Experience Replay to Stop Catastrophic Forgetting in LLMs
Master experience replay catastrophic forgetting llm solutions with complementary learning systems and rehearsal buffer LoRA fine-tuning strategies.
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
Catastrophic Forgetting
What Are Elastic Weights? EWC vs. LoRA in Continual Learning
Elastic Weight Consolidation (EWC) explained: how Fisher weight springs prevent catastrophic forgetting, why they struggle at LLM scale, and tools to try.
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.
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.
Sliding Window SFT vs Best-of-N in Continual Learning
In our 13-generation continual learning study, top-3 historical generation curation collapsed while a K=3 sliding window SFT buffer adapted on 4x less data.
Sliding Window SFT: Continual Learning Without Data Scaling
In a 13-generation agent study, a K=3 sliding window SFT loop retains 52.6% of full-history offline composite score and 58.4% of MB freed on 25% of the rows.