Doubly Robust Alignment for Large Language Models (2506.01183v1)

Abstract: This paper studies reinforcement learning from human feedback (RLHF) for aligning LLMs with human preferences. While RLHF has demonstrated promising results, many algorithms are highly sensitive to misspecifications in the underlying preference model (e.g., the Bradley-Terry model), the reference policy, or the reward function, resulting in undesirable fine-tuning. To address model misspecification, we propose a doubly robust preference optimization algorithm that remains consistent when either the preference model or the reference policy is correctly specified (without requiring both). Our proposal demonstrates superior and more robust performance than state-of-the-art algorithms, both in theory and in practice. The code is available at https://github.com/DRPO4LLM/DRPO4LLM

Doubly Robust Alignment for LLMs

The paper, Doubly Robust Alignment for LLMs, presents a novel approach to fine-tuning LLMs using reinforcement learning from human feedback (RLHF). This method, called Doubly Robust Preference Optimization (DRPO), is designed to mitigate challenges associated with model misspecification, which have been a significant obstacle in the effective application of RLHF techniques.

Background and Motivation

The alignment of LLMs with human preferences has become a critical area of research, especially as LLMs are employed in more complex tasks requiring nuanced understanding of human values such as helpfulness and honesty. Traditionally, the fine-tuning of LLMs involves RLHF methodologies, which have had considerable success across various domains. However, prevalent issues arise from misspecification in preference models, reward functions, and reference policies. These inaccuracies lead to subpar fine-tuning results, commonly referred to as reward hacking or policy overfitting.

Key Contributions

To address these limitations, the paper introduces a fundamentally new algorithm—Doubly Robust Preference Optimization (DRPO). This approach leverages the doubly robust estimation technique commonly seen in econometrics and causal inference to enhance the robustness of LLM alignment with human preferences. DRPO stands out by ensuring consistent performance even if only one of the preference model or the reference policy is correctly specified. The key contributions are:

1. Doubly Robust Preference Estimation:
   • The method proposes an estimator for preference evaluation that maintains consistency with either the correct specification of the preference model or the reference policy. This is instantiated through a combination of a Direct Method (DM) estimator and an Importance Sampling (IS) estimator.
2. Optimization Algorithm:
   • A new optimization strategy is developed, integrating the doubly robust preference estimator, to fine-tune LLMs. When the Bradley-Terry (BT) model holds, the proposed algorithm exhibits favorable regret bounds, indicating superior and more reliable performance compared to existing PPO- and DPO-based algorithms.
3. Theoretical Insights:
   • The authors provide a thorough theoretical analysis, demonstrating that the proposed estimator achieves semi-parametric efficiency, attaining the lowest possible variance, while also being doubly robust. Suboptimality bounds for the resultant policy confirm that it frequently outperforms competing methods under practical and theoretical scenarios.

Implications and Future Directions

The DRPO framework proposed in this paper has significant implications for the development of more robust and reliable LLMs that better align with nuanced human preferences. By reducing sensitivity to model specification errors, DRPO facilitates the generation of outputs that better meet ethical standards and user expectations. The dual emphasis on theoretical rigor and empirical validation underscores the potential for this method to be a mainstay in future LLM alignment tasks.

Looking forward, further exploration into integrating DRPO with broader preference models, potentially using alternative statistical frameworks, could yield even more flexible and potent alignment methodologies. Additionally, the approach may be extended beyond LLMs to other AI domains where human preference alignment is crucial, adding a layer of trust and dependability in AI systems.

In conclusion, Doubly Robust Alignment for LLMs provides a crucial advancement in refining LLMs while addressing inherent challenges in preference alignment, promising a more stable and efficient integration of human values into AI systems.