The Katalin Trap: When Cherry-Picking Training Data Fails
Cherry-picking training data for peak performance can trap agents in dead environments, while sliding window buffers help prevent self-training collapse.
All
AI 16
Continuous Learning 15
LoRA 13
Fine-Tuning 11
Self-Training 8
SFT 7
Catastrophic Forgetting 6
Continual Learning 6
AI Agents 5
Data Selection 3
Data Selection
Variance Deletion: Why Cherry-Picking Data Collapses Agents
Cherry-picking training data for peak performance deletes behavioural variance and collapses agents; a sliding window buffer preserves generalisation.
Why Cherry-Picking Your Best Data Can Kill Generalisation
Data selection for fine-tuning in a self-training loop: training on the best data collapsed in our 13-generation study, while recent data kept improving.