Multi-Task Learning Architecture - Interactive Visualization

Multi-task learning (MTL) trains a single model on multiple related tasks simultaneously, sharing a backbone encoder across all task heads. The key benefit is data efficiency: a data-sparse task (e.g., ranking with few labeled examples) can borrow signal from a data-rich related task (e.g., classification). Hard parameter sharing uses one shared backbone for all tasks - simple, fewer parameters, but tasks can interfere. Soft parameter sharing gives each task its own backbone but regularizes them to stay similar - more parameters but less interference. Negative transfer occurs when task objectives conflict: adding a generation head alongside a classification head can hurt both, because generation requires diverse representations while classification requires tight discriminative boundaries.

• Hard sharing: all tasks share the same encoder layers - 80% reduction in parameters vs training separate models
• Soft sharing: separate encoders per task with L2 regularization pulling weights together - reduces negative transfer
• Task loss weighting: upweight data-sparse tasks to give them more gradient signal; downweight noisy or conflicting tasks
• Negative transfer detection: monitor per-task validation loss separately - if adding task B increases task A validation loss, negative transfer is occurring

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