Amuro & Char: Analyzing the Relationship between Pre-Training and Fine-Tuning of Large Language Models (2025)

Abstract: Large language model development relies on the pre-train-then-align paradigm, in which the model is typically pre-trained on a large text corpus and undergoes a tuning stage to align the model with human preference or downstream tasks. We investigate the relationship between pre-training and supervised fine-tuning by considering multiple tasks as well as different pre-trained model checkpoints. Our results on 18 datasets and two models suggest that i) although the model benefits significantly through supervised fine-tuning, it may forget previously known domain knowledge and tasks that are not seen during fine-tuning; ii) the model exhibits high sensitivity to evaluation prompts after supervised fine-tuning, but this sensitivity can be alleviated through further pre-training; iii) continual pre-training improves the model in a latent way that manifests after fine-tuning; iv) The model can already solve some tasks after pre-training while fine-tuning most benefits datasets where the model does not show capability during pre-training.

## introduction the rise of large language models (llms) as a general-purpose tool for a diverse range of natural language processing tasks has dramatically transformed the field, introducing new paradigms for data collection and model training @xcite @xcite @xcite @xcite . numerous models, training methods, datasets, and evaluation methods continue to be developed on an ongoing basis. nevertheless, a unified paradigm has emerged for training llms: based on this paradigm, work has focused on improving these two stages. work to improve pretrained models includes larger training sets @xcite @xcite , different data selection mechanisms @xcite , higher quality data @xcite , and various model architectures @xcite . meanwhile, research on model alignment includes different training objectives @xcite , new datasets @xcite , more efficient training @xcite and safety tuning @xcite . the alignment stage usually involves either supervised fine-tuning for specific tasks or instruction finetuning for general-purpose usage. regardless, finetuning (almost always) comes at the end of pretraining and yields remarkable improvements on downstream tasks @xcite . consequently, the benefits of each stage are largely explored independently, with improvements to pretraining being orthogonal to benefits from model alignment. rather than exploring these two training regimes independently, we ask: what does the model learn and forget during pre-training and fine-tuning? specifically, how do pretraining and fine-tuning interact to produce the resulting model? does more pre-training hinder better fine-tuning results? answering these questions requires us to examine how models learn during pre-training and how this affects fine-tuning. therefore, we begin by fine-tuning two language models under a variety of conditions to determine how fine-tuning affects model behavior. we explore both supervised and instruction fine-tuning, testing the models' memorization and forgetting when learning specific tasks and serving as general-purpose language-ai tools. we then explore the affect of pre-training on these behaviors by fine-tuning multiple pre-training checkpoints of a large language model (figure 1 ), evaluating each checkpoint and its fine-tuned variant on downstream evaluation sets. we track model abilities during pre-training and compare them to improvements achieved after fine-tuning at the corresponding pre-training step. 2 our experiments yield the following insights into llm training: (1) although supervised fine-tuning can improve performance on in-distribution tasks, it can also cause the model to forget domain knowledge or tasks that it was previously capable of solving ( §4); (2) fine-tuned models show high sensitivity to evaluation prompts, but this sensitivity can be alleviated by more pre-training ( §4); (3) continued pre-training can improve a model in ways that are only revealed after fine-tuning ( §6); (4) tasks for which the model already performs well during pre-training benefit much less from fine-tuning than those where the model does not demonstrate capabilities ( §5, §6); our findings provide insights into model training and can inform methods for both pre-training and fine-tuning. furthermore, our work shows the value of analyzing the training dynamics, in addition to analyzing the final checkpoint of an llm, as an aspect of interpretability, and we encourage model developers to release these checkpoints to aid future studies. supervised fine-tuning task training id test ood test summary generation xsum xsum, xlsum cnn question generation socialiqa socialiqa sciq, tweetqa natural language inference mnli mnli1, mnli2 rte, gpt3nli 3 paraphrase detection paws paws qqp, sts-b instruction tuning dataset description tülu-v2 a mixture of instruction datasets. arc grade-school multiple-choice qa. openbookqa open book exam qa. hellaswag commonsense