Understanding the Role of Training Data in Test-Time Scaling

• URL: http://arxiv.org/abs/2510.03605v1
• Date: Sat, 04 Oct 2025 01:38:48 GMT
• Title: Understanding the Role of Training Data in Test-Time Scaling
• Authors: Adel Javanmard, Baharan Mirzasoleiman, Vahab Mirrokni,
• Abstract summary: We study the performance of test-time scaling for transformers trained on an in-context weight prediction task for linear regression.<n>We show that training on a diverse, relevant, and hard set of tasks results in best performance for test-time scaling.
• Score: 56.12341509545198
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Test-time scaling improves the reasoning capabilities of large language models (LLMs) by allocating extra compute to generate longer Chains-of-Thoughts (CoTs). This enables models to tackle more complex problem by breaking them down into additional steps, backtracking, and correcting mistakes. Despite its strong performance--demonstrated by OpenAI's o1 and DeepSeek R1, the conditions in the training data under which long CoTs emerge, and when such long CoTs improve the performance, remain unclear. In this paper, we study the performance of test-time scaling for transformers trained on an in-context weight prediction task for linear regression. Our analysis provides a theoretical explanation for several intriguing observations: First, at any fixed test error, increasing test-time compute allows us to reduce the number of in-context examples (context length) in training prompts. Second, if the skills required to solve a downstream task are not sufficiently present in the training data, increasing test-time compute can harm performance. Finally, we characterize task hardness via the smallest eigenvalue of its feature covariance matrix and show that training on a diverse, relevant, and hard set of tasks results in best performance for test-time scaling. We confirm our findings with experiments on large, nonlinear transformer architectures.

Related papers

• Towards Thinking-Optimal Scaling of Test-Time Compute for LLM Reasoning [108.07030347318624]
  We show that scaling with longer Chain of Thoughts (CoTs) can indeed impair the reasoning performance of Large Language Models (LLMs) in certain domains.<n>We propose a Thinking- Optimal Scaling strategy to teach models to adopt different reasoning efforts for deep thinking.<n>Our self-improvement models built upon Qwen2.5-32B-Instruct outperform other distillation-based 32B o1-like models across various math benchmarks.
  arXiv  Detail & Related papers  (2025-02-25T10:48:05Z)
• Learning to Stop Overthinking at Test Time [1.0356759327536202]
  Test time scaling is one of the most active research areas that shows promise after training time scaling has reached its limits.<n>We introduce a test time training method for determining the optimal amount of computation needed for each sample during test time.<n>We also propose Conv-LiGRU, a novel recurrent architecture for efficient and robust visual reasoning.
  arXiv  Detail & Related papers  (2025-02-16T02:17:05Z)
• Rethinking Fine-Tuning when Scaling Test-Time Compute: Limiting Confidence Improves Mathematical Reasoning [32.45574194957491]
  We show that training with cross-entropy loss can be misaligned with pass@N in that pass@N accuracy $it decreases$ with longer training.<n>We suggest a principled, modified training loss that is better aligned to pass@N by limiting model confidence and rescuing pass@N test performance.
  arXiv  Detail & Related papers  (2025-02-11T00:33:31Z)
• Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters [27.656263126925815]
  We study the scaling of inference-time computation in LLMs. We find that in both cases, the effectiveness of different approaches to scaling test-time compute critically varies depending on the difficulty of the prompt.
  arXiv  Detail & Related papers  (2024-08-06T17:35:05Z)
• Does learning the right latent variables necessarily improve in-context learning? [13.168329639763678]
  Large autoregressive models like Transformers can solve tasks through in-context learning (ICL) without learning new weights.<n>In this paper, we investigate the effect of explicitly inferring task latents.<n>We find little discernible difference between the two; biasing towards task-relevant latent variables does not lead to better out-of-distribution performance.
  arXiv  Detail & Related papers  (2024-05-29T15:06:10Z)
• Exploring Learning Complexity for Efficient Downstream Dataset Pruning [8.990878450631596]
  Existing dataset pruning methods require training on the entire dataset.<n>We propose a straightforward, novel, and training-free hardness score named Distorting-based Learning Complexity (DLC)<n>Our method is motivated by the observation that easy samples learned faster can also be learned with fewer parameters.
  arXiv  Detail & Related papers  (2024-02-08T02:29:33Z)
• Efficient Grammatical Error Correction Via Multi-Task Training and Optimized Training Schedule [55.08778142798106]
  We propose auxiliary tasks that exploit the alignment between the original and corrected sentences. We formulate each task as a sequence-to-sequence problem and perform multi-task training. We find that the order of datasets used for training and even individual instances within a dataset may have important effects on the final performance.
  arXiv  Detail & Related papers  (2023-11-20T14:50:12Z)
• How Many Pretraining Tasks Are Needed for In-Context Learning of Linear Regression? [92.90857135952231]
  Transformers pretrained on diverse tasks exhibit remarkable in-context learning (ICL) capabilities. We study ICL in one of its simplest setups: pretraining a linearly parameterized single-layer linear attention model for linear regression.
  arXiv  Detail & Related papers  (2023-10-12T15:01:43Z)
• Robust Learning with Progressive Data Expansion Against Spurious Correlation [65.83104529677234]
  We study the learning process of a two-layer nonlinear convolutional neural network in the presence of spurious features. Our analysis suggests that imbalanced data groups and easily learnable spurious features can lead to the dominance of spurious features during the learning process. We propose a new training algorithm called PDE that efficiently enhances the model's robustness for a better worst-group performance.
  arXiv  Detail & Related papers  (2023-06-08T05:44:06Z)
• Confident Adaptive Language Modeling [95.45272377648773]
  CALM is a framework for dynamically allocating different amounts of compute per input and generation timestep. We demonstrate the efficacy of our framework in reducing compute -- potential speedup of up to $times 3$ -- while provably maintaining high performance.
  arXiv  Detail & Related papers  (2022-07-14T17:00:19Z)
• Sequence Length is a Domain: Length-based Overfitting in Transformer Models [0.0]
  In machine translation, the neural-based systems perform worse on very long sequences when compared to the preceding phrase-based translation approaches. We show that the observed drop in performance is due to the hypothesis length corresponding to the lengths seen by the model during training rather than the length of the input sequence.
  arXiv  Detail & Related papers  (2021-09-15T13:25:19Z)

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.