Training a neural reward model for visual program synthesis involves creating a model that can evaluate the quality of the generated programs based on various criteria. To train such a model, one approach could be to use reinforcement learning techniques where the reward model learns to assign rewards based on the correctness, efficiency, and other desirable attributes of the synthesized programs. This training process would involve exposing the reward model to a large dataset of visual programs along with their ground truth outputs, allowing it to learn the patterns that lead to high-quality programs.
By having a neural reward model that can provide fine-grained rewards, the self-training process in visual program synthesis can be significantly enhanced. Fine-grained rewards can offer more detailed feedback to the language model during training, guiding it towards generating programs that not only produce correct outputs but also exhibit desirable characteristics such as logical reasoning, efficiency, and clarity. This level of detailed feedback can help the language model understand the nuances of program synthesis better and refine its capabilities more effectively than coarse rewards alone.