This paper studies forking, a generalization failure discovered in NanoGPT autoresearch.
Under data replay, models with an over-encoding n-gram memory branch show a sharp separation of training and validation loss at epoch boundaries, resembling the shape of forks.
We study this phenomenon in a controlled vanilla NanoGPT setting and reproduce it in a DeepSeek-style model with Engram.
Mechanistically, repeated updates sharpen the continuations observed in training while suppressing the probability of unseen continuations, whose loss grows with each pass.
The n-gram module creates weakly interacting context-specific subspaces, amplifying this effect.
Low-frequency contexts contribute most of the gap, whereas larger training budgets and heavily crowded tables suppress it.
We also observe forking in short-budget, heavily repeated SFT and RL-like regimes.
The contributions of this paper are twofold:
- Forking reveals yet another curious phenomenon in deep learning, in addition to grokking and double descent.
- Forking is an unexpected and unpleasant by-product of tricks proposed by autoresearch agents.
While these agents produce an enormous number of results that seem useful, we should always be careful with their results.