What’s the difference between pretraining and fine-tuning in machine learning? This article breaks down the key concepts, use cases, and trade-offs of each approach—helping you understand when to use pretrained models and how fine-tuning tailors them for specific tasks.
Here is the key information on pretraining and fine-tuning.
Pretraining is the initial training of a model on a large, general dataset (often without labels) to learn broad patterns, while fine-tuning is the subsequent training on a smaller, task-specific dataset (with labels) to specialize the model for a particular task. In short: pretraining builds a general foundation, and fine-tuning adapts it to a specific goal.
Pretraining gives the model a head start by learning language or vision fundamentals from vast data. This general knowledge makes the model effective on many tasks out-of-the-box. Fine-tuning then leverages this knowledge and fine-tunes it to achieve high performance on a specific task, instead of training a new model from scratch (which would require far more data and compute).
Yes, you can train a model from scratch on a specific task (which is essentially training without a pretraining phase), but it’s usually less efficient. Without pretraining, you’d need a lot more task-specific data and time to reach the same performance. Using a pretrained model as the starting point is best practice for most applications, because it converges faster and performs better with limited data.
Fine-tuning adapts a pretrained model to a particular task or domain. By training the model on labeled, task-specific data, fine-tuning tweaks the model’s parameters so it can excel at the target task (for example, improving accuracy on sentiment analysis, machine translation, etc.). It takes a generalist model and makes it a specialist for your use case.
Modern machine learning has seen the transition from large language models (LLM) to vision language models (VLM) and multimodal language models (MLM). Recent advancements in the availability of computing resources, web-scale datasets, synthetic data generation, and training strategies have made this increase in generalisation capabilities possible.
Text-based AI has already reshaped how we interact, work, and communicate—but human experience extends far beyond text alone.
Multimodal models bridge this gap, empowering AI to see, hear, and sense the world more like humans do. Leveraging existing pretrained language and vision models, these powerful systems blend different data types to solve complex real-world problems, from robotics and autonomous driving to advanced document understanding.
As we move into the era of AI agents, multimodal training strategies and model adaptation techniques will become increasingly relevant. These approaches enable models to leverage knowledge transfer between different data types – from images and text to audio and structured information – creating more versatile and robust systems.
We often see a multistage training paradigm combining pretraining, fine-tuning and instruction-tuning (or post-training), each stage contributing uniquely to the model's performance and adaptability. This multi step approach boosts performance by enabling the model to learn progressively more specific representations at each stage.
This article will look at recent techniques for using models in multimodal and cross-domain applications and explore the following:
Pretraining allows the models to learn fundamental representations about the underlying structure in a self-supervised manner. This phase is crucial for developing a robust understanding of complex visual and textual features by leveraging large-scale datasets, and allows the model to develop a versatile feature space without explicit instructions that forms the basis for subsequent task-specific learning.
The primary objective of pretraining is to develop generalized representations and pattern recognition abilities by exposing the model to vast amounts of diverse data before fine-tuning it for specific downstream tasks. During pretraining, models typically learn through self-supervised objectives where the data provides its own supervision signals.
For language models, this might involve predicting masked words or next tokens, while vision models might reconstruct partially obscured images or determine relative positions of image patches. These tasks force the model to develop rich internal representations that capture semantic, syntactic, and contextual information. This allows the model to:
A common strategy used during model pretraining is contrastive learning.
Pre training techniques have evolved significantly over the years with self supervised learning becoming the dominant paradigm. These methods allow us to define an unsupervised learning task (without needing labels) wherein the supervision or the signal is provided by the data itself. This becomes increasingly important in cases where data annotation is expensive such as medical imaging. Some of the key techniques used in vision and text pre training are:
Now, let’s explore pretraining in various real-life computer vision use cases.
Similar to recent trends, the Autonomous Driving domain has also seen an evolution from traditional supervised approaches to leveraging pretrained models in novel ways to enhance perception tasks.
One such instance is the use of pretrained semantic segmentation networks to guide geometric representation learning. As demonstrated in Guizilini et al.'s ICLR 2020 work Semantically-Guided Representation Learning for Self-Supervised Monocular Depth, these pretrained networks can improve monocular depth prediction without requiring additional supervision, effectively transferring semantic knowledge to depth estimation tasks.
In particular, they use pretrained semantic segmentation networks to guide geometric representation learning and pixel-adaptive convolutions to learn semantic-dependent representations thereby exploiting latent information in the data.
