Mechanism-Aligned Observability for Training Instability of Large Language Models
The authors developed a method for detecting training instability early by creating internal monitors aligned with how specific components of a model compute. They showed that for FlashAttention, monitoring weight updates reveals low-rank structures caused by errors, and for MoE routers, monitoring per-token routing entropy detects impending collapse. This approach allows identifying faults before they ruin global performance.