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[2506.00486] It Takes a Good Model to Train a Good Model: Generalized Gaussian Priors for Optimized LLMs
Summary
This paper presents a novel optimization framework for large language models (LLMs) based on generalized Gaussian distributions, enhancing training efficiency and model performance.
Why It Matters
As LLMs continue to evolve, understanding their statistical properties is crucial for improving training methods. This research introduces innovative techniques that can lead to faster, more efficient models, which is vital for advancing AI capabilities in various applications.
Key Takeaways
- Generalized Gaussian distributions effectively model LLM weight and activation statistics.
- A new initialization method accelerates convergence and improves accuracy.
- The ACT method reduces redundancy in training, enhancing efficiency.
- GCT significantly lowers communication costs in distributed training setups.
- The proposed framework supports the development of scalable and hardware-aware AI systems.
Computer Science > Machine Learning arXiv:2506.00486 (cs) [Submitted on 31 May 2025 (v1), last revised 22 Feb 2026 (this version, v4)] Title:It Takes a Good Model to Train a Good Model: Generalized Gaussian Priors for Optimized LLMs Authors:Jun Wu, Patrick Huang, Jiangtao Wen, Yuxing Han View a PDF of the paper titled It Takes a Good Model to Train a Good Model: Generalized Gaussian Priors for Optimized LLMs, by Jun Wu and 3 other authors View PDF HTML (experimental) Abstract:Despite rapid progress in large language models (LLMs), the statistical structure of their weights, activations, and gradients-and its implications for initialization, training dynamics, and efficiency-remains largely unexplored. We empirically show that these quantities in LLMs are well modeled by generalized Gaussian (GG) distributions, and introduce a unified, end-to-end optimization framework grounded in this observation. Our contributions are threefold: (1) a GG-based initialization that aligns with trained model statistics, accelerating convergence and improving accuracy; (2) ACT, a progressive activation-constrained training method that reduces redundancy and propagation overhead; and (3) GCT, a gradient-constrained training algorithm that substantially lowers communication cost in distributed training. Experiments across diverse architectures demonstrate consistently smaller, faster models with minimal communication overhead that match or surpass standard baselines. By anchoring LLM optimizati...