中文Sign up

m-a-p/OProver-32B

🤗 Hugging Face sourcetext-generationapache-2.032.8B params66 GBsafetensors✓ 20 checksumsupdated today
Have the files? Submit a model torrent

If you have the complete model files and permission to share them, replace the example folder path and run this command. It checks your files, creates a torrent, and submits the magnet and checksums to Pirate Face. Keep your torrent client seeding so others can download from peers. Pirate Face does not receive the model files. Get your community key from your account. Read the script first if you like.

curl -fsSL https://pirateface.co/package.sh | bash -s -- --repo m-a-p/OProver-32B ./model-folder
Needs a seeder →

OProver

A unified framework for agentic formal theorem proving in Lean 4.

OProver treats theorem proving as a multi-round refinement loop. Given a target theorem, the prover retrieves top-k compiler-verified proofs from a memory of prior proofs, generates a proof attempt, runs the Lean 4 compiler, and—on failure—revises the attempt using the compiler feedback in the next round. The same retrieval and feedback signals are baked into training, so the training-time interface matches the proving-time interaction.

Highlights

  • State-of-the-art among open-weight whole-proof provers. OProver-32B attains the best Pass@32 on MiniF2F (93.3), ProverBench (58.2), and PutnamBench (11.3), and the second-best on MathOlympiad (22.8) and ProofNet (33.2). Even OProver-8B beats Goedel-Prover-V2-32B on all five benchmarks despite having 4× fewer parameters.
  • Agentic proving is in the policy, not bolted on. Retrieval, multi-turn compiler feedback, and iterative repair are all part of the trained policy. Ablations show feedback is the dominant driver: removing it costs OProver-32B 4.9–7.4 Pass@32 points across benchmarks; removing retrieval on top adds 0.5–1.7 more.
  • Co-evolving prover and corpus. Each post-training iteration runs agentic rollouts; verified proofs are added to OProofs and re-indexed for retrieval, repair trajectories become SFT data, and unresolved hard cases provide RL signal. MiniF2F-Test Pass@32 grows monotonically: OProver-8B 79.5 → 86.2 → 87.0 → 91.8 across rounds; OProver-32B 84.7 → 88.1 → 93.3.

What's in this release

The OProver collection (m-a-p/OProver) bundles the paper, the corpus, and seven model checkpoints covering both training stages and both model sizes:

Repo Stage Size Note
OProver-8B-Base Continued pretraining only 8B Domain-adapted base; before SFT/RL
OProver-32B-Base Continued pretraining only 32B Domain-adapted base; before SFT/RL
OProver-8B-Round1 Post-training Round 1 8B After first SFT+RL iteration
OProver-8B-Round2 Post-training Round 2 8B After second SFT+RL iteration
OProver-32B-Round1 Post-training Round 1 32B After first SFT+RL iteration
OProver-8B Final 8B The 8B prover reported in the paper (Round 3)
OProver-32B Final 32B The 32B prover reported in the paper (Round 2)
OProofs Dataset 6.86M proofs Lean 4 corpus used for CPT, SFT, and the retrieval memory

Use OProver-8B or OProver-32B for proving. The Base / Round-N checkpoints are released for reproducibility and ablation studies.

Dataset: OProofs

OProofs is a large-scale Lean 4 corpus that doubles as the retrieval memory at proving time. It is built from three sources: public Lean resources (NuminaMath-LEAN, Lean-Workbook, Leanabell-FormalStmt, Goedel-Pset, …), large-scale autoformalization + agentic proof synthesis from informal math mined on Common Crawl and GitHub, and traces from OProver's own agentic proving runs.

Training

Two stages, both anchored on OProofs.

1. Continued pretraining (one-time). A 65B-token mixture of formal Lean (≈30%, from OProofs), code (≈20%, OpenCoder), mathematics (≈40%, Nemotron-Math-4-Plus), and long-CoT (≈10%, ProLong-64K). AdamW, peak LR 5e-5, cosine with 3% warmup, batch 512, sequence length 8192. Output: OProver-{8B,32B}-Base.

2. Iterative post-training. Each round runs:

  1. Agentic proving with the current prover on a theorem pool, producing multi-round rollouts conditioned on retrieved proofs and Lean feedback.
  2. SFT on round-level repair examples (s, R, p_{t-1}, f_{t-1}) → p_t, with cross-entropy loss only on the new attempt.
  3. GSPO RL on hard cases (groups with non-trivial pass-rate). Per-round reward r = 0.8 + 0.2·1[format ok] if Lean-verified, else 0; advantages are pooled across the n×R rounds for the same theorem.
  4. Newly verified proofs and repair trajectories are folded back into OProofs and re-indexed into the retrieval memory for the next round.

Results

Pass@32 (n=64) across five Lean 4 benchmarks. Bold is best, underlined is second-best.

OProver-32B reaches three best and two second-best across five benchmarks — the most top placements of any model in the comparison, despite being a 32B dense model versus a 560B MoE or a 671B dense competitor.

Loading

from transformers import AutoModelForCausalLM, AutoTokenizer

# Pick any of: OProver-8B, OProver-32B, *-Base, *-Round1, *-Round2
name = "m-a-p/OProver-8B"
tok = AutoTokenizer.from_pretrained(name)
model = AutoModelForCausalLM.from_pretrained(name, torch_dtype="bfloat16", device_map="auto")

from datasets import load_dataset
ds = load_dataset("m-a-p/OProofs", split="train")

OProver is trained against a multi-round agentic interface: at each round the input includes the target Lean statement, top-k retrieved verified proofs, the prior proof attempt, and the Lean compiler feedback. See the paper §2.1 for the prompt template, and Appendix B for serialization details.

Citation

@article{ma2026oprover,
  title   = {OProver: A Unified Framework for Agentic Formal Theorem Proving},
  author  = {David Ma and Kaijing Ma and Shawn Guo and Yunfeng Shi and Enduo Zhao and Jiajun Shi and Zhaoxiang Zhang and Gavin Cheung and Jiaheng Liu and Zili Wang},
  journal = {arXiv preprint arXiv:2605.17283},
  year    = {2026}
}