As seen in DepthPro (Bochkovski et al., 2024), the field now leverages pretrained vision transformers for transfer learning, allowing for increased flexibility in model design. This approach uses a combination of pretrained ViT encoders: a multi-scale patch encoder for scale invariance and an image encoder for global context anchoring.
The effectiveness of pretraining is particularly evident in the mixing of datasets; using a combination of real and synthetic datasets during pretraining has been shown to increase generalization, as measured by zero-shot accuracy (Ranftl et al. 2019). This hybrid approach to pretraining helps models become more robust and adaptable across different scenarios.
In the context of ego-motion estimation, pretraining has evolved to incorporate multiple modalities. The two-stream network architecture proposed by Ambrus et al. (2019) demonstrates how pretraining can be enhanced by treating RGB images and predicted monocular depth as separate input modalities.
This multi-modal pretraining approach enables the network to learn both appearance and geometry features effectively.
For camera self-calibration tasks, pretraining has moved toward end-to-end frameworks that can learn from video sequences rather than static frames. The work by Fang et al. (2022) shows how pretraining can be done using view synthesis objectives alone, enabling models to adapt to various camera geometries, including perspective, fisheye, and catadioptric setups.
The trend in pretraining for autonomous driving is clearly moving towards the use of foundational models that can operate in zero-shot scenarios. This shift is exemplified by recent works that are moving away from requiring metadata such as camera intrinsics, instead focusing on developing models that can generalize across different scenarios and camera setups without explicit calibration or fine-tuning.
The combination of multi-modal inputs and the use of both synthetic and real data during pretraining has enabled models to achieve better performance across various autonomous driving tasks while reducing the need for expensive labeled data.
Pro Tip: Are you labeling data? Check out 12 Best Data Annotation Tools for Computer Vision (Free & Paid).
DeepMind introduced Gato in 2022 in the realm of agents and reinforcement learning. It is a generalist agent designed to perform a number of tasks across multiple modalities, including text, images, and robotic control.
By leveraging a single neural network with consistent weights, Gato seamlessly switches between tasks such as playing Atari games, generating image captions, engaging in conversations, and controlling robotic arms to stack blocks.
To effectively balance multiple objectives, Gato employed a unified training approach. Gato was trained on a diverse dataset encompassing over 600 tasks, including text generation, image captioning, and robotic control. This extensive training allowed it to learn shared representations applicable across tasks, facilitating efficient multitask learning.
The model's architecture and training regimen enable it to generalise across tasks without requiring task-specific fine-tuning. Gato represented a significant step toward creating adaptable and efficient AI systems capable of performing diverse tasks.
Gato's architecture emphasises the use of general-purpose representations applicable across various tasks. The model can adapt to new tasks by learning shared representations during training without requiring task-specific modifications.
PaLI-X, Chen et al. (2023) is a multilingual vision and language model that significantly advanced benchmark performance across diverse tasks, including image captioning, visual question answering, document understanding, object detection, and video analysis.
The authors employ an encoder-decoder architecture to process diverse data formats. Images are passed through a Vision Transformer (ViT) encoder, which processes visual data into embeddings. These embeddings are combined with textual inputs—such as questions, prompts, or captions—and fed into the decoder. This enables PaLI-X to handle tasks like image captioning, where the output is text describing the image, and visual question answering, where the output is a text response to a question about the image. Additionally, it can process multiple images simultaneously, facilitating tasks like video captioning and object detection.
Since the model balances multiple objectives, PaLI-X utilises a mixture-of-objectives training approach. This strategy combines prefix-completion and masked-token completion tasks, allowing the model to learn from both the context provided by preceding tokens and the structure of the masked tokens.
Fine-tuning is a method of transfer learning where a pretrained AI model undergoes additional training on a specialized dataset to enhance its accuracy and effectiveness on specific tasks or within particular domains.
This targeted retraining leverages the model’s existing knowledge, allowing it to rapidly adapt and achieve superior performance in tasks such as image classification, object detection, or natural language processing.
Fine-tuning helps maximize the relevance of AI models in real-world scenarios, significantly reducing time and resources compared to training models from scratch.
The primary objective of fine-tuning is to adapt a model's general knowledge to perform well on targeted applications while preserving the valuable representations learned during pretraining.
By leveraging the rich representations developed during pretraining, fine-tuning can achieve remarkable results with orders of magnitude less data than would be required for training from scratch, making specialized AI applications more accessible.
