Multi-Task Model Training via Task-Induced Variance Reduction
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Solution Overview
Problem
Training multi-task models often face task interference, where optimizing one task adversely affects the performance of other tasks, leading to suboptimal performance across all tasks.
Innovation Solution
The system uses simulation training data to determine task-induced variance between gradients or loss values for different tasks and iteratively adjusts shared parameters to minimize this variance, thereby reducing task interference and optimizing each task independently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multi-task models use shared parameters to perform multiple tasks simultaneously, then learning efficiency and prediction accuracy are improved, but task interference occurs where optimization of one task adversely impacts other tasks
Solution Approach 1:
The patent dynamically changes the weighting parameters of different tasks during training based on their current performance and gradient characteristics. By adjusting task weights adaptively rather than keeping them fixed, the system can prevent task interference while maintaining the benefits of shared parameters for improved learning efficiency
Solution Approach 2:
The patent implements a feedback mechanism where the training process continuously monitors gradient variance and task performance, then uses this information to adjust task weights and training dynamics. This closed-loop control allows the system to detect and correct task interference in real-time, maintaining stable performance across all tasks
2Adaptability or versatility
If multi-task models use shared parameters, then the model can exploit commonalities across tasks, but optimization of one task adversely impacts optimization of other tasks
Solution Approach 1:
The patent dynamically adjusts task weighting parameters during training based on observed gradient variance and task performance. This allows the model to maintain adaptability across multiple tasks while precisely optimizing each task by compensating for interference through parameter adjustments
Solution Approach 2:
The patent applies different weighting strategies to different tasks based on their individual characteristics and interference patterns. By treating each task's optimization precision requirements locally rather than uniformly, the system can maintain high precision for each task while preserving multi-task versatility
3Ease of operation
If standard multi-task training is used without variance reduction, then training is simpler, but task interference causes suboptimal performance across all tasks
Solution Approach 1:
The patent introduces a feedback mechanism that monitors gradient variance and task performance during training, then uses this information to adjust task weights dynamically. While this adds some complexity to the training process, it automatically detects and corrects task interference, ensuring reliable overall performance without requiring manual intervention
Data Source
AI summary
A system for training a multi-task model includes a processor and a memory in communication with the processor. The memory has a multi-task training module having instructions that, when executed by the processor, causes the processor to provide simulation training data having a plurality of samples to a multi-task model capable of performing at least a first task and a second task using at least one shared. The training module further causes the processor to determine a first value (gradience or loss) for the first task and a second value (gradience or loss) for a second task using the simulation training data and the at least one shared parameter, determine a task induced variance between the first value and the second value, and iteratively adjust the at least one shared parameter to reduce the task induced variance.