Iterative Alignment › Practical Considerations

Deploying iterative alignment in production requires attention to several practical issues that our simplified examples gloss over.

How often should you iterate? Too frequent iteration (e.g., after every batch) prevents the model from fully learning from current data. Too infrequent iteration (e.g., once per quarter) allows significant distribution shift. Most practitioners find weekly to monthly iteration cycles work well, depending on the rate of model deployment and feedback collection.

Rather than training exclusively on new preference data each iteration, mixing in data from previous iterations helps prevent catastrophic forgetting of earlier lessons. A common approach maintains a replay buffer of preferences from all iterations, sampling proportionally from recent and historical data.

Even when using AI feedback for most iterations, periodic human evaluation checkpoints catch drifts that AI judges miss. These checkpoints might occur every few iterations or whenever automated metrics suggest significant behavioral changes.

Sometimes iterative training goes wrong: the model develops undesirable behaviors or loses capabilities. Having clear rollback criteria and maintaining checkpoints from each iteration enables quick recovery. Define specific metrics that trigger automatic rollback: capability benchmark drops below threshold, diversity falls too low, or human evaluators flag concerning patterns.