Now let’s look at various strategies and techniques used in various industries to improve downstream model accuracy and overall performance on relevant benchmarks. We will explore how iterative training, domain-specific adjustments, and algorithmic enhancements contribute to achieving SOTA performance.
While LLMs have already become integral to many people’s workflows, their integration into our wider lives has been blocked by their inability to generalise to open-domain tasks and weak reasoning capabilities. To enable a world where intelligent agents and humans are in a shared environment, we need models capable of reasoning across text, vision, speech, and sensor modalities.
The NeurIPS 2023 paper "Large Language Models are Visual Reasoning Coordinators" introduces Cola. This novel framework leverages large language models (LLMs) to coordinate multiple vision-language models (VLMs) for enhanced visual reasoning tasks. The authors propose that an LLM can effectively coordinate multiple VLMs by harnessing their individual strengths. They propose two primary variants:
While VLMs have demonstrated proficiency in tasks like visual question answering with the help of methods like Cola, they often struggle with real-time processing and integrating multimodal data necessary for embodied tasks.
PaLM-E by Driess et al. (2023) attempts to create a single large embodied multimodal model capable of operating on multimodal sentences from a variety of observation modalities, on multiple embodiments, i.e., sequences of tokens, where inputs from arbitrary modalities (images or neural 3D representations) alongside text allow for a direct integration of the rich semantic knowledge stored in pretrained LLMs into the planning process.
They build upon Google's Pathways Language Model (PaLM) (A 540 B parameter LLM), incorporating sensor data from robotic agents, such as images and continuous state estimations, alongside textual inputs.
PaLM-E employs an encoder that maps continuous observations into a sequence of vectors. These vectors are interleaved with text tokens, forming a combined input sequence for the model. The self-attention layers of the LLM backbone can then process these multimodal sentences in the same way as text. This allows the model to incorporate real-world continuous sensor modalities directly.
The model then generates sequences of actions for robots to perform complex tasks, considering physical constraints and environmental dynamics. Moreover, PaLM-E can also answer questions about visual scenes, integrate information from images and textual queries, and generate descriptive captions for images to showcase its understanding of visual content.
Zhai et al. (2024) propose a framework for training VLMs with Reinforcement Learning in their paper “Fine-Tuning Large Vision-Language Models as Decision-Making Agents via Reinforcement Learning”. Given a task description, a VLM is prompted to generate chain-of-thought (CoT) reasoning based on the current states. This enables the efficient exploration of intermediate reasoning steps that lead to a final text-based action.
The prompting step encourages the model to decompose complex tasks into manageable sub-tasks, facilitating structured decision-making. The model's output, an open-ended text response, is then parsed into an executable action, enabling interaction with the environment. The environment then provides feedback through rewards based on the model's actions.
This iterative process allows the model to improve its performance over time, adapting to the specific requirements of the task.
Document understanding in Vision-Language Models (VLMs) involves integrating textual, visual, and structural elements to comprehend and process documents effectively. Models like DocumentCLIP (Liu et al. 2023) utilise contrastive learning to align images and their corresponding textual content within documents.
By training on large datasets, these models learn to associate visual elements with relevant text, enhancing their ability to understand the context and semantics of documents. They achieve this using multiple embeddings:
"LayoutLLM: Layout Instruction Tuning with Large Language Models for Document Understanding" by Luo et al. (CVPR 2024) introduces LayoutLLM, a method that enhances document comprehension by integrating large language models (LLMs) with layout-specific instruction tuning. This approach addresses the challenge of effectively utilising document layout information, which is crucial for accurate document understanding.
To capture the structural nuances of documents, LayoutLLM employs a pretraining strategy that focuses on three levels of information:
LayoutLLM also introduced a novel module named LayoutCoT, which enables the model to focus on regions relevant to a given question. This enhances the model's ability to generate accurate answers by directing attention to relevant sections of the document. Additionally, LayoutCoT provides interpretability, allowing for manual inspection and correction of the model's reasoning process. By training on document-level, region-level and segment-level tasks, LayoutLLM develops a hierarchical understanding of document layouts.
The multimodal approaches outlined in this article represent different strategies for bridging the gap between language, vision, and embodied reasoning. While coordination frameworks like Cola and PaLM-E tackle the challenge through different architectural choices, document understanding models like LayoutLLM show how structural information can be effectively incorporated into model reasoning.
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