FAMO: A Fast Optimization Method for Multitask Learning (MTL) that Mitigates the Conflicting Gradients using O(1) Space and Time

Multitask Learning: Challenges and Solutions

Challenges in Multitask Learning

Multitask learning (MLT) involves training a single model to perform multiple tasks simultaneously, which can pose challenges in managing large models and optimizing across tasks. Balancing task performance and optimization strategies is critical for effective MLT.

Existing Solutions

Existing solutions for mitigating the under-optimization problem in multitask learning involve gradient manipulation techniques. However, these methods can become computationally expensive with many tasks and model size.

Introducing FAMO

Fast Adaptive Multitask Optimization (FAMO) dynamically adjusts task weights to ensure a balanced loss decrease across tasks, leveraging loss history instead of computing all task gradients. FAMO offers O(1) space and time complexity per iteration and demonstrates comparable or superior performance to existing methods across various MLT benchmarks, with significant computational efficiency improvements.

Key Contributions of FAMO

FAMO aims to decrease all task losses at an equal rate as much as possible and amortizes computation over time. It achieves this by updating task weights based on the change in log losses and approximating the gradient, leading to improved performance without extensive gradient computations.

Evaluation and Conclusion

FAMO consistently performed well across various MLT scenarios, showcasing its effectiveness and efficiency. With its balanced loss decrease approach and efficient optimization strategy, FAMO offers a valuable contribution to the field of multitask learning, paving the way for more scalable and effective machine learning models.

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