In the HuggingFace's TrainingArguments and SFTConfig (inheriting from TrainingArguments), there are two arguments for initializing SFTConfig():

• group_by_length: Whether or not to group together samples of roughly the same length in the training dataset (to minimize padding applied and be more efficient). Only useful if applying dynamic padding.
• packing: Whether to pack multiple sequences into a fixed-length format. Uses max_length to define sequence length.

config = SFTConfig(..., 
                   group_by_length=True, 
                   packing=True, ...)

Those arguments serve the purpose of reducing the effort to filling in paddings. However, when packing=True, it is pointless to use group_by_length=True. Shall we use both to increase the training performance? Do they counteract each other?