Correcting the Mythos of KL-Regularization: Direct Alignment without Overoptimization via Chi-Squared Preference Optimization (2407.13399v2)

Abstract: LLM alignment methods, such as reinforcement learning from human feedback (RLHF), have led to impressive advances in LLM capabilities, but existing techniques are limited by a widely observed phenomenon known as overoptimization, where the quality of the LLM plateaus or degrades over the course of the alignment process. Overoptimization is often attributed to overfitting to an inaccurate reward model, and while it can be mitigated through online data collection, this is infeasible in many settings. This raises a fundamental question: Do existing offline alignment algorithms make the most of the data they have, or can their sample-efficiency be improved further? We address this question with a new algorithm for offline alignment, $\chi^2$-Preference Optimization ($\chi$PO). $\chi$PO is a one-line change to Direct Preference Optimization (DPO; Rafailov et al., 2023), which only involves modifying the logarithmic link function in the DPO objective. Despite this minimal change, $\chi$PO implicitly implements the principle of pessimism in the face of uncertainty via regularization with the $\chi^2$-divergence -- which quantifies uncertainty more effectively than KL-regularization -- and provably alleviates overoptimization, achieving sample-complexity guarantees based on single-policy concentrability -- the gold standard in offline reinforcement learning. $\chi$PO's simplicity and strong guarantees make it the first practical and general-purpose offline alignment algorithm that is provably robust to overoptimization.

A Comprehensive Analysis of KL-Regularization Alternatives in LLM Alignment

This paper introduces a novel algorithmic approach to address the challenge of overoptimization in LLM alignment. The overoptimization phenomenon often arises during the alignment process, where the quality of the LLM plateaus or degrades. This work critiques the reliance on KL-regularization in existing methods and proposes a minimalist yet effective alternative leveraging $$-divergence, resulting in the algorithm that is simple, efficient, and provably robust to overoptimization.</p> <h3 class='paper-heading'>Background and Motivation</h3> <p>Alignment methods like <a href="https://www.emergentmind.com/topics/reinforcement-learning-from-human-feedback-rlhf" title="" rel="nofollow" data-turbo="false" class="assistant-link">RLHF</a> transform <a href="https://www.emergentmind.com/topics/audio-text-large-language-models-llms" title="" rel="nofollow" data-turbo="false" class="assistant-link">LLMs</a> into policies governed by human feedback-derived reward models. Despite advancements, these methods experience reward overoptimization due to inaccuracies in the reward functions and limited data coverage. Overoptimization leads the policy to diverge from the high-quality states defined by the offline dataset to states with poor reward model generalization. Traditional DRM methods apply KL-divergence to regularize policy updates but are fundamentally limited by not appropriately bounding the induced distributional shift from the reference policy$$\piref$$.</p> <h3 class='paper-heading'>Core Contributions</h3> <p><strong>Algorithm Design</strong>: At the heart of the paper is the introduction of$$-divergence in lieu of the KL-divergence within the optimization framework. The authors argue that$-divergence more effectively quantifies and penalizes off-manifold behavior, aligning the learned policy&#39;s exploration with regions of the state space that the reward model can accurately evaluate.</p> <p><strong>Framework &amp; Implementation</strong>: The proposed algorithm implements a simple but impactful modification to the <a href="https://www.emergentmind.com/topics/direct-preference-optimization" title="" rel="nofollow" data-turbo="false" class="assistant-link">Direct Preference Optimization</a> (DPO) technique. By altering the link function, the framework directly incorporates a pessimism principle, bringing strong theoretical guarantees. The algorithm deviates minimally from existing implementation structures, ensuring ease of adoption and scalability.</p> <p><strong>Theoretical Guarantees</strong>: The paper provides comprehensive theoretical analyses, demonstrating that the algorithm achieves sample complexity guarantees grounded on single-policy concentrability. These guarantees reflect robustness to overoptimization, signaling meaningful sample efficiency improvements over past methods.</p> <h3 class='paper-heading'>Results and Implications</h3> <p>The modification addresses key inefficiencies in offline alignment and presents a framework that is robust, simple, and effective for general-purpose LLM alignment. The tuning mechanisms for the regularization coefficient $\beta$$provide pathways for balancing bias and variance optimally. The empirical section highlights the algorithm’s benefits, achieving better bias-overfitting trade-offs against unpredictable reward model accuracies.</p> <p>Considering future applications, the insights and techniques formulated here extend beyond simple LLM alignment. The paper sets a precedent for incorporating$$-divergence into broader RL settings where offline or self-supervised alignment criteria prevail. Additionally, the paradigm shift offered by explicitly integrating$$-regularization highlights a trend towards uncertainty-aware algorithms in empirical ML frameworks.</p> <h3 class='paper-heading'>Critique and Future Directions</h3> <p>One notable implication is how overoptimization could be tackled in scenarios beyond offline RLHF, especially when adaptive or continuous feedback mechanisms are impractical. Future directions could explore hybrid approaches merging online exploration strategies with offline robust learning paradigms or apply these methods in semi-offline contexts where exploration through proxy signals is feasible.</p> <p>In synthesis, this paper profoundly alters the ongoing dialogue concerning offline RLHF methodologies, presenting an efficient, direct intervention that promises greater assurance against model degradation during the alignment process. Such work solidifies the theoretical and empirical basis for leveraging$$-divergence within offline RL, framing a new avenue for in-depth exploration around data-efficient, principled alignment algorithms for large-scale LLMs.