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The Slingshot Effect: A Late-Stage Optimization Anomaly in Adam-Family of Optimization Methods

This paper presents the Slingshot Effect, a phenomenon in neural network optimization occurring in late training stages. It involves cyclic phase transitions between stable and unstable training regimes, demonstrated by cyclic behavior of the last layer’s weight norm. The effect can be replicated in various settings, but its implications remain unexplored.

 The Slingshot Effect: A Late-Stage Optimization Anomaly in Adam-Family of Optimization Methods

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Adaptive Gradient Methods and The Slingshot Effect

Adaptive gradient methods like Adam are crucial for optimizing neural networks, especially when working with Transformers. Our paper introduces the Slingshot Effect, a unique anomaly that occurs in the later stages of training. This phenomenon involves cyclic phase transitions between stable and unstable training, which is identifiable through the cyclic behavior of the last layer’s weights.

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