proportional task sampling

**Proportional task sampling** is **sampling tasks in proportion to dataset size or example count** - Larger tasks receive more updates, matching raw data availability. **What Is Proportional task sampling?** - **Definition**: Sampling tasks in proportion to dataset size or example count. - **Core Mechanism**: Larger tasks receive more updates, matching raw data availability. - **Operational Scope**: It is applied during data scheduling, parameter updates, or architecture design to preserve capability stability across many objectives. - **Failure Modes**: Small but critical tasks can be undertrained when pure proportional rules are used. **Why Proportional task sampling 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**: Add minimum sampling floors for strategic tasks and validate that key low-volume tasks meet quality targets. - **Validation**: Track per-task gains, retention deltas, and interference metrics at every major checkpoint. Proportional task sampling is **a core method in continual and multi-task model optimization** - It offers simple scalable scheduling for large task portfolios.