task-specific heads

**Task-specific heads** is **output modules tailored to each task while a common backbone provides shared features** - Heads map shared representations to task-native outputs such as labels rankings or structured predictions. **What Is Task-specific heads?** - **Definition**: Output modules tailored to each task while a common backbone provides shared features. - **Core Mechanism**: Heads map shared representations to task-native outputs such as labels rankings or structured predictions. - **Operational Scope**: It is applied during data scheduling, parameter updates, or architecture design to preserve capability stability across many objectives. - **Failure Modes**: Weak head design can bottleneck strong shared features and hide transfer gains. **Why Task-specific heads Matters** - **Retention and Stability**: It helps maintain previously learned behavior while new tasks are introduced. - **Transfer Efficiency**: Strong design can amplify positive transfer and reduce duplicate learning across tasks. - **Compute Use**: Better task orchestration improves return from fixed training budgets. - **Risk Control**: Explicit monitoring reduces silent regressions in legacy capabilities. - **Program Governance**: Structured methods provide auditable rules for updates and rollout decisions. **How It Is Used in Practice** - **Design Choice**: Select the method based on task relatedness, retention requirements, and latency constraints. - **Calibration**: Optimize head capacity and loss scaling per task so backbone and heads co-adapt effectively. - **Validation**: Track per-task gains, retention deltas, and interference metrics at every major checkpoint. Task-specific heads is **a core method in continual and multi-task model optimization** - They provide clean specialization boundaries on top of shared infrastructure.