Mitigating Reward Hacking in Language Model Alignment through Regularized Best-of-N Sampling

Core Concepts

Regularized Best-of-N (RBoN) sampling is proposed as a method to mitigate reward hacking in language model alignment, by incorporating proximity regularization into the Best-of-N sampling approach.

Abstract

The content discusses the challenge of aligning the behavior of large language models (LLMs) with human preferences, and introduces Regularized Best-of-N (RBoN) sampling as a method to address the reward hacking problem. The key highlights are: Preference learning methods like Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimization (DPO) are susceptible to the reward hacking problem, where optimizing the proxy reward model does not necessarily optimize the true intended objective. Best-of-N (BoN) sampling is a popular decoding-time alignment method, but it is also vulnerable to reward hacking. RBoN is proposed as a variant of BoN that incorporates proximity regularization, similar to the KL divergence term used in RLHF and DPO, to mitigate reward hacking. Two variants of RBoN are introduced: RBoNKL, which uses KL divergence as the proximity regularizer, and RBoNWD, which uses Wasserstein distance. Experiments on the AlpacaFarm dataset show that RBoN outperforms vanilla BoN, especially when the proxy reward model is loosely correlated with the true objective. RBoNWD is also evaluated for generating a pairwise preference dataset for DPO, and the resulting DPO model outperforms one trained on a dataset generated by vanilla BoN.

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Key Insights Distilled From

Regularized Best-of-N Sampling to Mitigate Reward Hacking for Language Model Alignment

by Yuu Jinnai,T... at arxiv.org 04-02-2024

https://arxiv.org/pdf/2404.01054.pdf

Deeper Inquiries

How can the proximity regularization in RBoN be further improved or extended to better mitigate reward hacking

Proximity regularization in RBoN can be further improved or extended by incorporating adaptive regularization techniques. One approach could involve dynamically adjusting the strength of the regularization term based on the performance of the model during training. For example, a reinforcement learning approach could be used to learn the optimal value of β over time, allowing the model to adapt to the changing dynamics of the reward landscape. Additionally, exploring different forms of regularization functions beyond KL divergence and Wasserstein distance could provide more flexibility in mitigating reward hacking. Techniques such as adversarial training or meta-learning could also be explored to enhance the robustness of the proximity regularization in RBoN.

What are the potential limitations or drawbacks of using Wasserstein distance as the proximity regularizer in RBoNWD, and how could they be addressed

One potential limitation of using Wasserstein distance as the proximity regularizer in RBoNWD is the computational complexity involved in computing the exact WD between distributions. This can become prohibitive, especially when dealing with large datasets or complex models. To address this limitation, approximate methods such as using mini-batch approximations or leveraging pre-trained embeddings to reduce the dimensionality of the space could be employed. Additionally, exploring alternative distance metrics that capture the distributional differences effectively while being computationally efficient could be beneficial. Regularization techniques that incorporate a trade-off between model complexity and proximity to the reference model could also help mitigate the drawbacks of using Wasserstein distance.

Given the sensitivity of RBoNKL to the choice of the regularization strength β, are there any techniques or heuristics that could be used to automatically determine the optimal value of β for a given task or dataset

To address the sensitivity of RBoNKL to the choice of the regularization strength β, several techniques or heuristics can be employed to automatically determine the optimal value of β. One approach could involve using grid search or random search over a predefined range of β values to find the optimal setting through cross-validation. Bayesian optimization techniques could also be utilized to efficiently search for the best hyperparameter setting. Additionally, adaptive learning rate schedules or gradient-based optimization methods could be employed to dynamically adjust β during training based on the model's performance on a validation set. Reinforcement learning algorithms could also be explored to learn the optimal β value in an end-to-end manner, allowing the model to adapt to the specific characteristics of the dataset or task.