Exploratory Preference Optimization: Harnessing Implicit Q*-Approximation for Sample-Efficient RLHF (2405.21046v1)

Abstract: Reinforcement learning from human feedback (RLHF) has emerged as a central tool for LLM alignment. We consider online exploration in RLHF, which exploits interactive access to human or AI feedback by deliberately encouraging the model to produce diverse, maximally informative responses. By allowing RLHF to confidently stray from the pre-trained model, online exploration offers the possibility of novel, potentially super-human capabilities, but its full potential as a paradigm for LLM training has yet to be realized, owing to computational and statistical bottlenecks in directly adapting existing reinforcement learning techniques. We propose a new algorithm for online exploration in RLHF, Exploratory Preference Optimization (XPO), which is simple and practical -- a one-line change to (online) Direct Preference Optimization (DPO; Rafailov et al., 2023) -- yet enjoys the strongest known provable guarantees and promising empirical performance. XPO augments the DPO objective with a novel and principled exploration bonus, empowering the algorithm to explore outside the support of the initial model and human feedback data. In theory, we show that XPO is provably sample-efficient and converges to a near-optimal LLM policy under natural exploration conditions, irrespective of whether the initial model has good coverage. Our analysis, which builds on the observation that DPO implicitly performs a form of $Q^{\star}$-approximation (or, BeLLMan error minimization), combines previously disparate techniques from LLMing and theoretical reinforcement learning in a serendipitous fashion through the perspective of KL-regularized Markov decision processes. Empirically, we find that XPO is more sample-efficient than non-exploratory DPO variants in a preliminary evaluation.

Exploratory Preference Optimization: Harnessing Implicit $Q^\star$-Approximation for Sample-Efficient RLHF

The paper "Exploratory Preference Optimization: Harnessing Implicit $Q^\star$-Approximation for Sample-Efficient RLHF" addresses the computational and statistical challenges encountered in Reinforcement Learning from Human Feedback (RLHF) when applied to LLMs. The central theme of this work is to enhance the sample efficiency of RLHF through a novel algorithm known as Exploratory Preference Optimization (XPO). XPO augments Direct Preference Optimization (DPO) with an exploration bonus, thereby empowering the model to discover and generate novel, potentially superior responses by exploiting feedback mechanisms more efficiently.

Key Contributions

1. Novel Algorithm for RLHF: The authors propose XPO, which integrates an exploration bonus into the DPO framework, facilitating enhanced exploration beyond the pre-trained model's initial responses. This slight modification to the DPO objective marks a substantial advancement, offering the strongest theoretical guarantees and exhibiting promising empirical performance.
2. Theoretical Guarantees: XPO is shown to be provably sample-efficient. Under standard assumptions, such as policy realizability and bounded density ratios, the algorithm converges to a near-optimal policy using a polynomial number of samples, thus addressing the sample complexity barrier traditionally associated with RLHF.
3. Empirical Validation: Preliminary experiments demonstrate that XPO can achieve performance comparable to existing models while requiring significantly less preference data. The empirical results underscore the practical efficacy of XPO, especially in scenarios demanding online exploration.

Technical Insights

The design of XPO leverages insights from both LLMing and theoretical reinforcement learning:

• Implicit $Q^\star$-Approximation: The paper generalizes the understanding of DPO as performing an implicit form of BeLLMan error minimization. This re-interpretation allows the incorporation of a principled exploration bonus, which is computationally feasible and yet theoretically robust.
• KL-Regularized MDP: The analysis hinges on viewing the problem through the lens of KL-regularized Markov Decision Processes (MDPs), offering a novel perspective that connects these domains effectively.

Theoretical Implications

The authors provide rigorous bounds on the sample complexity of XPO, contending that the algorithm scales polynomially with the coverability coefficient of the policy class. This means that the sample complexity needed to learn a near-optimal policy is significantly reduced compared to previous methods.

An important theoretical contribution is the characterization of the Sequential Extrapolation Coefficient (SEC), which generalizes the exploration guarantees to more complex setups than previous works, including tabular and linear MDPs.

Practical Implications

Practically, XPO is a feasible and efficient enhancement to the current RLHF practices:

• Implementation Simplicity: XPO's integration into existing pipelines requires minimal changes, essentially a one-line modification to the DPO objective.
• Robustness: The ability of XPO to maintain performance with reduced data makes it valuable for real-world applications, where the cost of collecting extensive human feedback can be prohibitive.

Future Directions

The work opens several avenues for further exploration:

• Generalization to Stochastic Dynamics: While XPO currently applies to Deterministic Contextual MDPs, extending it to MDPs with stochastic dynamics could significantly widen its applicability.
• Instance-Dependent Bounds: Deriving tighter sample complexity bounds that are instance-dependent can provide more nuanced insights into the algorithm's efficiency.
• Broader Feedback Modalities: Incorporating more diverse forms of feedback, beyond binary preferences, could enhance the model's learning efficacy and robustness.

Conclusion

This paper advances the field of RLHF by introducing XPO, an algorithm that not only enriches the theoretical understanding of preference optimization in reinforcement learning but also delivers on practical efficiency and simplicity. The delicate balancing of rigorous theoretical underpinnings with empirical validation signifies a substantial step forward in making RLHF more accessible and effective for developing advanced LLMs.