---
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

```python
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*.

- [Encoder (ModernBERT-large)](https://huggingface.co/EPFL-LARA/lean-encoder-modernbert-large)
- [One-token (Qwen3-8B)](https://huggingface.co/EPFL-LARA/lean-one-token-qwen3-8b)
- [Feedback (Qwen3-4B)](https://huggingface.co/EPFL-LARA/lean-feedback-qwen3-4b)