Hybrid Preference Optimization for Alignment: Provably Faster Convergence Rates by Combining Offline Preferences with Online Exploration (2412.10616v1)

Abstract: Reinforcement Learning from Human Feedback (RLHF) is currently the leading approach for aligning LLMs with human preferences. Typically, these models rely on extensive offline preference datasets for training. However, offline algorithms impose strict concentrability requirements, which are often difficult to satisfy. On the other hand, while online algorithms can avoid the concentrability issue, pure online exploration could be expensive due to the active preference query cost and real-time implementation overhead. In this paper, we propose a novel approach: Hybrid Preference Optimization (HPO) which combines online exploration with existing offline preferences by relaxing the stringent concentrability conditions for offline exploration, as well as significantly improving the sample efficiency for its online counterpart. We give the first provably optimal theoretical bound for Hybrid RLHF with preference feedback, providing sample complexity bounds for policy optimization with matching lower bounds. Our results yield improved sample efficiency of hybrid RLHF over pure offline and online exploration.

Hybrid Preference Optimization for Alignment: Provably Faster Convergence Rates by Combining Offline Preferences with Online Exploration

The paper addresses a critical challenge in reinforcement learning, particularly the alignment of LLMs with human preferences through Reinforcement Learning from Human Feedback (RLHF). RLHF has demonstrated potential in fine-tuning models with preferences derived from human feedback, allowing for more intuitive and user-friendly AI systems. However, the reliance on extensive offline datasets, while beneficial, comes with constraints, often necessitating stringent assumptions about data concentration and quality. Conversely, online methods, which can circumvent these limitations through active exploration, bring their challenges, such as high costs associated with real-time data collection and processing.

In this context, the researchers introduce the concept of Hybrid Preference Optimization (HPO), a novel approach that synergistically integrates the strengths of both offline and online RLHF paradigms. HPO is designed to exploit existing offline data while simultaneously facilitating efficient online exploration. By relaxing the stringent concentrability conditions typically required for offline data, and improving the sample efficiency of online exploration, HPO aims to provide a more balanced, cost-effective, and sample-efficient method for RLHF.

Contributions and Theoretical Insights

1. HPO Algorithm: The authors propose the first hybrid RLHF algorithm that is both theoretically and practically efficient. HPO leverages the combination of offline preferences and online explorative data to overcome the limitations of both methods when implemented individually.
2. Sample Complexity and SEC Coefficient: Theoretical analysis shows that HPO achieves superior sample complexity compared to either pure offline or online RLHF methods. This is measured using a modified version of the Sequential Exploration Coefficient (SEC), which integrates the offline dataset's coverage metric into the exploration process, leading to reduced sample requirements.
3. Linear MDPs Analysis: The researchers extend their analysis to the linear Markov Decision Process (MDP) framework. This allows for a comparison of HPO's benefits over existing lower bounds for both offline and online RLHF, demonstrating HPO's advantage in terms of sample efficiency when there is non-trivial coverage from offline data.

Implications and Future Directions

The integration of offline and online methods via HPO offers several practical advantages:

• Resource Efficiency: By reducing the need for extensive online feedback, HPO can significantly cut costs associated with real-time data querying and processing.
• Improved Alignment: The approach potentially allows models to align more effectively with human preferences by combining the robustness of offline learning with the adaptability of online exploration.
• Scalability: The reduced requirement for online samples may enable the deployment of RLHF in scenarios previously deemed too resource-intensive, broadening the application of personalized AI systems.

The research opens avenues for further exploration in hybrid RL paradigms. Future work may delve into optimizing the weighting between offline and online data in HPO, enhancing the scalability of activity in larger models, and addressing new, complex environments. Developments in this domain could lead to more generalized solutions that retain efficiency and efficacy across diverse applications. The HPO framework presents a promising step towards more economically viable and flexible AI systems, potentially setting the groundwork for future advancements in reinforcement learning methodologies.