This paper introduces LAB (Large-scale Alignment for chatBots), a novel methodology designed to address the scalability challenges in the instruction-tuning phase of large language model (LLM) training. LAB leverages a taxonomy-guided synthetic data generation process and a multi-phase tuning framework to reduce reliance on expensive human annotations and proprietary models like GPT-4. The method aims to enhance LLM capabilities and instruction-following behaviors without suffering from catastrophic forgetting, offering a cost-effective solution for training LLMs for various applications. The LAB method consists of two main components: 1. **Taxonomy-guided Synthetic Data Generation**: This process curates a diverse and high-quality instruction dataset using a taxonomy that hierarchically classifies data samples into smaller task groups. The taxonomy ensures high diversity and quality in the synthetic data. 2. **Multi-phased Training Framework**: This framework includes two phases—knowledge tuning and skills tuning—followed by a replay buffer to prevent catastrophic forgetting. The training process is designed to be stable and efficient, ensuring that the model can learn from a wide range of tasks without losing previously learned knowledge. The paper demonstrates that LAB-trained models achieve competitive performance across several benchmarks compared to models trained with traditional human-annotated or GPT-4-generated synthetic data. The findings show that LAB is a scalable and cost-effective solution for enhancing LLM capabilities, particularly in the alignment-tuning phase of LLM training.This paper introduces LAB (Large-scale Alignment for chatBots), a novel methodology designed to address the scalability challenges in the instruction-tuning phase of large language model (LLM) training. LAB leverages a taxonomy-guided synthetic data generation process and a multi-phase tuning framework to reduce reliance on expensive human annotations and proprietary models like GPT-4. The method aims to enhance LLM capabilities and instruction-following behaviors without suffering from catastrophic forgetting, offering a cost-effective solution for training LLMs for various applications. The LAB method consists of two main components: 1. **Taxonomy-guided Synthetic Data Generation**: This process curates a diverse and high-quality instruction dataset using a taxonomy that hierarchically classifies data samples into smaller task groups. The taxonomy ensures high diversity and quality in the synthetic data. 2. **Multi-phased Training Framework**: This framework includes two phases—knowledge tuning and skills tuning—followed by a replay buffer to prevent catastrophic forgetting. The training process is designed to be stable and efficient, ensuring that the model can learn from a wide range of tasks without losing previously learned knowledge. The paper demonstrates that LAB-trained models achieve competitive performance across several benchmarks compared to models trained with traditional human-annotated or GPT-4-generated synthetic data. The findings show that LAB is a scalable and cost-effective solution for enhancing LLM capabilities, particularly in the alignment-tuning phase of LLM training.