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[2602.19580] Leap+Verify: Regime-Adaptive Speculative Weight Prediction for Accelerating Neural Network Training
Summary
The paper introduces Leap+Verify, a framework that enhances neural network training through speculative weight prediction, adapting to different training regimes for improved efficiency.
Why It Matters
As neural networks grow in complexity, optimizing their training processes becomes crucial. Leap+Verify offers a novel approach by predicting model weights based on training regimes, potentially leading to faster training times and better resource utilization, which is vital for advancing AI capabilities.
Key Takeaways
- Leap+Verify utilizes speculative execution to predict future model weights, enhancing training speed.
- The framework identifies three training regimes: chaotic, transition, and stable, optimizing predictions accordingly.
- Finite-difference predictors outperform momentum-based methods, achieving significant acceptance rates in stable regimes.
- Larger models show a complex relationship between predictability and training stability, impacting overall training efficiency.
- Cross-seed results indicate high consistency in validation loss, reinforcing the framework's reliability.
Computer Science > Machine Learning arXiv:2602.19580 (cs) [Submitted on 23 Feb 2026] Title:Leap+Verify: Regime-Adaptive Speculative Weight Prediction for Accelerating Neural Network Training Authors:Jeremy McEntire View a PDF of the paper titled Leap+Verify: Regime-Adaptive Speculative Weight Prediction for Accelerating Neural Network Training, by Jeremy McEntire View PDF HTML (experimental) Abstract:We introduce Leap+Verify, a framework that applies speculative execution -- predicting future model weights and validating predictions before acceptance -- to accelerate neural network training. Inspired by speculative decoding in language model inference and by the Automatically Scalable Computation (ASC) architecture for program execution, Leap+Verify decomposes training into three dynamically detected regimes (chaotic, transition, stable) using activation-space cosine similarity as a real-time Lyapunov proxy signal. Within each regime, analytic weight predictors (momentum, linear, quadratic extrapolation) attempt to forecast model parameters K training steps ahead; predictions are accepted only when validated against a held-out loss criterion. We evaluate Leap+Verify on GPT-2 124M and Qwen 2.5-1.5B trained on WikiText-103 across five random seeds, sweeping prediction depth K in {5, 10, 25, 50, 75, 100}. Momentum-based prediction (Adam moment extrapolation) fails catastrophically at both scales, with predicted losses exceeding actuals by 100-10,000x -- a universal norm explo...
