Self-Rewarding Language Models

Models need superhuman feedback for training signals. A self-rewarding language model outperforms existing systems.

Summary: The article discusses the concept of Self-Rewarding Language Models (SRLMs) and their ability to continually improve in both instruction following and reward modeling through iterative training. The authors propose a method where SRLMs generate their own rewards during training via an iterative procedure, and they demonstrate that this approach leads to improved performance in instruction following tasks and reward modeling ability. The study focuses on fine-tuning a Llama 2 70B model using three iterations of the proposed approach and shows that the model outperforms existing systems on the AlpacaEval 2.0 leaderboard.

Major Findings:

1. Self-Rewarding Language Models (SRLMs):
   • The study introduces Self-Rewarding Language Models, which can act as both instruction following models and as generators and evaluators of new instruction-following examples. These models are trained using an iterative DPO framework, allowing them to update their reward model continually during alignment.
2. Improved Instruction Following and Reward Modeling Ability:
   • Through iterative training, the SRLMs demonstrate improved instruction following ability and the ability to provide high-quality rewards to themselves. The findings show that the reward modeling ability of the model dynamically improves during training, deviating from standard practices where the reward model is fixed.
3. Performance on AlpacaEval 2.0 Leaderboard:
   • The SRLM, after three iterations of training, outperforms existing systems on the AlpacaEval 2.0 leaderboard, including Claude 2, Gemini Pro, and GPT-4 0613. The preliminary study suggests the possibility of models continually improving in both instruction following and reward modeling.

Analysis and Critique:

The article presents an innovative approach to self-rewarding language models and demonstrates promising findings. However, the study is limited in several ways: - Lack of In-depth Evaluation: While the authors conducted head-to-head evaluations and reported performance on the AlpacaEval 2.0 leaderboard, there is a lack of in-depth evaluation using other benchmarks or comprehensive human evaluations. - Limited Iterations: The study conducted only three iterations of training, leaving open questions about the scalability and long-term effectiveness of the proposed approach. - Safety Evaluation: The article acknowledges the need for safety evaluations but does not provide an in-depth analysis of potential safety issues or the model’s capability to improve in safety over time. - Methodological Limitations: The study lacks a critical analysis of potential biases or limitations with the proposed approach, such as reward-hacking or unforeseen challenges in iterative training.

In conclusion, while the concept of Self-Rewarding Language Models shows promise, further research is necessary to address the limitations and evaluate the long-term effectiveness and safety implications of this approach.

Appendix

Model	gpt-3.5-turbo-1106
Date Generated	2024-02-26
Abstract	http://arxiv.org/abs/2401.10020v1
HTML	https://browse.arxiv.org/html/2401.10020v1
Truncated	False
Word Count	7958