Focal Loss for LLMs: An Untapped Potential or a Hidden Pitfall?
Published:Jan 3, 2026 15:05
•1 min read
•r/MachineLearning
Analysis
The post raises a valid question about the applicability of focal loss in LLM training, given the inherent class imbalance in next-token prediction. While focal loss could potentially improve performance on rare tokens, its impact on overall perplexity and the computational cost need careful consideration. Further research is needed to determine its effectiveness compared to existing techniques like label smoothing or hierarchical softmax.
Key Takeaways
- •Focal loss is designed to address class imbalance by focusing on hard examples.
- •LLM training involves predicting the next token, which can be viewed as a highly imbalanced classification task.
- •The effectiveness of focal loss in LLM pretraining remains largely unexplored.
Reference
“Now i have been thinking that LLM models based on the transformer architecture are essentially an overglorified classifier during training (forced prediction of the next token at every step).”