RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment (2304.06767v4)

Abstract: Generative foundation models are susceptible to implicit biases that can arise from extensive unsupervised training data. Such biases can produce suboptimal samples, skewed outcomes, and unfairness, with potentially serious consequences. Consequently, aligning these models with human ethics and preferences is an essential step toward ensuring their responsible and effective deployment in real-world applications. Prior research has primarily employed Reinforcement Learning from Human Feedback (RLHF) to address this problem, where generative models are fine-tuned with RL algorithms guided by a human-feedback-informed reward model. However, the inefficiencies and instabilities associated with RL algorithms frequently present substantial obstacles to the successful alignment, necessitating the development of a more robust and streamlined approach. To this end, we introduce a new framework, Reward rAnked FineTuning (RAFT), designed to align generative models effectively. Utilizing a reward model and a sufficient number of samples, our approach selects the high-quality samples, discarding those that exhibit undesired behavior, and subsequently enhancing the model by fine-tuning on these filtered samples. Our studies show that RAFT can effectively improve the model performance in both reward learning and other automated metrics in both LLMs and diffusion models.

• The paper presents RAFT, a framework that ranks model-generated samples based on rewards to achieve robust alignment with human preferences.
• RAFT improves stability and efficiency by decoupling sample generation from optimization, significantly reducing GPU memory needs compared to traditional RL methods.
• Empirical results show RAFT maintains language fluency and outperforms SFT and PPO in mean rewards and diversity metrics across LLM and diffusion model tasks.

An Analysis of "RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment"

The paper "RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment" addresses a pertinent challenge in the domain of AI: the alignment of generative foundation models with human ethics and preferences. Traditionally reliant on Reinforcement Learning from Human Feedback (RLHF), this adroitly proposed framework, termed Reward rAnked FineTuning (RAFT), seeks to enhance model alignment with improved stability and simplicity over conventional RL methods.

Core Contributions

RAFT introduces an innovative approach by ranking model-generated samples based on a reward function and subsequently fine-tuning the model using high-quality samples. The primary contributions of RAFT lie in its simplicity, computational efficiency, and flexibility:

1. Stability and Robustness: RAFT utilizes a fine-tuning strategy akin to supervised learning, circumventing the instabilities often associated with RL algorithms. This ensures a streamlined process with fewer hyper-parameters, thus facilitating easier implementation and adjustment.
2. Efficient Resource Utilization: Unlike RL algorithms such as PPO that have significant computational overhead due to their requirement to load multiple LLMs concurrently, RAFT decouples sample generation from model optimization, thus minimizing GPU memory requirements.
3. Broad Applicability: RAFT’s versatility extends across various generative models, including both LLMs and diffusion models, provided a suitable reward model is available.
4. Clear Preference Objectives: The framework prioritizes high-reward samples, thus mitigating reward hacking through transparency and interpretability of the training data.

Empirical Evaluation

The empirical paper benchmarks RAFT against the PPO within the context of LLMs, specifically employing the LLaMA-7B model and leveraging the HH-RLHF dataset. RAFT demonstrated superior ability to maintain model language fluency while achieving higher mean rewards indicative of model alignment success. Notably, the RAFT-aligned models outperformed the SFT baseline and PPO across various diversity metrics, without sacrificing complexity. It also suggested robustness against typical complications such as reward noise.

In diffusion model experiments, RAFT was effective in two domains: enhancing resolution capability and aligning text-to-image generation. The capability to adapt model resolution and align images more accurately with contextual prompts further corroborates the efficacy and adaptability of RAFT in visual tasks.

Implications and Future Directions

By proposing RAFT, the authors illuminate practical paths to achieving a balance between model performance and alignment with human feedback. The paper implicitly suggests the potential for extending RAFT to other domains in AI where ethical alignment is crucial. Furthermore, the decoupled nature of RAFT presents opportunities to integrate supplementary data sources and advanced generation techniques, thereby improving inference quality.

The paper opens several avenues for future research. An immediate step is to explore the integration of more sophisticated reward functions, potentially leveraging insights from continual learning and meta-learning. Also, considerations of scale and real-world deployment scenarios could further reveal the strengths and limitations of RAFT.

Conclusion

"RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment" provides a methodologically sound and pragmatic approach to the optimization of generative foundation models. Its design reflects a balance between simplicity and performance, offering a robust alternative to incumbent RLHF techniques. As LLMs and diffusion models continue to advance, RAFT proposes a viable mechanism to ensure these models increasingly align with human ethical expectations and social values.