LLM training dynamics, efficiency, and continual learning

CluSTER reports near-maintained performance with about 31% of fine-tuning data and up to 69.6% less training time on 7B/13B models, while DLC examines training–inference mismatch and other work explores unified likelihood training and offline consolidation. Evidence is promising but mixed or bounded by reported settings; broader comparisons and reproducibility checks remain open.