aiqbench analysis
This research introduces an online learning algorithm that achieves a 1,000x gain in data efficiency for reinforcement learning from human feedback (RLHF) using Gemma LLMs. By incorporating information-directed exploration and an epistemic neural network to model reward uncertainty, the method matches offline training performance with significantly fewer labels. This advancement addresses critical scalability challenges in aligning large language models without requiring massive datasets.
Efficient Exploration at Scale
arXiv:2603.17378v2 Announce Type: replace-cross Abstract: 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 RLHF trained on 200K labels using fewer than 20K labels, representing more than a 10x gain in data efficiency. Extrapolating from our results, we expect our algorithm trained on 1M labels to match offline RLHF trained on 1B labels. This represents a 1,000x gain. To our knowledge, these are the first results to demonstrate that such large improvements are possible.
Abstract: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 RLHF trained on 200K labels using fewer than 20K labels, representing more than a 10x gain in data efficiency. Extrapolating from our results, we expect our algorithm trained on 1M labels to match offline RLHF trained on 1B labels. This represents a 1,000x gain. To our knowledge, these are the first results to demonstrate that such large improvements are possible.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2603.17378 [cs.LG]
(or arXiv:2603.17378v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2603.17378 Focus to learn more arXiv-issued DOI via DataCite
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