Research
Mid-training is essential for LLM reasoning, IBM study shows
IBM Research has demonstrated that mid-training — a dedicated training phase between initial pre-training and fine-tuning — is essential for improving reasoning capabilities in large language models (
IBM Research has demonstrated that mid-training — a dedicated training phase between initial pre-training and fine-tuning — is essential for improving reasoning capabilities in large language models (LLMs). This intermediate stage allows models to develop stronger logical, mathematical, and problem-solving skills before task-specific fine-tuning is applied. The findings suggest that skipping mid-training results in measurably weaker reasoning performance, highlighting it as a critical component in the modern LLM development pipeline.
Related
- Rethinking Data Mixing from the Perspective of Large Language Models
- Towards Hierarchical Multi-Step Reward Models for Enhanced Reasoning in Large Language Models
- Efficient PRM Training Data Synthesis via Formal Verification
- Loop, Think, & Generalize: Implicit Reasoning in Recurrent-Depth Transformers
- Rectifying LLM Thought from Lens of Optimization
Source: IBM Research | 2026-04-15