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LLMs that ace shortest-path planning on small maps completely fall apart when asked to plan routes just a little bit longer.
Training a smaller LLM on a carefully pruned dataset lets it memorize as many facts as a model 10x larger trained on everything.
Stop guessing how much to pretrain vs. specialize your language model – scaling laws can now tell you the optimal compute allocation for maximizing performance on downstream tasks.
