Widely used language models (LMs) are typically built by scaling up a two-stage training pipeline: a pre-training stage that uses a very large, diverse dataset of text and a fine-tuning (sometimes, 'alignment') stage using more targeted examples of specific behaviors and/or human preferences. While it has been hypothesized that knowledge and skills come from pre-training, and fine-tuning mostly filters this knowledge and skillset, this intuition has not been rigorously tested. In this paper, we test this hypothesis with a novel methodology for scaling these two stages independently, essentially asking, What would happen if we combined the knowledge learned by a large model during pre-training with the knowledge learned by a small model during fine-tuning (or vice versa)? Using a reinforcement learning-based framework derived from recent developments in learning from human preferences, we introduce emulated fine-tuning (EFT), a principled and practical method for sampling from a distribution that approximates the result of pre-training and fine-tuning at different scales. Our experiments with EFT show that scaling up fine-tuning tends to improve helpfulness, while scaling up pre-training tends to improve factuality. Further, we show that EFT enables test-time adjustment of competing behavioral factors like helpfulness and harmlessness without additional training. Finally, we find that a special case of emulated fine-tuning, which we call LM up-scaling, avoids resource-intensive fine-tuning of large pre-trained models by ensembling small fine-tuned models with large pre-trained models, essentially 'emulating' the result of fine-tuning the large pre-trained model. Up-scaling consistently improves helpfulness and factuality of widely used pre-trained models like Llama, Llama-2, and Falcon, without additional hyperparameters or training.