Idea:
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- Idea1: use the formal ground truth solution string in MATH, implement Draft Sketch Proof (DSP) for Lean4 + use some symbolic/ntp solver (hammer/tidy/ReProver)
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