How do large-scale TensorFlow systems prevent model feedback loops from corrupting training data over time?

They use delayed labeling, exploration strategies, and unbiased data logging to break reinforcement loops.

In production ML systems, predictions influence user behavior, which in turn becomes training data. This creates a feedback loop where the model reinforces its own biases. TensorFlow pipelines mitigate this using counterfactual logging, randomized exploration (epsilon-greedy or Thompson sampling), and delayed ground-truth labeling. Without these, data distribution collapses into self-reinforced bias.