MTL

MTL jointly optimizes shared representations across a fixed task set, exploiting inter-task relatedness to improve generalization on each individual task ^[37]. The defining coordinate is Task Distribution: the training corpus spans a predetermined collection of tasks simultaneously available in memory, rather than a single-task labeled dataset (Full FT, , Large Labeled). Its structural distinction from Meta-Learning is that, while both share Task Distribution, MTL's task set is statically fixed at training time rather than dynamically drawn from a distribution. The Whole-Model scope is the critical consequence of the joint optimization: because all task-specific output heads share the same backbone parameters, there is no modular isolation boundary between tasks—any change to the shared encoder propagates across all tasks simultaneously. This means modifying the task set, task-weighting schedule, or encoder architecture requires full-system regression testing across all tasks to verify that positive transfer to new tasks has not induced negative interference on existing ones. At the MLLM tier, MTL is the foundational architecture underlying multimodal instruction tuning—a unified backbone must serve heterogeneous vision, audio, and text task heads simultaneously. MTL requires comprehensive cross-task regression testing at every task-set modification boundary; the inter-task interference structure must be explicitly characterized and documented, as negative transfer to any task constitutes a behavioral regression on that task's validated performance envelope.