One-shot Entropy Minimization
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.
683889f78c8e3b72170f6412
Zitian Gao
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2025-05-30T06:54:47.238Z
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Zitian Gao
user
zgao3186
683889f78c8e3b72170f6413
Lynx Chen
683889f78c8e3b72170f6414
Joey Zhou
683889f78c8e3b72170f6415
Bryan Dai
One-shot Entropy Minimization
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.
Entropy minimization with one sample and minimal optimization achieves significant performance improvements for large language models.
large language models entropy minimization unlabeled data optimization rule-based reinforcement learning post-training paradigms