Test-Time Scaling Makes Overtraining Compute-Optimal

• URL: http://arxiv.org/abs/2604.01411v1
• Date: Wed, 01 Apr 2026 21:17:32 GMT
• Title: Test-Time Scaling Makes Overtraining Compute-Optimal
• Abstract summary: We present Train-to-Test ($T2$) scaling laws that jointly optimize model size, training tokens, and number of inference samples.<n>$T2$ modernizes pretraining scaling laws with pass@$k$ modeling used for test-time scaling, then jointly optimize pretraining and test-time decisions.
• Score: 23.520624926542755
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modern LLMs scale at test-time, e.g. via repeated sampling, where inference cost grows with model size and the number of samples. This creates a trade-off that pretraining scaling laws, such as Chinchilla, do not address. We present Train-to-Test ($T^2$) scaling laws that jointly optimize model size, training tokens, and number of inference samples under fixed end-to-end budgets. $T^2$ modernizes pretraining scaling laws with pass@$k$ modeling used for test-time scaling, then jointly optimizes pretraining and test-time decisions. Forecasts from $T^2$ are robust over distinct modeling approaches: measuring joint scaling effect on the task loss and modeling impact on task accuracy. Across eight downstream tasks, we find that when accounting for inference cost, optimal pretraining decisions shift radically into the overtraining regime, well-outside of the range of standard pretraining scaling suites. We validate our results by pretraining heavily overtrained models in the optimal region that $T^2$ scaling forecasts, confirming their substantially stronger performance compared to pretraining scaling alone. Finally, as frontier LLMs are post-trained, we show that our findings survive the post-training stage, making $T^2$ scaling meaningful in modern deployments.

Related papers

• Scaling Laws for Optimal Data Mixtures [36.161727115217964]
  We propose a systematic method to determine the optimal data mixture for any target domain using scaling laws.<n>We validate the universality of these scaling laws by demonstrating their predictive power in three distinct and large-scale settings.
  arXiv  Detail & Related papers  (2025-07-12T21:16:08Z)
• The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws [51.608402959163925]
  We present the first systematic exploration of optimal sparse pre-training configurations for large language models.<n>We find that initiating pruning at 25% of total training compute and concluding at 75% achieves near-optimal final evaluation loss.<n>We propose a new scaling law that modifies the Chinchilla scaling law to use the average parameter count over pre-training.
  arXiv  Detail & Related papers  (2025-01-21T20:23:22Z)
• P$^2$ Law: Scaling Law for Post-Training After Model Pruning [25.07013858614455]
  Pruning has become a widely adopted technique for reducing the hardware requirements of large language models (LLMs)<n>To recover model performance after pruning, post-training is commonly employed to mitigate the resulting performance degradation.<n>To balance post-training cost and model performance, it is necessary to explore the optimal amount of post-training data.
  arXiv  Detail & Related papers  (2024-11-15T15:28:42Z)
• Scaling Laws for Precision [73.24325358259753]
  We devise "precision-aware" scaling laws for both training and inference.<n>For inference, we find that the degradation introduced by post-training quantization increases as models are trained on more data.<n>For training, our scaling laws allow us to predict the loss of a model with different parts in different precisions.
  arXiv  Detail & Related papers  (2024-11-07T00:10:10Z)
• Language models scale reliably with over-training and on downstream tasks [121.69867718185125]
  Scaling laws are useful guides for derisking expensive training runs. However, there remain gaps between current studies and how language models are trained. In contrast, scaling laws mostly predict loss on inference, but models are usually compared on downstream task performance.
  arXiv  Detail & Related papers  (2024-03-13T13:54:00Z)
• A Dynamical Model of Neural Scaling Laws [79.59705237659547]
  We analyze a random feature model trained with gradient descent as a solvable model of network training and generalization. Our theory shows how the gap between training and test loss can gradually build up over time due to repeated reuse of data.
  arXiv  Detail & Related papers  (2024-02-02T01:41:38Z)
• An Emulator for Fine-Tuning Large Language Models using Small Language Models [91.02498576056057]
  We introduce emulated fine-tuning (EFT), a principled and practical method for sampling from a distribution that approximates the result of pre-training and fine-tuning at different scales. We show that EFT enables test-time adjustment of competing behavioral traits like helpfulness and harmlessness without additional training. Finally, a special case of emulated fine-tuning, which we call LM up-scaling, avoids resource-intensive fine-tuning of large pre-trained models by ensembling them with small fine-tuned models.
  arXiv  Detail & Related papers  (2023-10-19T17:57:16Z)
• nanoLM: an Affordable LLM Pre-training Benchmark via Accurate Loss Prediction across Scales [65.01417261415833]
  We present an approach to predict the pre-training loss based on our observations that Maximal Update Parametrization (muP) enables accurate fitting of scaling laws. With around 14% of the one-time pre-training cost, we can accurately forecast the loss for models up to 52B. Our goal with nanoLM is to empower researchers with limited resources to reach meaningful conclusions on large models.
  arXiv  Detail & Related papers  (2023-04-14T00:45:01Z)
• Scaling Laws for Acoustic Models [7.906034575114518]
  Recent work has shown that autoregressive generative models with cross-entropy objective functions exhibit smooth power-law relationships. We show that acoustic models trained with an auto-predictive coding loss behave as if they are subject to similar scaling laws.
  arXiv  Detail & Related papers  (2021-06-11T18:59:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.