What are the differences between inference efficiency and training efficiency in LLMs?

Inference efficiency refers to the ability of a model to generate predictions quickly and with minimal resource usage, while training efficiency pertains to how effectively a model learns from data during the training phase. Inference efficiency is achieved through techniques that optimize the model's performance during prediction, such as parallel decoding or token pruning, which help reduce compute and latency. In contrast, training efficiency focuses on minimizing the computational resources and time required to train the model effectively, often involving strategies like data augmentation or more efficient training algorithms. For instance, research on CoRefine demonstrates that by using a confidence-guided self-refinement approach, models can achieve high accuracy during inference without incurring the high computational costs typically associated with extensive pre-filling processes. This method illustrates a significant improvement in inference efficiency while maintaining competitive performance, highlighting the distinct challenges and strategies associated with both inference and training efficiency in LLMs.