L2hforadaptivity Guide

Enter , a paradigm shift that flips the script. Instead of forcing a model to memorize rigid targets, L2H focuses on learning adaptive label policies—usually transitioning from Hard to Soft labels—to create models that are robust, calibrat­ed, and truly adaptable.

This acts as a powerful regularizer. It prevents the model from becoming over-confident in its errors, making the decision boundary smoother and more robust to noise. l2hforadaptivity

When we train a neural network with a standard Cross-Entropy loss on hard labels, we are essentially telling the model: "There is a 100% probability this is Class A." Enter , a paradigm shift that flips the script

Different applications and use cases have varying requirements in terms of scalability, security, and decentralization. For instance, DeFi applications may require high throughput and low latency, while NFT marketplaces may prioritize decentralization and security. L2H for adaptivity addresses this need by providing a flexible and modular framework that allows developers to tailor their L2 scaling solutions to specific use cases. It prevents the model from becoming over-confident in

Instead of minimizing the loss between predictions $y$ and a fixed hard target $y_hard$, L2H introduces a learnable target $y_soft$.

Hard labels force the model to draw razor-sharp decision boundaries. While this yields high accuracy on the training set, it leads to models that cannot adapt when the data distribution shifts even slightly. This is the antithesis of adaptivity .

Hard labels are only available for the source. If the model overfits the source hard labels, it fails on the target. L2H strategies can generate for the target data. By "softening" the labels for target instances where the model is uncertain, the model gradually adapts its decision boundary to fit the new distribution without collapsing under the weight of source-domain rigidity.