-
Is multi‑task learning always beneficial for real‑world tasks?
Multi‑task learning looks promising on benchmark leaderboards. But when deployed to messy real‑world datasets, sometimes single‑task fine‑tuned models beat multi‑task variants. What conditions make multi‑task setups actually worthwhile?
David Brown, Daniel Lopez and 21 others3 Comments-
Multi‑task works best when tasks share strong semantic similarities; conflicting objectives will drag overall performance down.
2 -
Task weighting becomes critical, poorly weighted loss can ruin your whole training run.
2 -
Sample size per task matters a lot. Very low‑volume tasks often get ignored during joint training.
2
-