RS-DPO: A Hybrid Rejection Sampling and Direct Preference Optimization Method for Alignment of Large Language Models (2402.10038v2)

Abstract: Reinforcement learning from human feedback (RLHF) has been extensively employed to align LLMs with user intent. However, proximal policy optimization (PPO) based RLHF is occasionally unstable requiring significant hyperparameter finetuning, and computationally expensive to maximize the estimated reward during alignment. Recently, direct preference optimization (DPO) is proposed to address those challenges. However, DPO relies on contrastive responses generated from human annotator and alternative LLM, instead of the policy model, limiting the effectiveness of the RLHF. In this paper, we addresses both challenges by systematically combining rejection sampling (RS) and DPO. Our proposed method, RS-DPO, initiates with the development of a supervised fine-tuned policy model (SFT). A varied set of k responses per prompt are sampled directly from the SFT model. RS-DPO identifies pairs of contrastive samples based on their reward distribution. Finally, we apply DPO with the contrastive samples to align the model to human preference. Our experiments indicate that our proposed method effectively fine-tunes LLMs with limited resource environments, leading to improved alignment with user intent. Furthermore, it outperforms existing methods, including RS, PPO, and DPO.

• The paper proposes RS-DPO, a hybrid approach that integrates rejection sampling with direct preference optimization for aligning LLMs.
• It leverages a fine-tuned policy model and diverse response sampling to address RLHF challenges including instability and high computational costs.
• Experimental results demonstrate that RS-DPO outperforms RS, PPO, and DPO, achieving more stable and efficient alignment in resource-constrained settings.

The paper "RS-DPO: A Hybrid Rejection Sampling and Direct Preference Optimization Method for Alignment of LLMs" explores the challenges and advancements in the domain of aligning LLMs with user intent through reinforcement learning from human feedback (RLHF). Commonly, Proximal Policy Optimization (PPO) is utilized for RLHF but it presents issues of instability, need for significant hyperparameter tuning, and high computational cost during the alignment phase.

To address these obstacles, Direct Preference Optimization (DPO) has been proposed as an alternative technique. However, DPO itself has limitations as it relies on contrastive responses generated from human annotators and alternative LLMs rather than the policy model, which hampers its effectiveness.

The authors of the paper propose a novel hybrid approach termed RS-DPO, which integrates Rejection Sampling (RS) with DPO to leverage the strengths of both methods. The process begins with training a supervised fine-tuned policy model (SFT). From this SFT model, a diverse set of $k$ responses are sampled for each prompt. Rejection Sampling is then employed to identify pairs of contrastive samples based on their reward distribution. Ultimately, DPO is applied to these selected samples to align the LLM with human preferences.

Experimental results presented in the paper demonstrate that RS-DPO is effective in fine-tuning LLMs, even in resource-constrained environments. It showcases superior performance compared to existing methods including RS, PPO, and DPO. The hybrid method thus offers a more stable and computationally efficient approach for the alignment of LLMs, leading to improved adherence to user intent with reduced resource expenditure.