One potential limitation of the Granular Contrastive Reinforcement Learning (GCRL) approach is its reliance on the quality and diversity of negative samples. If the negative answers provided are not sufficiently varied or representative of the potential incorrect answers, the model may not learn effectively to distinguish between correct and incorrect responses. To address this, the framework could implement a dynamic sampling strategy that continuously updates the pool of negative samples based on the model's performance, ensuring that it encounters a wide range of challenging examples throughout training.
Another drawback is the computational complexity associated with the contrastive learning process, which may lead to longer training times and increased resource requirements. This can be mitigated by optimizing the training process through techniques such as mini-batch training or using more efficient algorithms for negative sampling. Additionally, the reward function's design in GCRL may need to be fine-tuned to balance the trade-off between precision and recall, ensuring that the model does not overly focus on minimizing false positives at the expense of missing relevant answers. Regular evaluations and adjustments to the reward structure can help maintain this balance.