Loss Spikes are sudden, sharp increases in training loss that temporarily disrupt the training process — the loss dramatically increases for a few steps or epochs, then rapidly recovers, often to a value lower than before the spike, suggesting the model is transitioning between different solution basins.

Loss Spike Characteristics

• Magnitude: Can be 2-100× the pre-spike loss — sometimes dramatic increases.
• Recovery: Loss typically recovers within a few hundred to a few thousand steps.
• Causes: Large learning rates, numerical instability (fp16 overflow), batch composition, data quality issues, or representation reorganization.
• Beneficial: Some loss spikes precede improved performance — the model "jumps" to a better region of the loss landscape.

Why It Matters

• Training Stability: Loss spikes can derail training if severe — require monitoring and mitigation (gradient clipping, loss scaling).
• LLM Training: Large language model training frequently experiences loss spikes — especially at scale.
• Learning Signal: Some spikes indicate the model is learning new, qualitatively different representations — a positive sign.

Loss Spikes are turbulence in training — sudden loss increases that can signal either instability issues or beneficial representation transitions.