Negative Training

Negative training is a machine learning technique focused on improving model performance by explicitly instructing the model to avoid undesirable outputs during training. Current research explores its application across diverse domains, including reinforcement learning, text generation, and embedding models, employing various methods such as contrastive learning, diffusion models, and targeted negative example selection. This approach is significant because it enhances model robustness, reduces biases, and improves the quality and diversity of generated outputs, leading to more reliable and effective AI systems across numerous applications.