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DeepSeek-Prover-V1.5: Harnessing Proof Assistant Feedback for Reinforcement Learning and Monte-Carlo Tree Search research paper by DeepSeek, 2024

DeepSeek · Aug 15, 2024 · Retrieval and data · 207 citations · 63 upvotes · unverified

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What it shows

DeepSeek-Prover-V1.5 improves theorem proving by optimizing training and inference, utilizing reinforcement learning, and proposing RMaxTS for diverse proof paths, achieving state-of-the-art results on miniF2F and ProofNet benchmarks.

UnverifiedHugging Face's summary; not yet checked by hand.

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About this paper
Authors
Huajian Xin, Z. Z. Ren, Junxiao Song and 14 more
arXiv
2408.08152 · PDF
Venue
arXiv.org
Citations
207, 28 influential · Semantic Scholar
Upvotes
63 · Hugging Face
Code
github.com/deepseek-ai/deepseek-prover-v1.5
Lab
DeepSeek · on Companies

Changes

What changed
Influential citationsfirst count: 28Sep 25, 2026
Citationsfirst count: 207Sep 25, 2026
New paperFound by the weekly scan, unverifiedSep 25, 2026

Sources: each lab's own papers and arXiv, with citation and upvote counts from Semantic Scholar and Hugging Face. One-line summaries are for orientation, not a substitute for the paper. Logos via logo.dev; trademarks belong to their owners.

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