Revisiting Superficial Alignment Hypothesis
The paper revisits the Superficial Alignment Hypothesis. It studies post-training scaling behavior with finetuning examples. Performance scales as a power law with more finetuning examples. Model performance correlates with reasoning ability, not just style. Language models can integrate new knowledge post-pre-training. Results suggest the hypothesis is an oversimplification.