Compute Aligned Training: Optimizing for Test Time Inference

• URL: http://arxiv.org/abs/2604.24957v1
• Date: Mon, 27 Apr 2026 19:52:38 GMT
• Title: Compute Aligned Training: Optimizing for Test Time Inference
• Abstract summary: Scaling test-time compute has emerged as a powerful mechanism for enhancing Large Language Model (LLM) performance.<n>Standard post-training paradigms, Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) optimize the likelihood of individual samples under a base policy.<n>We propose Compute Aligned Training, which aligns training objectives with test-time strategies.
• Score: 23.99156538891498
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Scaling test-time compute has emerged as a powerful mechanism for enhancing Large Language Model (LLM) performance. However, standard post-training paradigms, Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL), optimize the likelihood of individual samples under a base policy, creating a misalignment with test time procedures that rely on aggregated or filtered outputs. In this work, we propose Compute Aligned Training, which aligns training objectives with test-time strategies. By conceptualizing inference strategies as operators on the base policy, we derive new loss functions that maximize performance when said strategies are applied. We instantiate such loss functions for SFT and RL across common test time strategies. Finally, we provide empirical evidence that this training method substantially improves test time scaling over standard training.

Related papers

• Test-Time Gradient Guidance of Flow Policies in Reinforcement Learning [50.738952715864116]
  Expressive continuous control policies form the backbone of advances in scaling imitation learning for simulated and real robot control.<n>We propose QGF (Q-Guided Flow), an RL algorithm that performs policy optimization entirely at test time.<n> Empirically, QGF outperforms prior test-time RL methods on single-task and goal-conditioned offline RL benchmarks.
  arXiv  Detail & Related papers  (2026-06-09T16:45:57Z)
• ThinkBooster: A Unified Framework for Seamless Test-Time Scaling of LLM Reasoning [110.46431027868954]
  ThinkBooster is a framework for seamless test-time compute scaling of large language model (LLM) reasoning.<n>It consists of a modular Python library implementing state-of-the-art TTC scaling strategy and scorer families.<n>A deployable OpenAI-compatible proxy service enables drop-in integration of adaptive reasoning into real-world applications.
  arXiv  Detail & Related papers  (2026-06-05T05:28:46Z)
• Test-time Offline Reinforcement Learning on Goal-related Experience [50.94457794664909]
  Research in foundation models has shown that performance can be substantially improved through test-time training.<n>We propose a novel self-supervised data selection criterion, which selects transitions from an offline dataset according to their relevance to the current state.<n>Our goal-conditioned test-time training (GC-TTT) algorithm applies this routine in a receding-horizon fashion during evaluation, adapting the policy to the current trajectory as it is being rolled out.
  arXiv  Detail & Related papers  (2025-07-24T21:11:39Z)
• AdaLRS: Loss-Guided Adaptive Learning Rate Search for Efficient Foundation Model Pretraining [12.630306478872043]
  We propose textbfAdaLRS, a plug-in-and-play adaptive learning rate search algorithm that conducts online optimal learning rate search.<n>Experiments show that AdaLRS adjusts suboptimal learning rates to the neighborhood of optimum with marked efficiency and effectiveness.
  arXiv  Detail & Related papers  (2025-06-16T09:14:01Z)
• A Snapshot of Influence: A Local Data Attribution Framework for Online Reinforcement Learning [45.19254609437857]
  Online reinforcement learning (RL) excels in complex, safety-critical domains but suffers from sample inefficiency, training instability, and limited interpretability.<n>Data attribution provides a principled way to trace model behavior back to training samples.<n>We propose an algorithm, iterative influence-based filtering (IIF), for online RL training that iteratively performs experience filtering to refine policy updates.
  arXiv  Detail & Related papers  (2025-05-25T19:25:57Z)
• Fast Adaptation with Behavioral Foundation Models [82.34700481726951]
  Unsupervised zero-shot reinforcement learning has emerged as a powerful paradigm for pretraining behavioral foundation models.<n>Despite promising results, zero-shot policies are often suboptimal due to errors induced by the unsupervised training process.<n>We propose fast adaptation strategies that search in the low-dimensional task-embedding space of the pre-trained BFM to rapidly improve the performance of its zero-shot policies.
  arXiv  Detail & Related papers  (2025-04-10T16:14:17Z)
• Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining [55.262510814326035]
  Existing reweighting strategies primarily focus on group-level data importance.<n>We introduce novel algorithms for dynamic, instance-level data reweighting.<n>Our framework allows us to devise reweighting strategies deprioritizing redundant or uninformative data.
  arXiv  Detail & Related papers  (2025-02-10T17:57:15Z)
• Bridging SFT and DPO for Diffusion Model Alignment with Self-Sampling Preference Optimization [67.8738082040299]
  Self-Sampling Preference Optimization (SSPO) is a new alignment method for post-training reinforcement learning.<n>SSPO eliminates the need for paired data and reward models while retaining the training stability of SFT.<n>SSPO surpasses all previous approaches on the text-to-image benchmarks and demonstrates outstanding performance on the text-to-video benchmarks.
  arXiv  Detail & Related papers  (2024-10-07T17:56:53Z)
• Take the Bull by the Horns: Hard Sample-Reweighted Continual Training Improves LLM Generalization [165.98557106089777]
  A key challenge is to enhance the capabilities of large language models (LLMs) amid a looming shortage of high-quality training data. Our study starts from an empirical strategy for the light continual training of LLMs using their original pre-training data sets. We then formalize this strategy into a principled framework of Instance-Reweighted Distributionally Robust Optimization.
  arXiv  Detail & Related papers  (2024-02-22T04:10:57Z)

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.