Aligning Large Language Models via Fully Self-Synthetic Data

2025-10-08

AI Summary

Existing RLHF approaches rely on costly human annotations, while RLAIF methods still require external reward models (e.g., GPT-4) or manually constructed preference data. To address these limitations, we propose Self-Alignment Optimization (SAO), the first fully self-generative alignment framework: the model autonomously generates diverse instructions, corresponding responses, and preference pairs—without human annotation, external reward models, or third-party APIs. SAO leverages role-playing to enhance instruction diversity and integrates an internal self-evaluation mechanism for preference modeling and reinforcement learning optimization. Evaluated on AlpacaEval 2.0, SAO achieves significant improvements in conversational quality while maintaining strong performance on downstream tasks—including question answering and mathematical reasoning—demonstrating its effectiveness, generalizability, and capacity for self-sustained improvement.

Technology Category

• Natural Language Processing: Generation
• Humans and AI: Learning Human Values and Preferences
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Traditional reinforcement learning from human feedback (RLHF) for large language models (LLMs) relies on expensive human-annotated datasets, while Reinforcement Learning from AI Feedback (RLAIF) also incurs significant costs, requiring the collection of diverse prompts and corresponding responses, often necessitating external reward models or proprietary models like GPT-4 to annotate preference pairs. In this work, we introduce Self-Alignment Optimization (SAO), a fully self-synthetic framework for LLM alignment, where all training data, including prompts (i.e., user queries), responses, and preferences, are generated by the model itself. Specifically, SAO first instructs the LLM to engage in persona role-play and generate diverse prompts and responses, which are then self-evaluated for preference optimization. Extensive experiments demonstrate that SAO effectively enhances the model's chat capabilities on standard benchmarks like AlpacaEval~2.0, while maintaining strong performance on downstream objective tasks (e.g., question-answering, math reasoning). Our work provides a practical solution for self-improvement in aligning LLMs, and the code for reproducing our results is available at: https://github.com/SJY8460/SAO.

Problem

• Reducing reliance on expensive human-annotated datasets for LLM alignment
• Eliminating need for external reward models in reinforcement learning
• Generating fully self-synthetic training data for model alignment

Innovation

• Fully self-synthetic framework for LLM alignment
• Model generates prompts, responses, and preferences itself
• Self-evaluated preference optimization without external data