Redirecting to original paper in 30 seconds...
Click below to go immediately or wait for automatic redirect
📄 Abstract
Abstract: 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.