#model-training

#model-training

"Conventionally, your data is either in or out. Once I train on that data, you lose control. And you have no way out, unless you force me to go through another multi-million-dollar round of training."

Keras offers a high-level API built on top of tf.Module, enhancing model complexity through optional losses, metrics, and configurable saving options, fostering seamless training.

In our head-to-head experiments, we observe that offline contrastive training offers a more valuable training signal than traditional SFT methods, demonstrating its effectiveness in model performance.

The RLHF pipeline comprises supervised fine-tuning, preference sampling, and reward learning, followed by reinforcement learning optimization, enhancing model effectiveness in decision making.