What Happens During the Loss Plateau? Understanding Abrupt Learning in Transformers (2506.13688v1)

Abstract: Training Transformers on algorithmic tasks frequently demonstrates an intriguing abrupt learning phenomenon: an extended performance plateau followed by a sudden, sharp improvement. This work investigates the underlying mechanisms for such dynamics, primarily in shallow Transformers. We reveal that during the plateau, the model often develops an interpretable partial solution while simultaneously exhibiting a strong repetition bias in their outputs. This output degeneracy is accompanied by internal representation collapse, where hidden states across different tokens become nearly parallel. We further identify the slow learning of optimal attention maps as a key bottleneck. Hidden progress in attention configuration during the plateau precedes the eventual rapid convergence, and directly intervening on attention significantly alters plateau duration and the severity of repetition bias and representational collapse. We validate that these identified phenomena-repetition bias and representation collapse-are not artifacts of toy setups but also manifest in the early pre-training stage of LLMs like Pythia and OLMo.

• The paper presents a detailed analysis of the loss plateau in Transformer training, revealing a strong repetition bias and collapse of internal representations.
• The paper highlights that slow formation of optimal attention maps acts as a bottleneck to resolving the loss plateau and triggering abrupt performance improvements.
• The paper shows that targeted interventions in attention map learning can reduce plateau duration, with findings validated across various models and algorithmic tasks.

Understanding Abrupt Learning in Transformers via Loss Plateau Dynamics

The paper "What Happens During the Loss Plateau? Understanding Abrupt Learning in Transformers" explores the intriguing phenomenon of abrupt learning dynamics observed during Transformer training, particularly on algorithmic tasks. The research seeks to unravel the mechanisms behind an extended period of performance stagnation, known as the loss plateau, which is followed by a sudden leap in performance. This behavior is prevalent even in shallow Transformers and is linked to the broader phenomenon of emergence, where model capabilities arise unexpectedly with the addition of parameters, data, or training steps.

Key Insights and Contributions

1. Understanding the Loss Plateau: The paper provides a detailed examination of what transpires during the loss plateau phase. It is identified that during this time, the model tends to develop a partial solution, often driven by a significant repetition bias in output, accompanied by a collapse in internal representations, characterized by nearly parallel hidden states across different tokens.
2. Attention Map as a Bottleneck: A crucial finding is that the slow learning of optimal attention maps is a primary bottleneck in resolving the loss plateau. The paper shows that while the optimal attention configuration progresses during plateau, it is the delayed learning of this configuration that prolongs the plateau.
3. Intervening Learning Dynamics: The research demonstrates that interventions in the attention map learning process, such as biasing the attention scores, can significantly alter the duration of the loss plateau and impact the severity of repetition bias and representational collapse.
4. Validation Across Models: The paper confirms that the identified phenomena are not artifacts of simplistic toy setups but are also prevalent in the early training stages of larger models including LLMs such as Pythia and OLMo. This suggests that repetition bias and representation collapse are inherent characteristics of Transformer training dynamics.
5. Generalization Across Tasks: The findings extend to various algorithmic tasks beyond simple setups, including multi-digit addition, histogram tasks, and permutation tasks, indicating the broad applicability of the insights garnered.

Implications and Future Directions

The implications of this research are twofold—practical and theoretical. Practically, understanding the dynamics of loss plateaus and the role of attention mechanisms can inform the design of more efficient training regimes for Transformers, potentially reducing the training time and computational resources required. By actively managing the learning of attention maps, practitioners can mitigate the effects of early-stage biases and expedite the convergence of Transformer models.

Theoretically, this work paves the way for a deeper exploration into the emergent phenomena within neural architectures, particularly the pivotal role of attention mechanisms in dictating the learning progression. Future research could explore more sophisticated interventions in attention learning or delve into the mathematics underpinning these emergent capabilities.

In summary, this paper makes significant strides in decoding the enigma of loss plateaus and abrupt learning in Transformers. By shedding light on this nuanced aspect of model training, it not only enhances our understanding of model dynamics but opens avenues for optimizing the training processes of next-generation AI systems.