PILAF: Optimal Human Preference Sampling for Reward Modeling (2502.04270v1)

Abstract: As LLMs increasingly drive real-world applications, aligning them with human values becomes paramount. Reinforcement Learning from Human Feedback (RLHF) has emerged as a key technique, translating preference data into reward models when oracle human values remain inaccessible. In practice, RLHF mostly relies on approximate reward models, which may not consistently guide the policy toward maximizing the underlying human values. We propose Policy-Interpolated Learning for Aligned Feedback (PILAF), a novel response sampling strategy for preference labeling that explicitly aligns preference learning with maximizing the underlying oracle reward. PILAF is theoretically grounded, demonstrating optimality from both an optimization and a statistical perspective. The method is straightforward to implement and demonstrates strong performance in iterative and online RLHF settings where feedback curation is critical.

An Analytical Overview of "PILAF: Optimal Human Preference Sampling for Reward Modeling"

The paper, "PILAF: Optimal Human Preference Sampling for Reward Modeling," presents a sophisticated approach to enhancing Reinforcement Learning from Human Feedback (RLHF) through an innovative sampling method named PILAF (Policy-Interpolated Learning for Aligned Feedback). This method is designed to optimize the collection of preference data, a crucial aspect that determines the efficacy of reward modeling, an essential process in RLHF for aligning LLMs with human preferences.

The authors begin by identifying a gap in the current RLHF methodologies: the misalignment between reward model training via Maximum Likelihood Estimation (MLE) and the policy optimization goals. This misalignment often leads to inefficiencies, particularly in scenarios where new data needs to be collected mid-training to address off-policy distributional shifts.

Theoretical and Empirical Advancements

The theoretical contribution of this work lies in introducing and analyzing T-PILAF. This variant of PILAF generates responses by balancing the exploration between the policy model and a reference model. The authors provide rigorous proofs showing that T-PILAF aligns the gradient of the MLE-based reward model loss with the gradient of the oracle objective, ensuring that policy updates are effectively directed towards maximizing the true oracle value. From a statistical perspective, the approach reduces variance by ensuring that gathered preference samples align with the steepest direction of the oracle objective, thereby enhancing training stability.

Empirically, the authors implement PILAF — a practical adaptation of T-PILAF — within both iterative and online DPO (Direct Preference Optimization) frameworks. Their extensive experimental analysis, using the Skywork-Llama-3.1-8B model as a stand-in for human feedback, demonstrates that PILAF not only achieves superior alignment in terms of higher oracle-measured rewards and lower KL divergences from the reference model but also reduces computational and annotation costs by over 40%.

Implications and Future Directions

The core contribution of PILAF lies in its potential to significantly improve the sample efficiency of RLHF pipelines, which is critical given the high cost associated with expert preference labeling. The theoretical guarantees provided ensure that the method is robust across a range of scenarios, potentially making it a versatile tool for various applications of preference learning in LLMs.

Furthermore, the authors propose that their method could be extended beyond DPO to other RLHF paradigms such as Proximal Policy Optimization (PPO). This suggests a broad applicability of PILAF across different RLHF architectures, potentially leading to more resource-efficient and robustly aligned LLMs.

As for future work, the paper points towards empirical evaluations with larger models and real human feedback, which would further substantiate the efficacy and scalability of the approach. Exploring PILAF's integration with newer RLHF frameworks could yield additional insights into its versatility and performance in more complex alignment tasks.

This paper contributes a significant step in advancing the methodologies surrounding data collection in RLHF, providing a theoretically sound and empirically validated method that aligns reward modeling more closely with human values.