Engineering brief
Silent failures at scale: why your training code probably has undetected bugs
At a glance
- Relevance
- Practical value
- Warnings
- None
Poolside's pretraining team caught a race condition corrupting 0.5% of gradients. Most teams lack the hash checks to detect this.
Silent training failures can destroy weeks of compute. Observability is a budget line item.
Summary
Poolside's talk reveals that scaling pretraining surfaces silent failures like broken GPUs
causing data corruption and numerical precision issues that halt convergence. These problems
are invisible without custom observability like weight hash checks across replicas.
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