PACT uses proof artifacts to scale language-model theorem proving

The method extracts self-supervised objectives from kernel-level proof terms and trains them alongside tactic prediction.

What happened?

PACT co-trained a Transformer theorem prover on Lean proof artifacts and improved held-out theorem-proving success from 32% to 48%.

Why it matters

It helped establish that formal libraries contain much more learnable signal than only the next tactic labels exposed at the surface.

Significance rationale: PACT established proof-term-derived self-supervision as a practical way to alleviate data scarcity in neural theorem proving.

Historical context

Later developments

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Related milestones

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Primary evidence

Evidence & status at a glance

Significance
H2 · Field milestone
Verification
Paper released
Evidence level
E3 · Inspectable artifact
AI role
Substantive AI support
Formal assurance
Artifact available

How should these labels be read? →

Contribution & verification

Mathematical novelty
Tooling only
Interface
Formal, Symbolic
AI contribution
The language model predicts proof tactics using representation learned from both tactic labels and proof-artifact objectives.
Human contribution
Researchers designed the artifact objectives, instrumented Lean, and curated the evaluation setting.
Verification
Paper released · E3 · Inspectable artifact · Artifact available
Last reviewed
2026-09-11

Technical details

Systems
pact, lean
Organizations
openai
Problems
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Methods
proof-artifact-cotraining, transformer, tactic-prediction

Sources & artifacts

Verification history

  • 2021-02-11 · Paper released · E2 · Research record

    The Chronicle records this event from the cited primary or official source; the status does not imply independent Chronicle verification.

  • 2026-09-11 · Paper released · E3 · Inspectable artifact

    This corpus refresh added a directly inspectable public artifact maintained by the authors or releasing organization. E3 records artifact-level inspectability and does not imply independent verification of the underlying scientific or mathematical claim.

Corrections

No corrections recorded.