The Synthetic Data Echo Chamber: How to Fine-Tune Domain Models Without Triggering Mode Collapse

Training open-source models on synthetic datasets generated by frontier LLMs often triggers catastrophic mode collapse, jargon loss, and sterile outputs. Here is how Adversarial Perplexity Filtering and Counter-Example Synthesis create high-density training data.

Written and maintained by Hassan Nazir, Forward Deployed Engineer and Applied AI practitioner.