Datasets:
pretty_name: Evaluation benchmark (Lean proof candidates)
language:
- en
task_categories:
- text-classification
tags:
- lean
- lean4
- theorem-proving
- formal-verification
- code
configs:
- config_name: default
data_files:
- split: test
path: data/test-00000-of-00001.parquet
Evaluation benchmark
The paper's evaluation benchmark for learned Lean 4 evaluators contains 68,932 deduplicated candidate programs from three public provers. Each row contains a complete source program and the acceptance label obtained with Lean v4.15.0.
| Subset | Programs | Valid |
|---|---|---|
| miniF2F | 41,638 | 25,702 (61.7%) |
| ProofNet | 27,294 | 2,443 (9.0%) |
| Combined | 68,932 | 28,145 (40.8%) |
The remaining 40,787 programs are invalid. Candidates come from Pythagoras-Prover-4B, Goedel-Prover-V2-8B, and DeepSeek-Prover-V2-7B, each run on miniF2F and ProofNet. This benchmark is separate from the training corpus and the 27,210-program in-house validation set used to select thresholds and the self-consistency voting rule.
Labels and collection
passed maps to label 1; failed and wrong_declaration map to 0.
Timeouts, infrastructure exceptions, not_run, and precheck failures are
excluded. Labels reflect the collection verifier's acceptance policy,
including expected-theorem matching and forbidden constructs, rather than
successful elaboration of an arbitrary statement alone.
Programs are deduplicated by (source, lean_version) and conflicting verdicts
are removed. The collection uses a 180-second per-command timeout, 64
concurrent verification jobs, and reuse of loaded Lean environments.
miniF2F and ProofNet validity rates differ substantially; report metrics per
benchmark as well as pooled results. The release does not assert that theorem
statements are disjoint from all evaluator training material.
Columns
| Column | Meaning |
|---|---|
run_id |
Source prover evaluation run |
benchmark |
minif2f or proofnet |
prover_model |
Prover that generated the candidate |
lean_version |
Toolchain used for the reference verdict |
problem |
Benchmark problem identifier |
sample_id |
Generator sample identifier, when available |
candidate_index |
Candidate position within its source run |
source |
Complete unannotated Lean source |
label |
1 for valid, 0 for invalid |
status |
passed, failed, or wrong_declaration |
error_kind |
Error category for invalid candidates; null for valid candidates |
Load
from datasets import load_dataset
benchmark = load_dataset("EPFL-LARA/lean-evaluation-benchmark", split="test")
source = benchmark[0]["source"]
label = benchmark[0]["label"]
Use source as the model input and reserve label, status, and
error_kind for evaluation.
Results reported in the paper
| Evaluator | Accuracy | Macro F1 | MCC | AUROC |
|---|---|---|---|---|
| Encoder: ModernBERT-large | 0.586 | 0.563 | 0.372 | 0.862 |
| One-token 8B | 0.752 | 0.752 | 0.592 | 0.905 |
| One-token 8B, calibrated threshold 0.86 | 0.831 | 0.831 | 0.683 | 0.905 |
| Feedback 4B, greedy | 0.851 | 0.851 | 0.733 | — |
| Cascade: one-token → greedy feedback | 0.878 | 0.877 | 0.772 | — |
| Cascade with 5-sample feedback, 4 valid votes required | 0.888 | 0.887 | 0.783 | — |
Default classifier and gate thresholds are 0.5. Calibrated thresholds and the four-of-five self-consistency rule are selected on the separate in-house validation set. Self-consistency uses five traces at temperature 0.8. See the model cards for exact prompts, parsing rules, and serving settings.
Integrity
SHA-256 of data/test-00000-of-00001.parquet:
5a59f709b1466d53c72412cb8c7b7bb53cadd131b3a5b4ce8596677f70e2b3aa
Paper and related artifacts
This is the evaluation benchmark described in Learning Lean Feedback for Efficient Proof-Candidate Evaluation on GPUs.