LLM Training is Still Extremely Fragile: Insider Reveals High Failure Rates
ivan_bezdomny · x · 2026-08-02
An experienced practitioner highlighted that large-scale pre-training and post-training runs still fail constantly. To push the model's capabilities, engineers constantly operate on the fragile edge between maximal learning and basic stability.
LLM consumers often don't see this underlying fragility. Furthermore, as soon as a training process is stabilized, teams immediately try to push boundaries further through techniques like quantization.
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