Select and Improve: Understanding the Mechanics of Post-Training for Reasoning

2026-06-12 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, extended

Summary

Reinforcement learning (RL) post-training significantly enhances reasoning capabilities in models like Qwen-2.5-1.5B, primarily through two mechanisms: strategy selection and strategy improvement. Strategy selection involves routing problems to existing reasoning patterns learned during pre-training and supervised fine-tuning (SFT), rapidly boosting performance to over 95% accuracy on math reasoning tasks in GF(11) when SFT data includes diverse strategies. Strategy improvement refines these existing patterns, enabling generalization to harder problems, such as those with 6-9 or 6-15 arithmetic steps, but requires RL data of increased difficulty. The study emphasizes that RL largely refines pre-existing behaviors rather than inducing novel ones, underscoring the critical role of high-quality, diverse pre-training and SFT data in scaling reasoning capabilities.

Key takeaway

For AI Scientists and ML Engineers optimizing reasoning models, you should prioritize diverse reasoning strategies in your Supervised Fine-Tuning (SFT) data to enable effective strategy selection during Reinforcement Learning (RL). Design your RL datasets with a difficulty curriculum, ensuring problems are harder than those in SFT to drive strategy improvement and generalization. This integrated approach, focusing on pre-RL data quality, is crucial for scaling model reasoning capabilities, as RL primarily refines existing patterns rather than creating new ones.

Key insights

RL post-training enhances reasoning via strategy selection and improvement, driven by diverse pre-training and challenging RL data.

Principles

• RL refines pre-existing reasoning patterns.
• Diverse SFT data enables strategy selection.
• Harder RL data improves existing strategies.

Method

The study used Qwen-2.5-1.5B, SFT on 2-5 step math problems (forward/backward/mixed reasoning), then RL with GRPO on 6-9 or 6-15 step problems over GF(11) or GF(13).

In practice

• Ensure SFT data includes diverse reasoning strategies.
• Design RL datasets with increasing problem difficulty.
• Prioritize pre-training data quality for RL success.

Topics

• Reinforcement Learning
• Large Language Models
• Reasoning Capabilities
• Supervised Fine-Tuning
• Qwen-2.5-1.5B
• Finite-Field Arithmetic

Best for: Research Scientist, AI Engineer, NLP Engineer, AI Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.