MIT and IBM Researchers Develop Framework to Optimize AI Scaling Laws for Efficient LLM Training and Budget Use

Building effective AI scaling laws is essential for optimizing large language model (LLM) training and maximizing limited budgets. As training LLMs can cost millions of dollars, researchers need reliable methods to predict performance before committing to large-scale experiments. Scaling laws offer a way to estimate the performance of a large target model by analyzing smaller, cheaper models from the same family. These laws typically relate model loss to key variables such as the number of parameters and training tokens, allowing teams to make informed decisions about architecture, data, and compute allocation. However, the challenge lies in the vast number of possible scaling law configurations and the lack of consistency across studies. Most prior work focused on single model families or isolated experiments, making it difficult to generalize findings. To address this, researchers from MIT and the MIT-IBM Watson AI Lab conducted a comprehensive meta-analysis of over 1,000 scaling laws derived from a large dataset of 485 pre-trained models across 40 different families, including LLaMA, OPT, Pythia, Bloom, and T5-Pile. The team collected detailed data on model architectures, training checkpoints, computational costs (FLOPs), training epochs, and performance metrics—amounting to 1.9 million data points. By fitting scaling laws across diverse models and training regimes, they evaluated predictive accuracy using absolute relative error (ARE), measuring how close predictions were to actual performance of fully trained large models. Their analysis revealed several practical insights. First, a 4% ARE is near the theoretical limit due to random seed variability, but even 20% ARE remains useful for strategic decision-making. Including intermediate training checkpoints—especially those beyond 10 billion tokens—significantly improves prediction accuracy. Early training stages are too noisy to be reliable. The researchers also found that training five models across a range of sizes provides a strong foundation for robust scaling laws, and that partially training a target model to about 30% of its dataset can yield accurate predictions while saving costs. Another key finding was that scaling laws can be effectively transferred across similar model families, especially when architectures are comparable. However, this approach is less reliable for encoder-decoder models. The team also discovered that scaling laws built from large models can accurately predict performance in smaller models—a counterintuitive result that challenges the idea that small and large models behave fundamentally differently. Perhaps most surprisingly, the researchers found that intermediate training states from a fully trained model can serve as independent data points for prediction, effectively offering free training data. This insight allows teams to extract more value from existing experiments without additional cost. Looking ahead, the team plans to extend their work to inference-time scaling laws—predicting how much computational effort a model needs at runtime to produce high-quality responses. As users interact with models in real time, understanding how long a model should “think” to deliver accurate answers will become increasingly important. The ability to forecast inference costs could significantly improve efficiency and user experience. This research provides a systematic, data-driven framework for building and applying scaling laws, empowering researchers—especially those with limited resources—to make smarter, more cost-effective decisions in LLM development. By turning scaling laws from ad hoc tools into reliable, reproducible methods, the work advances both the science and practical application of AI.