How to Efficiently Tune Large Language Models with Early-Exiting

Know Early AI Trends!

Sign-up to get Trends and Tools related to AI directly to your inbox

We don’t spam!

In the rapidly evolving world of deep learning, the efficiency of model training and inference has become as crucial as model accuracy itself. Large Language Models (LLMs) like GPT and BERT have pushed the boundaries of what’s possible in natural language processing, but their immense size and complexity present a significant challenge. The computational cost of training and running these models can be prohibitive, limiting their accessibility and practicality for many in the community. This is where the concept of early exiting offers a promising solution.

What is Early Exiting?

Early exiting is an advanced technique designed to enhance the efficiency of deep learning models by introducing multiple exit points within the model architecture. This approach allows for inputs to be classified at earlier stages without having to pass through the entire model, saving significant computational resources. Each exit point is equipped to make predictions with a varying degree of accuracy, depending on the complexity of the input and the depth at which the prediction is made.

The Process

  1. Model Architecture Modification: Early exits require changes to the traditional model architecture. Exit layers are inserted at strategic intermediate points to act as potential endpoints for inference.
  2. Training: These exit layers are trained in tandem with the main model, learning to accurately predict based on the partial information processed up to their respective positions.
  3. Inference: During inference, each exit layer assesses the confidence of its prediction. If the confidence level surpasses a predefined threshold, it can serve as the final output, circumventing the need for further computation.

Challenges of Implementing Early Exits in LLMs

The primary hurdle in applying early exits to LLMs lies in the considerable complexity and computational demands of these models. Training a model with billions of parameters is an expensive endeavor, out of reach for most researchers and developers.

Transforming LLMs with Early Exits

To integrate early exits into existing LLMs, we need to consider the architecture of these additional exits and the initialization of their parameters. Early exits can vary from simple embedding layers to full Transformer layers, with each configuration offering a balance between performance and complexity. Here’s a brief overview of the types of early exits:

  • Embedding: A straightforward approach using a single linear layer to map hidden states to logits.
  • Norm: Adds a normalization module before the embedding, enhancing stability and output quality.
  • MLP: Incorporates an MLP (Multi-Layer Perceptron) before the norm layer, mirroring the structure found in the Transformer backbone.
  • Layer: The most complex option, adding an entire Transformer layer before the norm architecture.

Initialization Strategies

The initialization of early exits is critical for efficient training. Options include random initialization or duplicating parameters from corresponding modules in the pre-trained LLM. For instance, embedding and norm parameters can be directly copied, whereas MLP and Transformer layers might be initialized by duplicating existing modules from the original model.

Tuning Early Exits

Once initialized, the early-exit layers are fine-tuned using standard backpropagation, focusing on training losses from multiple exits while keeping the original LLM’s modules frozen. This process resembles training multiple shallow networks in parallel, each targeting a specific exit point. Opting for a smaller batch size can help achieve faster convergence and better generalization.


Early exiting represents a powerful method to enhance the practicality and accessibility of LLMs, making them more viable for a wider range of applications and researchers. By carefully designing and tuning these exit points, it’s possible to maintain high levels of accuracy while significantly reducing computational demands. This approach not only democratizes access to cutting-edge NLP technologies but also paves the way for more sustainable and efficient AI development practices.

As we continue to explore and refine techniques like early exiting, the future of LLMs looks promising, with potential breakthroughs in both performance and efficiency on the horizon. Whether you’re a researcher, developer, or enthusiast, understanding and implementing early exiting could be a game-changer in your work with large-scale deep learning models.