title: One-shot Entropy Minimization
summary: We trained 13,440 large language models and found that entropy minimization
  requires only a single unlabeled data and 10 steps optimization to achieve
  performance improvements comparable to or even greater than those obtained
  using thousands of data and carefully designed rewards in rule-based
  reinforcement learning. This striking result may prompt a rethinking of
  post-training paradigms for large language models. Our code is avaliable at
  https://github.com/zitian-gao/one-shot-em.
paper_authors._id: 683889f78c8e3b72170f6412
paper_authors.name: Zitian Gao
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-30T06:54:47.238Z
paper_authors._id: 641ddac5be3bd3a5a06ed4a4
paper_authors.avatarUrl: /avatars/14969dff861d53b0a75305606495eca7.svg
paper_authors.fullname: zitian gao
paper_authors.type: user
paper_authors.user: zgao3186
paper_authors._id: 683889f78c8e3b72170f6413
paper_authors.name: Lynx Chen
paper_authors._id: 683889f78c8e3b72170f6414
paper_authors.name: Joey Zhou
paper_authors._id: 683889f78c8e3b72170f6415
paper_authors.name: Bryan Dai
paper_title: One-shot Entropy Minimization
paper_summary: We trained 13,440 large language models and found that entropy minimization
  requires only a single unlabeled data and 10 steps optimization to achieve
  performance improvements comparable to or even greater than those obtained
  using thousands of data and carefully designed rewards in rule-based
  reinforcement learning. This striking result may prompt a rethinking of
  post-training paradigms for large language models. Our code is avaliable at
  https://github.com/zitian-gao/one-shot-em.
paper_ai_summary: Entropy minimization with one sample and minimal optimization achieves significant performance improvements for large language models.
paper_ai_keywords: large language models entropy minimization unlabeled data optimization rule-based reinforcement learning post-training paradigms