Training on your test set is always a bad move, because it means you can’t usefully measure what you built. You need to hold something out, something the training process has never seen.
This isn’t just an LLM thing. You should consider it for something as simple as a linear regression.
Otherwise your training process overfits to the available training data, and your model looks good until it encounters new, real world data. Then performance shifts considerably from your expectations.
Training on your test set is always a bad move, because it means you can’t usefully measure what you built. You need to hold something out, something the training process has never seen.
This isn’t just an LLM thing. You should consider it for something as simple as a linear regression.
Otherwise your training process overfits to the available training data, and your model looks good until it encounters new, real world data. Then performance shifts considerably from your expectations.