How do you set the initial and final beta values for training?

Setting initial and final beta values depends on the specific training algorithm and the problem being solved. Beta typically controls the balance between competing objectives, such as exploration versus exploitation in reinforcement learning or reconstruction versus regularization in variational autoencoders (VAEs). The initial beta value is often chosen to prioritize one objective early in training, while the final beta value is adjusted to achieve a desired balance by the end of training. For example, in beta-VAEs, a lower initial beta might focus on accurate data reconstruction, while a higher final beta increases regularization to improve latent space structure. The exact values are often determined empirically, but starting with common defaults (e.g., beta=1.0 for VAEs) and adjusting based on observed behavior is a practical approach.

When choosing values, consider the trade-offs your model needs to manage. In optimization algorithms like Adam, beta1 and beta2 control the decay rates for momentum and squared gradient estimates, respectively. These are usually fixed (e.g., beta1=0.9, beta2=0.999) and not adjusted during training. However, in scenarios like beta-scheduled VAEs or curriculum learning, beta might start low (e.g., 0.1) to avoid over-regularization early on and gradually increase (e.g., to 1.0) to enforce stronger constraints as training progresses. If beta controls a loss component’s weight, such as in a multi-task learning setup, you might start with equal weighting (beta=1.0) and adjust based on task performance. Monitoring validation metrics, like reconstruction error or task-specific scores, helps identify whether beta needs to increase, decrease, or follow a predefined schedule.

For concrete examples, imagine training a VAE for image generation. Starting with beta=0.5 allows the model to focus on reconstructing inputs before slowly increasing beta to 4.0 over epochs to encourage disentangled latent representations. Alternatively, in a reinforcement learning policy gradient method, beta might start high (e.g., 2.0) to prioritize exploration and decay to 0.1 to shift toward exploitation. Tools like linear schedules, cosine annealing, or adaptive methods (e.g., based on gradient norms) can automate beta adjustments. Always document how beta changes affect outcomes—for instance, if a higher final beta reduces overfitting but harms reconstruction quality, you might need to revise the schedule. Ultimately, beta values are problem-specific, and experimentation is key to finding the right initial and final values.