Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-Constraint (2312.11456v4)

Abstract: This paper studies the alignment process of generative models with Reinforcement Learning from Human Feedback (RLHF). We first identify the primary challenges of existing popular methods like offline PPO and offline DPO as lacking in strategical exploration of the environment. Then, to understand the mathematical principle of RLHF, we consider a standard mathematical formulation, the reverse-KL regularized contextual bandit for RLHF. Despite its widespread practical application, a rigorous theoretical analysis of this formulation remains open. We investigate its behavior in three distinct settings -- offline, online, and hybrid -- and propose efficient algorithms with finite-sample theoretical guarantees. Moving towards practical applications, our framework, with a robust approximation of the information-theoretical policy improvement oracle, naturally gives rise to several novel RLHF algorithms. This includes an iterative version of the Direct Preference Optimization (DPO) algorithm for online settings, and a multi-step rejection sampling strategy for offline scenarios. Our empirical evaluations on real-world alignment experiment of LLM demonstrate that these proposed methods significantly surpass existing strong baselines, such as DPO and Rejection Sampling Optimization (RSO), showcasing the connections between solid theoretical foundations and their potent practical implementations.

• The paper introduces iterative algorithms for RLHF under KL constraints, providing finite-sample theoretical guarantees.
• It formulates RLHF as a reverse-KL regularized contextual bandit problem applicable in offline, online, and hybrid settings.
• Empirical results demonstrate that the proposed approach outperforms baselines like DPO and RSO in large language model alignment tasks.

An Expert Review of "Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-Constraint"

The paper "Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-Constraint" presents a detailed exploration of a theoretical framework meant to optimize the alignment of generative models with human preferences through Reinforcement Learning from Human Feedback (RLHF). The research addresses the reverse-KL regularized contextual bandit problem, a common yet theoretically underexplored approach in RLHF, analyzing it within offline, online, and hybrid contexts. This paper also introduces algorithms with finite-sample theoretical guarantees, highlighting their efficiency and empirical superiority over traditional baselines in LLM alignment tasks.

Theoretical Framework and Settings

The paper rigorously formalizes RLHF as a reverse-KL regularized contextual bandit problem. This mathematical formulation involves an expectation over states sampled from a distribution and actions governed by policies, with a regularization term based on Kullback-Leibler divergence that ensures policies do not deviate excessively from a pre-trained policy, denoted as the starting checkpoint.

Three distinct settings are considered:

• Offline Learning focuses on deriving policy from pre-existing datasets without new interactions.
• Online Learning exploits ongoing interactions to refine policies continuously.
• Hybrid Learning combines offline initialization with online data collection to optimize policy updates iteratively.

Empirical and Theoretical Contributions

The paper introduces algorithms for all three settings, embedding core concepts like pessimism and optimism to address spurious correlation challenges inherent in preference-based learning. In offline settings, a pessimistic reward model corrects for potential over-optimism inherent in theoretic models, while in online settings, a dual-agent strategy facilitates expansive exploration. This dual-agent framework effectively separates exploration and exploitation roles between two iteratively improving policies, enhancing learning efficiency and robust policy learning.

Importantly, this paper not only provides a theorized convergence guarantee but ties it to practical applications. The proposed algorithms are empirically validated through a series of real-world LLM alignment experiments, where new models consistently outperform state-of-the-art baselines like Direct Preference Optimization (DPO) and Rejection Sampling Optimization (RSO).

Implications and Future Directions

The intersection of theory and empirical validation provides new insights into RLHF, encouraging further exploration of KL-constraint optimization in practical settings. Although traditional methods such as DPO effectively address preference modeling without explicit reward modeling, this research suggests that iterative learning with enriched feedback datasets significantly enhances performance, reducing issues like reward hacking. This advances our understanding of effective policy optimization amid imperfect feedback settings, as commonly encountered in real-world AI systems.

Future research might explore exploring additional settings or extending the hybrid framework with more complex recursive feedback loops, optimizing the trade-off between computational efficiency and the richness of human-like model behavior. Additionally, refining uncertainty estimation for both offline and online settings can profoundly enhance policy robustness, offering exciting prospects for deploying more adaptive and accurate LLMs.

Conclusion

This research contributes a theoretically grounded, empirically validated advancement to RLHF methodologies, emphasizing iterative techniques and preference-based learning for generative models. The introduced algorithms encapsulate a sophisticated interplay of randomness, respect for initial distribution, and informed preference learning—a step forward in refining AI alignment with humanistic values and expectations.