Fine-Tuning Regimes Define Distinct Continual Learning Problems

The deeper provocation here is not about any single fine-tuning method performing better, but about the field's evaluation infrastructure: if different methods implicitly operate under different parameter regimes, then head-to-head benchmark comparisons may be measuring incompatible things entirely.

This connects most directly to the generalization work covered in 'Generalization in LLM Problem Solving: The Case of the Shortest Path' from mid-April, which similarly found that benchmark results can obscure structural limitations rather than reveal them. In that case, strong performance on spatial transfer masked a consistent failure at longer horizons. The pattern is similar here: surface-level scores on continual learning benchmarks may look comparable while the underlying learning dynamics are fundamentally misaligned. Both papers are pointing at the same diagnostic gap, which is that the field's standard evaluation setups are not controlled tightly enough to support the conclusions drawn from them. This is a slow-building methodological critique, not a single-paper finding.

Watch whether continual learning benchmark maintainers, particularly those behind established suites like Split-CIFAR or Permuted MNIST variants, issue updated protocols that stratify results by parameter regime within the next two conference cycles. If they do not, this paper's critique will remain unresolved in practice regardless of citation count.