How Instruction and Reasoning Data shape Post-Training: Data Quality through the Lens of Layer-wise Gradients

📰 ArXiv cs.AI

Learn how instruction and reasoning data impact post-training of large language models through layer-wise gradients analysis

advanced Published 12 May 2026

Action Steps

Apply spectral analysis to layer-wise gradients
Evaluate data quality using metrics like IFD and InsT
Compare the impact of low-quality vs high-quality instruction data on finetuning
Analyze the effect of reasoning data on post-training dynamics
Use layer-wise gradients to identify areas of improvement in LLMs

Who Needs to Know This

ML researchers and engineers working on LLMs can benefit from understanding how data quality affects finetuning dynamics, improving their model's performance and reliability

Key Insight

💡 Data quality significantly impacts LLM post-training, with layer-wise gradients analysis providing a valuable tool for evaluation and improvement

Key Takeaways

Learn how instruction and reasoning data impact post-training of large language models through layer-wise gradients analysis

Full Article

Title: How Instruction and Reasoning Data shape Post-Training: Data Quality through the Lens of Layer-wise Gradients

Abstract:
arXiv:2504.10766v2 Announce Type: replace-cross Abstract: As the post-training of large language models (LLMs) advances from instruction-following to complex reasoning tasks, understanding how different data affect finetuning dynamics remains largely unexplored. In this paper, we present a spectral analysis of layer-wise gradients induced by low/high-quality instruction and reasoning data for LLM post-training. Our analysis reveals that widely-studied metrics for data evaluation, e.g., IFD, InsT

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