# Massive Supervised Fine-tuning Experiments Reveal How Data, Layer, and Training Factors Shape LLM Alignment Quality

**Authors:** Yuto Harada, Yusuke Yamauchi, Yusuke Oda, Yohei Oseki, Yusuke Miyao, Yu Takagi  
**arXiv:** [2506.14681](https://arxiv.org/abs/2506.14681)  
**Date:** 2025-06-17  
**Summary page:** https://www.summarizepaper.com/en/paper/2506.14681v1/

## Key points

- Study focused on supervised fine-tuning (SFT) for aligning large language models (LLMs) with human instructions and values
- Extensive experiments conducted involving training diverse base models on various datasets like code generation, mathematical reasoning, and general-domain tasks
- Over 1,000 SFT models developed under controlled conditions
- Identification of key dataset properties impacting SFT effectiveness
- Layer-wise modifications introduced by SFT with consistent and varied training-task synergies across models
- Perplexity metrics showed consistent correlation with SFT effectiveness, often more reliable than superficial similarities to benchmark standards
- Mid-layer weight adjustments had strongest correlation with model performance improvements
- Commitment to releasing all 1,000+ SFT models and benchmark results for further research in the field

## Summary

In their study titled "Massive Supervised Fine-tuning Experiments Reveal How Data, Layer, and Training Factors Shape LLM Alignment Quality," authors Yuto Harada, Yusuke Yamauchi, Yusuke Oda, Yohei Oseki, Yusuke Miyao, and Yu Takagi delve into the critical process of supervised fine-tuning (SFT) for aligning large language models (LLMs) with human instructions and values. The researchers conducted extensive experiments involving training a diverse array of base models on various datasets such as code generation, mathematical reasoning, and general-domain tasks. This rigorous approach resulted in over 1,000 SFT models being developed under controlled conditions. Through their comprehensive analysis, the team identified key dataset properties that significantly impact the effectiveness of SFT. They also delved into the layer-wise modifications introduced by SFT and discovered that certain training-task synergies were consistent across all models while others exhibited significant variations. This highlights the importance of implementing model-specific strategies tailored to individual LLMs. One notable finding from the study was the consistent correlation between perplexity metrics and SFT effectiveness. Surprisingly, perplexity often proved to be a more reliable predictor of performance gains than superficial similarities between trained data and benchmark standards. Additionally, the researchers observed that mid-layer weight adjustments had the strongest correlation with improvements in model performance. To facilitate further research in this area, the authors have committed to releasing all 1,000+ SFT models along with benchmark results. By sharing their findings and resources with the scientific community, they aim to accelerate progress in understanding how data, layer configurations,and training factors collectively shape the alignment quality of large language models.