SALMON: Self-Alignment with Instructable Reward Models

TL;DR

SALMON introduces a method to align large language models with minimal human supervision by using an instructable reward model trained on synthetic data, enabling controllable and scalable AI behavior without extensive human annotations.

Contribution

The paper presents SALMON, a novel approach that reduces reliance on human annotations by using a synthetic preference-based reward model for effective LLM alignment.

Findings

Dromedary-2 outperforms state-of-the-art models on benchmarks.

Achieves high alignment quality with only 6 exemplars and 31 principles.

Reduces need for online human preference collection.

Abstract

Supervised Fine-Tuning (SFT) on response demonstrations combined with Reinforcement Learning from Human Feedback (RLHF) constitutes a powerful paradigm for aligning LLM-based AI agents. However, a significant limitation of such an approach is its dependency on high-quality human annotations, making its application to intricate tasks challenging due to difficulties in obtaining consistent response demonstrations and in-distribution response preferences. This paper presents a novel approach, namely SALMON, to align base language models with minimal human supervision, using only a small set of human-defined principles, yet achieving superior performance. Central to our approach is an instructable reward model. Trained on synthetic preference data, this model can generate reward scores based on arbitrary human-defined principles. By merely adjusting these principles during the RL training phase, we gain full control over the preferences with the instructable reward model, subsequently influencing the behavior of the RL-trained policy models, and reducing the reliance on the collection of online human preferences. Applying our method to the LLaMA-2-70b base language model, we developed an AI assistant named Dromedary-2. With only 6 exemplars for in-context learning and 31 human-defined principles, Dromedary-2 significantly surpasses the performance of several state-of-the-art AI systems, including LLaMA-2-Chat-70b, on various benchmark datasets. We have open-sourced the code and model weights to encourage further research into aligning LLM-based AI agents with enhanced supervision efficiency, improved controllability, and scalable oversight.