RCfD can be extended to handle settings where demonstrations are not available for all prompts by implementing a method to predict the reward of the demonstration. This can be achieved by training a regressor or a separate model to estimate the reward that would be assigned to a completion based on the prompt alone. By using this predicted reward as a proxy for the demonstration reward, RCfD can still align the model's behavior with the desired outcomes even in the absence of explicit demonstrations. This approach allows RCfD to generalize to prompts without specific demonstrations and adapt to a wider range of scenarios where demonstration data may be limited or unavailable.