Mixture-of-Experts Overfitting with Data Repetition
This research investigates how Mixture-of-Experts (MoE) models, known for their computational efficiency, behave when trained with repeated data. It reveals that MoEs are more susceptible to overfitting with data repetition compared to dense models, especially as sparsity increases. The study explores potential remedies and the underlying mechanisms of this overfitting.
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This research investigates how Mixture-of-Experts (MoE) models, known for their computational efficiency, behave when trained with repeated data. It reveals that MoEs are more susceptible to overfitting with data repetition compared to dense models, especially as sparsity increases. The study explores potential remedies and the underlying mechanisms of this overfitting.
Imagine a study group where each member (expert) specializes in a different topic. If they keep reviewing the same limited set of practice problems (repeated data), each specialist might become overly focused on those specific problems, making them less adaptable to new, unseen problems. The more specialized (sparse) the group, the quicker this over-specialization occurs, leading to poorer performance on diverse tests.
How MoE Overfitting with Data Repetition Works
- 11. MoE models, which use specialized 'experts' for different parts of the input, are trained on datasets where some data points are intentionally repeated.
- 22. As data repetition increases, MoE models show a more rapid decline in performance compared to dense models, indicating increased overfitting.
- 33. This degradation is more pronounced in highly sparse MoE configurations, suggesting that the degree of specialization contributes to the problem.
- 44. Internal analysis reveals that expert routing stabilizes early, and increased expert specialization correlates with this overfitting to repeated data.
- 55. Regularization techniques like dropout can mitigate this effect, allowing MoEs to perform better even with significant data repetition, but full recovery to all-unique data performance is not achieved.
MoE Degradation ∝ Sparsity × Data Repetition
This conceptual relationship indicates that the tendency for Mixture-of-Experts (MoE) models to degrade in performance due to overfitting increases proportionally with both the model's sparsity (how specialized its experts are) and the rate at which training data is repeated.
An 80M parameter dense model can handle data repeated over 8 times with minimal performance loss. In contrast, MoE models of similar active parameter count begin to suffer at 4x repetition and significantly underperform dense models after 32x repetition, losing their initial performance advantages.
Mixture-of-Experts models are significantly more prone to overfitting from data repetition than dense models, with sparsity being a key exacerbating factor, driven by early routing stabilization and expert over-specialization.