A helps B while B hurts A: directed transfer in instruction-tuning mixture
The research introduces a directed transfer map to measure how different tasks help or harm a target language model during instruction tuning. It found that task selection is not symmetric; some tasks actively benefit the target while others actively hurt it. This data-driven map predicts mixture performance before training, allowing for smarter task selection.