Scaling Laws in Large Language Model Training Accurately Predict Downstream Task Performance

Predicting the performance of large language models on real-world tasks remains a significant challenge, with traditional scaling laws often relying on indirect measures like pretraining loss. Jakub Krajewski, Amitis Shidani, and Dan Busbridge, alongside Sam Wiseman and Jason Ramapuram from Apple, now demonstrate a direct link between training investment and benchmark accuracy. Their work reveals that a simple power law accurately predicts performance on multiple popular downstream tasks, offering a more reliable method than previous approaches prone to accumulating errors. This achievement not only improves our ability to forecast language model capabilities, but also introduces new functional forms that account for varying model sizes and the impact of repeated sampling during inference, validated through training models with up to 17 billion parameters on datasets comprising 350 billion tokens. Scaling Laws and Maximum Language Model Accuracy This research investigates scaling laws for language models, exploring the relationships between model size, the amount of training data, and performance on various benchmarks. Scientists examined different mathematical formulations to best describe how performance improves with increased scale, and rigorously validated these laws to accurately predict performance even with larger models. A key focus was determining the maximum achievable accuracy on each benchmark, acknowledging inherent task and data limitations.

The team considered benchmarks like ARC-E, ARC-C, SciQ, and HellaSwag, establishing random guess baselines for comparison. The study revealed that many benchmarks allow for continued improvement with increased scale, while others exhibit a clear maximum accuracy, estimating meaningful maximum accuracy values for benchmarks like PIQA, HellaSwag, and LAMBADA. This work provides valuable insights into the limits of language model performance and guides future research towards more efficient scaling strategies.

Training Budget Predicts Downstream Accuracy This study pioneers a direct framework for predicting the performance of large language models on downstream tasks, moving beyond traditional methods. Researchers trained models with up to 17 billion parameters on datasets containing up to 350 billion tokens, and analyzed the results across twelve popular benchmarks. This work demonstrates that, when the ratio of tokens to parameters remains constant, downstream log accuracy scales predictably with a simple power law directly related to the training budget, measured in floating point operations (FLOPs).

The team systematically varied the training budget and meticulously measured the resulting accuracy on diverse benchmarks including ARC-E, ARC-C, and SciQ, fitting a power law to these data to reveal a strong correlation between training FLOPs and benchmark scores. This approach offers a simpler and more accurate alternative to previous two-stage methods. The study validated this law using a comprehensive suite of 130 experiments, demonstrating its ability to accurately forecast model capabilities from the training budget, and extends this scaling law to account for variations in the token-to-parameter ratio. By releasing the complete set of pretraining losses and downstream evaluation results, the team facilitates reproducibility and encourages further research.

Training Compute Predicts Downstream Accuracy This work demonstrates that downstream benchmark accuracy scales predictably with training compute, establishing a direct relationship between the amount of training and final performance. Researchers developed a simple scaling law that accurately models this connection, offering a systematic and efficient approach to developing large-scale models. By revealing this predictable scaling behaviour, the team presents the advancement of downstream capabilities as a measurable consequence of increased scale. The findings reconcile previously observed predictable and unpredictable scaling phenomena, showing that predictability emerges when considering a fixed data mixture and beyond a task-specific threshold. While the results demonstrate a clear trend, the authors acknowledge limitations in the calibration of prediction intervals, suggesting that bootstrap-based intervals and floor estimation would improve decision-making. They also note the need for detailed analyses of trade-offs between training and inference compute, particularly for code-related tasks.

The team has released comprehensive pretraining losses and downstream evaluation results to support reproducibility and encourage further investigation. 👉 More information 🗞 Revisiting the Scaling Properties of Downstream Metrics in Large Language Model Training 🧠 ArXiv: https://arxiv.org/abs/2512.08894 Tags: Rohail T. As a quantum scientist exploring the frontiers of physics and technology. My work focuses on uncovering how quantum mechanics, computing, and emerging technologies are transforming our understanding of reality. I share research-driven insights that make complex ideas in quantum science clear, engaging, and relevant to the modern world. Latest Posts by Rohail T.: Nanosecond Pulsed-Laser Densification Reduces Diamond Dislocation Defects by 65%, Recovering Optical Performance December 11, 2025 AI Verifiers Achieve 99% Accuracy in Label-Free Visual Reasoning December 11, 2025 Neutron Star-White Dwarf Collisions Studied: Conditions for Thermonuclear Explosions and Thorne-Zytkow Object Formation December 11, 2025