Efficient Exploration at Scale
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. Several features enable the efficiency gains: a small affirmative nudge added to each reinforcement signal, an epistemic neural network that models reward uncertainty, and information-directed exploration. With Gemma large language models (LLMs), our algorithm matches the performance of offline RLH...
| Authors | Seyed Mohammad Asghari, Chris Chute, Vikranth Dwaracherla et al. |
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| Primary category | cs.LG |
| Published | 18 March 2026 |
| Last updated at source | 20 August 2026 |
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