Fine-Tuning & Model Alignment
Synthetic Data Generation at Scale
Teacher-student distillation, Evol-Instruct, and building proprietary training datasets.
Interview: High - The secret weapon behind WizardLM, Phi-3, and most top open-source models.
The Best Training Data Doesn't Come From Humans
Synthetic Data Pipeline
Phi-3-mini (3.8B parameters) matches GPT-3.5 performance on benchmarks. Microsoft's secret: 95% of its training data was synthetically generated by GPT-4. A weak model trained on high-quality synthetic data from a stronger model can dramatically exceed its expected capability ceiling. This is the Teacher-Student distillation paradigm that now underpins nearly every frontier open-source model.
Evol-Instruct: Evolving Instructions to Higher Complexity
Evol-Instruct (WizardLM's method) takes a seed dataset (like Alpaca's 52K examples) and uses GPT-4 to rewrite each instruction into progressively harder variants. It applies operations like "add constraints," "deepen the reasoning required," "switch to a different domain," and "add multiple sub-questions." This generates 250K complex instructions from 52K simple seeds — purely using LLMs.
Generating Step-by-Step Reasoning Traces
Models trained on final answers alone cannot reason through problems (Chain of Thought). To inject reasoning capability, you generate thinking traces: ask GPT-4o to solve a problem by explicitly showing every reasoning step. Train your student model on these traces. The student learns to think, not just to recall answers.
Use Cases
Building internal fine-tuning datasets for proprietary domains without expensive human annotators
Injecting Chain-of-Thought reasoning capability into smaller, cheaper models via distillation
Generating edge-case test data for evaluating RAG and agent systems
Common Mistakes
Using GPT-3.5 to generate training data for fine-tuning GPT-3.5 — the student cannot exceed the teacher. Always use a stronger teacher model.
Not filtering synthetic data for quality — LLMs hallucinate factual errors in training data, which the student model then memorizes as facts
Overusing synthetic data without any real-world examples — models need grounding in actual human tasks and language patterns