inference. boolq reading comprehension. sciq science exam multiple choice qa. ## discussion our study fine-tunes model pre-training checkpoints to understand the dynamics of pre-training and fine-tuning on model performance. fine-tuning teaches additional task format but leads to forgetting unused abilities. our results show that fine-tuning guides the model to under-stand the format and complete a given task. as this information diminishes, the model's overall ability improves. additionally, more pre-training will lead to a model that reacts better to instruction-style prompts, and the ability to interpret such instruction will not be lost when the model is fine-tuned in a different format. however, fine-tuning comes at the expense of other model abilities, such as the capability of solving tasks or domains that are unrelated or weakly related to the fine-tuning task. this insight can be helpful in our understanding of the multitask abilities of llms, where certain tasks can introduce conflicts during multi-task training @xcite . some datasets can be learned without finetuning. we discover a dichotomy between datasets. some are learned during model pre-training, while others show no improvements during pre-training. furthermore, the datasets learned during pretraining do not benefit from fine-tuning. this observation, combined with our study about what is learned during fine-tuning ( §4) suggests that some tasks are presented in a manner that aligns with what the model sees during pre-training, and thus fine-tuning provides no additional information. it may be possible to modify tasks to better align with pre-training and thus make them learnable. pre-training can improve models in unseen ways. some datasets are not learned during pre-training but benefit significantly from fine-tuning ( §5). however, these datasets still benefit from additional pre-training, even though those benefits are not revealed without fine-tuning ( §6). the model learns important information to solve the task, even though it cannot express that information without fine-tuning. we empirically observe that the point where more pre-training lead to diminishing return on downstream fine-tuning results approximately align with the turning point of few-shot performance in section 5. future work may identify ways to verify the turning point and detect these improvements during pre-training, which can better guide pre-training choices to produce models that perform better post-fine-tuning. perhaps there is a way in which information about these tasks can be included in pre-training, allowing the model to better utilize the massive amount of pre-training data. for example, early stopping during pre-training could lead to better utilization of limited training resources if we know when to stop. ## related work recent studies identify phase transition of model training @xcite , where new capabilities or behaviors suddenly emerge when certain thresholds of model complexity are reached. the aspects of complexity often include model size, amount of training by flops or tokens, and model architecture. several prior works studied the training dynamics of language models by analyzing the internals of train-fromscratch models @xcite @xcite @xcite . the results of these works suggest that the behaviors that are often overlooked after training could be valuable signals for model analysis. in addition to train-from-scratch models, @xcite studied the fine-tuning dynamics of language models. this work focuses on the effect of pre-training dynamics on downstream fine-tuning results by fine-tuning intermediate pretraining checkpoints on various tasks. due to the scarcity of publically accessible intermediate pretraining checkpoints, the effect of fine-tuning at different pre-training stages is largely unexplored. concurrent work @xcite also fine-tunes intermediate pre-training checkpoints and finds that supervised fine-tuning results can be used as a signal to predict when emergence occurs, while our findings point out a dichotomy of model behavior on different datasets, with the potential for data-efficient and budget-friendly training by understanding the stages of model training. ## conclusion we explore the relationship between fine-tuning and pre-training llms through fine-tuning multiple pre-training checkpoints of large language models. our results on 18 datasets and two models provide insights into llm training. we identify the aspects that llm learns and forgets during supervised fine-tuning; by analyzing pre-training history, we find that pre-training improves the model in a latent way that is only observable after finetuning. the model may excel at some tasks without fine-tuning. however, the model can rapidly learn datasets that it does not demonstrate capabilities during pre-training with a small amount of supervision. overall, our study highlights the value of analyzing language model training dynamics. we encourage model developers to release pre-training checkpoints to facilitate research on llm training.