Entropy Controllable Direct Preference Optimization (2411.07595v2)

Abstract: In the post-training of LLMs, Reinforcement Learning from Human Feedback (RLHF) is an effective approach to achieve generation aligned with human preferences. Direct Preference Optimization (DPO) allows for policy training with a simple binary cross-entropy loss without a reward model. The objective of DPO is regularized by reverse KL divergence that encourages mode-seeking fitting to the reference policy. Nonetheless, we indicate that minimizing reverse KL divergence could fail to capture a mode of the reference distribution, which may hurt the policy's performance. Based on this observation, we propose a simple modification to DPO, H-DPO, which allows for control over the entropy of the resulting policy, enhancing the distribution's sharpness and thereby enabling mode-seeking fitting more effectively. In our experiments, we show that H-DPO outperformed DPO across various tasks, demonstrating superior results in pass@$k$ evaluations for mathematical tasks. Moreover, H-DPO is simple to implement, requiring only minor modifications to the loss calculation of DPO, which makes it highly practical and promising for wide-ranging applications in the training of LLMs.

• The paper introduces H-DPO, which refines Direct Preference Optimization by enabling entropy control to better align language model outputs with human preferences.
• It presents a streamlined alternative to RLHF, reducing computational demands while enhancing performance on key metrics such as pass@k evaluations.
• Empirical results on tasks like GSM8K demonstrate H-DPO's robustness, offering improved accuracy and diverse outputs with minimal modifications to the original framework.

Entropy Controllable Direct Preference Optimization: An Expert Overview

The paper "Entropy Controllable Direct Preference Optimization" presents an innovative approach to refining the training methodology for LLMs through an adaptation of Direct Preference Optimization (DPO). The primary contribution of this work is the introduction of H-DPO, which modifies the entropy control mechanisms within DPO, thereby enhancing its capability to align model outputs with human preferences more effectively.

Context and Motivation

LLMs have demonstrated significant utility across diverse applications, yet aligning these models to specific user needs remains challenging. Traditional techniques like Reinforcement Learning from Human Feedback (RLHF) rely on learned reward models and complex RL algorithms such as PPO, contributing to extensive computational demands and instability. Direct Preference Optimization offers a streamlined alternative by allowing model alignment using a simplified loss function devoid of reward models. However, it often employs reverse KL divergence for regularization, which is inherently mode-seeking but can sometimes fail in capturing target distributions accurately.

H-DPO – Key Contributions

H-DPO extends DPO by integrating a generalized loss function, augmenting the entropy control via a hyperparameter $\alpha$. This adjustment allows better management of the distribution's sharpness and encourages more effective mode-seeking fitting. The essence of this approach lies in manipulating the entropy term, thereby facilitating tuning between the diversity of model outputs and their adherence to human-preferred outcomes.

Experiments and Results

Empirical evaluations reveal H-DPO's robustness and versatility. By modifying the entropy component, H-DPO achieves superior performance over traditional DPO in key metrics, especially in pass@k evaluations where diversity and coverage are critical. For instance, in mathematical tasks represented in the GSM8K dataset, H-DPO demonstrated increased coverage and accuracy, underscoring its efficacy in producing high-quality and varied outputs even with reduced entropy.

Practical Implications

The simplicity of implementing H-DPO—requiring only minor alterations to DPO's existing framework—positions it as a highly practical solution for enhancing LLM training efficiency and effectiveness. By affording direct control over entropy, H-DPO achieves a desirable trade-off between diversity and performance, thereby addressing one of the lingering challenges in model alignment tasks. This capability is particularly pertinent in the context of open-ended tasks like instruction-following and multi-modal generation.

Future Directions

The paper notes a significant potential for automating the parameter $\alpha$ tuning process, which could streamline the application of H-DPO across various datasets and tasks without manual hyperparameter adjustments. Future explorations might delve into dynamic adjustment mechanisms, possibly employing meta-learning approaches to optimize entropy levels adaptively.

In conclusion, the methodological advancements brought forth by H-DPO pave the way for more efficient and targeted alignment of LLMs with human expectations, reinforcing the ongoing evolution of model fine-tuning practices within the AI research community. The paper makes a substantial contribution to our understanding of entropy's role in preference optimization, suggesting new vistas for enhancing model reliability and user-centricity.