One potential limitation of the demonstration-based reward model is its reliance on a fixed set of demonstration data, which may not fully capture the diversity of user preferences in complex scenarios. To address this limitation, the framework could benefit from incorporating a mechanism for online demonstration collection and adaptation. This would involve continuously updating the demonstration dataset based on real-time interactions with users, allowing the model to adapt to evolving user preferences.
Another limitation is the potential bias in the demonstration data, as the demonstrations may not cover all possible scenarios or preferences. To mitigate this, techniques such as active learning or diversity sampling could be employed to ensure a more comprehensive coverage of user preferences in the demonstration dataset.
Additionally, the demonstration-based reward model may struggle with generalization to unseen scenarios or environments. To improve generalization, techniques such as data augmentation, transfer learning, or domain adaptation could be applied to enhance the robustness of the model and its ability to capture a wide range of user preferences.