Indeed this is wasting HN time.

"The model reads 524,000 characters of chess". This is 100KByte of training data in a toy model with rigid parameters and no global learning. Gap with real LLM and trillions of tokens.

This model really addresses the problem of preserving previously learned knowledge, but by restricting the LR of the trunk it stops acquiring new knowledge. Details: "Rethinking the Stability-Plasticity Trade-off in Continual Learning from an Architectural Perspective"