WARM: On the Benefits of Weight Averaged Reward Models (AI summary)

Key Points

1. Reward modeling in reinforcement learning from human feedback (RLHF) is critical for aligning large language models (LLMs) with human preferences.

2. Reward hacking is a significant issue in RLHF, leading to degraded performances, unreliability of the reward model, and potential safety risks in LLM deployment.

3. Weight Averaged Reward Models (WARM) is proposed as a solution to mitigate reward hacking by efficiently combining multiple RMs to improve reliability under distribution shifts and robustness to label corruption.

4. WARM improves efficiency compared to traditional ensembling of predictions and enhances the overall quality and alignment of LLM predictions.

5. WARM reduces memorization of corrupted samples, improves robustness to label corruption, and maintains reliability under distribution shifts.

6. WARM leverages linear mode connectivity and diverse fine-tunings to achieve its benefits, including reliability and robustness.

7. Empirical experiments on summarization tasks show that WARM outperforms individual RMs and ensembling methods, demonstrating its effectiveness in mitigating reward hacking.

8. WARM's benefits extend to best-of- ? (BoN) sampling experiments, where it improves control rewards and oracle preference metrics.

9. In reinforcement learning experiments, WARM consistently outperforms individual RMs and ensembling methods, showing improved control rewards and oracle preference metrics.

Summary

Introduction of Weight Averaged Reward Models (WARM) in Addressing Reward Hacking and Improving RLHF
The research paper investigates the challenges of reward modeling and hacking in reinforcement learning from human feedback (RLHF). It introduces Weight Averaged Reward Models (WARM) as a solution to mitigate reward hacking and improve reliability under distribution shifts and robustness under inconsistent preferences. WARM involves a three-stage training procedure - pre-training by next token prediction, supervised fine-tuning, and reinforcement learning in conversational assistants. The paper discusses the challenges of distribution shifts and inconsistent preferences in RLHF and proposes WARM as an innovative approach to address these challenges.

Evaluation of WARM's Effectiveness in Enhancing Quality and Alignment of Large Language Models (LLMs)
The study evaluates the effectiveness of WARM in addressing reward hacking and improving the overall quality and alignment of large language models (LLMs). It found that WARM significantly improves efficiency compared to traditional ensembling of predictions, while enhancing reliability under distribution shifts and robustness under inconsistent preferences. The paper also highlights the benefits of WARM in reducing memorization of corrupted labels and improving stability in the reinforcement learning process. The study provides empirical validation of WARM's benefits through best-of-N experiments and reinforcement learning experiments, demonstrating its superior performance compared to traditional methods.

WARM as a Practical and Efficient Strategy for Reliable and Robust Reward Models
Overall, the paper introduces WARM as a practical and efficient strategy to obtain a reliable and robust reward model by combining multiple models. It emphasizes WARM's potential to contribute to more aligned, transparent, and effective AI systems and encourages further exploration in reward modeling.

Reference: https://arxiv.org/abs/2401.12187