Reinforcement Learning from Human Feedback with Active Queries
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866912227284484096 |
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| author | Ji, Kaixuan He, Jiafan Gu, Quanquan |
| author_facet | Ji, Kaixuan He, Jiafan Gu, Quanquan |
| contents | Aligning large language models (LLM) with human preference plays a key role in building modern generative models and can be achieved by reinforcement learning from human feedback (RLHF). Despite their superior performance, current RLHF approaches often require a large amount of human-labelled preference data, which is expensive to collect. In this paper, inspired by the success of active learning, we address this problem by proposing query-efficient RLHF methods. We first formalize the alignment problem as a contextual dueling bandit problem and design an active-query-based proximal policy optimization (APPO) algorithm with an $\tilde{O}(d^2/Δ)$ instance-dependent regret bound and an $\tilde{O}(d^2/Δ^2)$ query complexity, where $d$ is the dimension of feature space and $Δ$ is the sub-optimality gap over all the contexts. We then propose ADPO, a practical version of our algorithm based on direct preference optimization (DPO) and apply it to fine-tuning LLMs. Our experiments show that ADPO, while only making about half of queries for human preference, matches the performance of the state-of-the-art DPO method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_09401 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Reinforcement Learning from Human Feedback with Active Queries Ji, Kaixuan He, Jiafan Gu, Quanquan Machine Learning Artificial Intelligence Computation and Language Optimization and Control Aligning large language models (LLM) with human preference plays a key role in building modern generative models and can be achieved by reinforcement learning from human feedback (RLHF). Despite their superior performance, current RLHF approaches often require a large amount of human-labelled preference data, which is expensive to collect. In this paper, inspired by the success of active learning, we address this problem by proposing query-efficient RLHF methods. We first formalize the alignment problem as a contextual dueling bandit problem and design an active-query-based proximal policy optimization (APPO) algorithm with an $\tilde{O}(d^2/Δ)$ instance-dependent regret bound and an $\tilde{O}(d^2/Δ^2)$ query complexity, where $d$ is the dimension of feature space and $Δ$ is the sub-optimality gap over all the contexts. We then propose ADPO, a practical version of our algorithm based on direct preference optimization (DPO) and apply it to fine-tuning LLMs. Our experiments show that ADPO, while only making about half of queries for human preference, matches the performance of the state-of-the-art DPO method. |
| title | Reinforcement Learning from Human Feedback with Active Queries |
| topic | Machine Learning Artificial Intelligence Computation and Language Optimization and Control |
| url | https://arxiv.org/abs/2402.09401 |