Paper, researchers, and primary source
Major lab research / reasoning
DeepSeek-Prover-V1.5 trains formal theorem-proving models with proof-assistant feedback and uses tree search to improve proof discovery at inference time.
Contribution 1
Combined supervised formal-proof data with reinforcement learning from Lean verification.
Contribution 2
Adapted Monte Carlo tree search to explore proof states and model suggestions.
Contribution 3
Evaluated pass rates on formal mathematics benchmarks with verifiable outcomes.
Research context
reasoning / 2024
DeepSeek-Prover-V1.5: Harnessing Proof Assistant Feedback for Reinforcement Learning and Monte-Carlo Tree Search places deepseek prover inside the broader reasoning discussion at DeepSeek-AI, with formal verification supplying a second analytical lens. The paper's through-line contains three reported moves: Combined supervised formal-proof data with reinforcement learning from Lean verification; Adapted Monte Carlo tree search to explore proof states and model suggestions; and Evaluated pass rates on formal mathematics benchmarks with verifiable outcomes. That sequence keeps lean tied to the reported work instead of treating it as a free-standing promise. The practical stake is equally bounded: proof assistants provide exact feedback that can supervise search, though benchmark coverage and formalization quality bound what the system proves.
Methods and evidence reading
1 cataloged method notes
Method 1
The experimental design in DeepSeek-Prover-V1.5: Harnessing Proof Assistant Feedback for Reinforcement Learning and Monte-Carlo Tree Search tests combined supervised formal-proof data with reinforcement learning from lean verification and adapted monte carlo tree search to explore proof states and model suggestions against the paper's documented baselines, datasets, model variants, or systems workloads.
How to read the evidence
Evidence for DeepSeek-Prover-V1.5: Harnessing Proof Assistant Feedback for Reinforcement Learning and Monte-Carlo Tree Search is best read as a scoped argument rather than a universal verdict. The source record contains two linked moves: Combined supervised formal-proof data with reinforcement learning from Lean verification; and Adapted Monte Carlo tree search to explore proof states and model suggestions. Its reported outcome is: Evaluated pass rates on formal mathematics benchmarks with verifiable outcomes. The defensible takeaway remains proof assistants provide exact feedback that can supervise search, though benchmark coverage and formalization quality bound what the system proves. That conclusion must travel with the recorded boundary that the empirical reach of DeepSeek-Prover-V1.5: Harnessing Proof Assistant Feedback for Reinforcement Learning and Monte-Carlo Tree Search stops at verifier behavior, benchmark tasks, answer extraction, sampling policy, contamination controls, and prompt design; broader formal verification use therefore requires fresh measurements. A replication should preserve the disclosed setup and test whether deepseek prover still holds when formal verification conditions change. Replication of DeepSeek-Prover-V1.5: Harnessing Proof Assistant Feedback for Reinforcement Learning and Monte-Carlo Tree Search should version the formal verification setup, retain deepseek prover controls, and record failures connected to Adapted Monte Carlo tree search to explore proof states and model suggestions rather than only successful averages.
Findings in the source record
1 paper-specific findings
- The reported evidence in DeepSeek-Prover-V1.5: Harnessing Proof Assistant Feedback for Reinforcement Learning and Monte-Carlo Tree Search supports evaluated pass rates on formal mathematics benchmarks with verifiable outcomes.
Practical implication for AI builders
DeepSeek-AI / 2024
Proposed BrokenGPT application
Research blueprint / proposed status
Proposed route placement / deepseek prover
For a proposed BrokenGPT experiment based on DeepSeek-Prover-V1.5: Harnessing Proof Assistant Feedback for Reinforcement Learning and Monte-Carlo Tree Search, prototype a Lean-backed reasoning tool where generated proof steps run in isolation, failed branches remain inspectable, and compute budgets cap tree search. Keep the deepseek prover path isolated, versioned, and attributable to this research record.
Proposed acceptance test / formal verification
Validate the proposed deepseek prover route against the paper's reported outcome: Evaluated pass rates on formal mathematics benchmarks with verifiable outcomes. The acceptance record for DeepSeek-Prover-V1.5: Harnessing Proof Assistant Feedback for Reinforcement Learning and Monte-Carlo Tree Search should pair stability across samples, answer correctness, and verifier agreement with separate formal verification failures, preventing one deepseek prover average from settling the decision.
Proposed decision boundary / lean
Balance inference compute, faithfulness, and unresolved errors before promoting the proposed lean design. Because A deployment review should isolate language changes, open-ended conversations, domain shifts, unfaithful rationales, and unseen problem forms when translating the deepseek prover contribution into a different system, adoption remains conditional on replay under BrokenGPT's selected model, runtime, and policy configuration.
Limitations, verification, and source
Boundaries recorded with the paper
Limitations
- The empirical reach of DeepSeek-Prover-V1.5: Harnessing Proof Assistant Feedback for Reinforcement Learning and Monte-Carlo Tree Search stops at verifier behavior, benchmark tasks, answer extraction, sampling policy, contamination controls, and prompt design; broader formal verification use therefore requires fresh measurements.
- A deployment review should isolate language changes, open-ended conversations, domain shifts, unfaithful rationales, and unseen problem forms when translating the deepseek prover contribution into a different system.
PRIMARY SOURCES
- 01DeepSeek-Prover-V1.5: Harnessing Proof Assistant Feedback for Reinforcement Learning and Monte-Carlo Tree Search
DeepSeek-AI — Primary primary arXiv paper / 15 August 2024 / Huajian Xin, Z. Z. Ren, Junxiao Song, and 14 more