How to Rewrite Pre-Training Data for Boosted Math/Code Performance?

• Blockchain Council
• May 21, 2025

Rewriting pre-training data is one of the most effective ways to improve how language models perform in math and programming tasks. It doesn’t just clean up the data—it makes it smarter. By restructuring examples, adding step-by-step logic, fixing formatting issues, and improving clarity, you give the model much better material to learn from.

In this guide, you’ll learn exactly how rewriting helps, the best techniques to apply, and how to build a workflow that boosts your model’s accuracy in real-world coding and math benchmarks.

Why Rewriting Pre-Training Data Is Essential for Math and Code

Large language models like GPT and LLaMA rely heavily on the quality of their training data. When the data is vague, inconsistent, or poorly structured, models can’t learn effectively. This matters most in subjects like mathematics and coding—where precision, sequence, and logic are everything.

Instead of training on whatever code or math problems you can find online, rewriting involves improving that data so it becomes structured, logical, and useful for learning.

Benefits of rewriting include:

• Clearer examples for step-by-step tasks
• Fewer hallucinations and incorrect logic
• Stronger reasoning and debugging skills
• Better generalization across similar problems

Key Techniques to Rewrite Pre-Training Data for Math and Code

Rewriting doesn’t mean starting from scratch. It means making smart improvements to what already exists. Here are the most useful techniques:

Fix Syntax and Style in Code

Syntax errors can confuse models and lead to bad outputs. Fix these by:

• Using linters and compilers to check every snippet
• Applying consistent indentation and naming
• Ensuring each block runs correctly in a standard environment

Also, follow a consistent style guide (like PEP8 for Python). This makes the examples easier to parse and reproduce.

Add Context and Break Down Problems

Models learn better when they’re shown how to think. Add explanatory context like:

• Problem statements written in plain English
• Step-by-step solutions in math with reasoning
• Code comments explaining logic, inputs, and outputs

This teaches the model to reason, not just copy patterns.

Normalize and Standardize Data

Normalization helps reduce noise. Do things like:

• Replace unusual variable names with generic ones like x, n, sum, etc.
• Format all equations similarly (e.g., LaTeX or Markdown)
• Use predictable input/output patterns across examples

This helps the model detect consistent patterns.

Use Real Task Formats

Make sure your rewritten data looks like real problems users would ask. For example:

• “Write a Python function to calculate factorial using recursion.”
• “Solve for x in the equation 2x + 5 = 15.”
• “Debug this block of code: [code here]”

Training on questions like these makes models perform better in real-world usage.

Add Difficulty Levels

Categorizing problems by difficulty helps models learn progression. Include tags like:

• Beginner (basic syntax, variables)
• Intermediate (loops, conditionals, functions)
• Advanced (recursion, dynamic programming, integrals)

This structure helps during fine-tuning and evaluation.

Comparison of Data Rewriting Techniques 

Steps to Rewrite Pre-Training Data

Here’s a workflow you can use for rewriting your dataset:

Each step contributes to better structure and less guesswork for your model.

Common Mistakes to Avoid

• Too much filtering, not enough rewriting: Removing poor data is good, but rewriting makes even average data highly valuable.
• Inconsistent formats: Mixing styles or data structures reduces learning efficiency.
• No explanation: Raw code or math without context is hard to learn from.

Real-World Gains from Rewritten Datasets

Teams that apply rewriting have reported:

• Up to 15% improvement on coding benchmarks
• More accurate long-form math solutions
• Better generalization from one task type to another

This approach is now being used in open datasets like OpenCodeReasoning and in fine-tuning for competitive math/coding tasks.

Who Should Use This Approach?

This method works well if you’re:

• Training LLMs for code assistance, math tutoring, or STEM tasks
• Building agentic AI that reasons over steps
• Trying to improve performance in domains that require logic and structure

If you’re serious about learning how these systems work, the AI Certification can give you practical skills to train and apply LLMs. For those handling data pipelines or optimization, the Data Science Certification is ideal. And if you’re working in marketing, business, or product strategy, check out the Marketing and Business Certification.

Final Thoughts

Rewriting pre-training data is a powerful and underused method for improving how language models handle math and code. It’s not just about cleaner inputs—it’s about making the examples better teachers.

You don’t need to start from scratch. With a clear process, the right formatting, and consistent logic, you can turn average data into high-performing training material that gives your models a real edge.