A novel technique that tackles the critical problem of loss spikes during large language model training, enhancing stability and performance.

• Parameter-specific adjustment replaces traditional global gradient clipping with adaptive local thresholds
• Automatic calibration through exponential moving average of gradient norms
• Enhanced convergence with theoretical guarantees and empirical validation
• Improves scalability for engineering teams building increasingly larger AI systems

This innovation directly addresses a major engineering challenge in AI, potentially reducing training costs and improving model quality by preventing destructive gradient updates during the critical pretraining phase.

AdaGC: Improving Training Stability for Large Language Model Pretraining