AI RESEARCH
Efficient Exploration at Scale
arXiv CS.LG
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ArXi:2603.17378v1 Announce Type: new We develop an online learning algorithm that dramatically improves the data efficiency of reinforcement learning from human feedback (RLHF). Our algorithm incrementally updates reward and language models as choice data is received. The reward model is fit to the choice data, while the language model is updated by a variation of reinforce, with reinforcement signals provided by the reward model.