Synthetic Data
Also known as: Synthetic training data
Use a stronger LLM to generate training data: bootstrap fine-tuning, fill data gaps, distill knowledge.
Training data generated by an existing LLM rather than collected from humans. Used to bootstrap fine-tuning sets, augment scarce domains, distill capabilities into smaller models, or seed RLHF preference pairs.
In practice
Underpins most modern open-model post-training pipelines (Phi, Tulu, distilled SFT). Interviews probe quality filtering and contamination.
Related topics
Related terms
Fine-tuning
Continue training a pre-trained model on task-specific data to specialize its behavior.
RLHF (Reinforcement Learning from Human Feedback)
Train a reward model from human preference pairs, then RL-fine-tune the LLM against that reward.
LoRA (Low-Rank Adaptation)
Fine-tune by training small low-rank update matrices instead of every weight: same quality, ~1% of the cost.
DPO (Direct Preference Optimization)
Skip the reward model and PPO: fine-tune directly on preferred-vs-rejected response pairs.
Knowledge Distillation
Train a small student model to match a big teacher's outputs: cheap, fast inference with most of the quality.
Reasoning Model
An LLM trained to reason at length internally before answering. Slower and more expensive, but much better on hard problems.