Efficient Test-Time Scaling for Small Vision-Language Models

• URL: http://arxiv.org/abs/2510.03574v1
• Date: Fri, 03 Oct 2025 23:49:06 GMT
• Title: Efficient Test-Time Scaling for Small Vision-Language Models
• Authors: Mehmet Onurcan Kaya, Desmond Elliott, Dim P. Papadopoulos,
• Abstract summary: Small Vision-Language Models (VLMs) provide a computationally efficient alternative to larger models.<n>Existing methods are typically computationally demanding, contradicting the resource-efficient design goals of small models.<n>We propose two novel and efficient test-time scaling strategies that leverage the model-internal features rather than external supervision.
• Score: 14.654047034885288
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Small Vision-Language Models (VLMs) provide a computationally efficient alternative to larger models, at the cost of weaker generalization abilities and downstream task performance. These shortcomings could be addressed by test-time scaling techniques, but existing methods are typically computationally demanding, contradicting the resource-efficient design goals of small models. To address these limitations, we propose two novel and efficient test-time scaling strategies that leverage the model-internal features rather than external supervision: (i) Test-Time Augmentation (TTAug), which generates multiple augmented inputs and aggregates outputs at the token level without parameter updates, and (ii) Test-Time Adaptation (TTAdapt), which adapts model parameters during inference using consensus-based pseudolabels from TTAug. Through extensive experiments across nine benchmarks, we demonstrate consistent performance improvements while maintaining computational efficiency suitable for resource-constrained environments. The generality of our approach is demonstrated both within models at different scales and across different VLMs without additional tuning.

Related papers

• MatryoshkaThinking: Recursive Test-Time Scaling Enables Efficient Reasoning [33.47806621047652]
  MatryoshkaThinking is a novel method that significantly reduces computational cost while maintaining state-of-the-art performance.<n>MatryoshkaThinking attains a score of 99.79 on AIME2025 using only 4% of the computation required by DeepConf.
  arXiv  Detail & Related papers  (2025-10-11T17:18:12Z)
• ARISE: An Adaptive Resolution-Aware Metric for Test-Time Scaling Evaluation in Large Reasoning Models [102.4511331368587]
  ARISE (Adaptive Resolution-aware Scaling Evaluation) is a novel metric designed to assess the test-time scaling effectiveness of large reasoning models.<n>We conduct comprehensive experiments evaluating state-of-the-art reasoning models across diverse domains.
  arXiv  Detail & Related papers  (2025-10-07T15:10:51Z)
• Test-Time Model Adaptation for Quantized Neural Networks [37.84294929199108]
  Quantized models often suffer from severe performance degradation in dynamic environments with potential domain shifts.<n>Test-time adaptation (TTA) has emerged as an effective solution by enabling models to learn adaptively from test data.<n>We propose a continual zeroth-order adaptation (ZOA) framework that enables efficient model adaptation using only two forward passes.
  arXiv  Detail & Related papers  (2025-08-04T08:24:19Z)
• TAPS : Frustratingly Simple Test Time Active Learning for VLMs [0.0]
  Test-Time Optimization enables models to adapt to new data during inference by updating parameters on-the-fly.<n>We propose a novel Test-Time Active Learning framework that adaptively queries uncertain samples and updates prompts dynamically.<n>Our framework provides a practical and effective solution for real-world deployment in safety-critical applications such as autonomous systems and medical diagnostics.
  arXiv  Detail & Related papers  (2025-07-26T18:04:49Z)
• Accelerated Test-Time Scaling with Model-Free Speculative Sampling [58.69141724095398]
  We introduce STAND (STochastic Adaptive N-gram Drafting), a novel model-free speculative decoding approach.<n>We show that STAND reduces inference latency by 60-65% compared to standard autoregressive decoding.<n>As a model-free approach, STAND can be applied to any existing language model without additional training.
  arXiv  Detail & Related papers  (2025-06-05T07:31:18Z)
• Latent Thought Models with Variational Bayes Inference-Time Computation [52.63299874322121]
  Latent Thought Models (LTMs) incorporate explicit latent thought vectors that follow an explicit prior model in latent space.<n>LTMs demonstrate superior sample and parameter efficiency compared to autoregressive models and discrete diffusion models.
  arXiv  Detail & Related papers  (2025-02-03T17:50:34Z)
• SETS: Leveraging Self-Verification and Self-Correction for Improved Test-Time Scaling [39.57154199908565]
  Self-Enhanced Test-Time Scaling (SETS) is a simple yet effective approach that overcomes limitations by strategically combining parallel and sequential techniques.<n>SETS exploits the inherent self-verification and self- computation capabilities of Large Language Models, unifying sampling, verification, and correction within a single framework.<n>Our results show SETS achieves significant performance improvements and more advantageous test-time scaling behavior than the alternatives.
  arXiv  Detail & Related papers  (2025-01-31T17:03:16Z)
• Words Matter: Leveraging Individual Text Embeddings for Code Generation in CLIP Test-Time Adaptation [21.20806568508201]
  We show how to leverage class text information to mitigate distribution drifts encountered by vision-language models (VLMs) during test-time inference.<n>We propose to generate pseudo-labels for the test-time samples by exploiting generic class text embeddings as fixed centroids of a label assignment problem.<n>Experiments on multiple popular test-time adaptation benchmarks presenting diverse complexity empirically show the superiority of CLIP-OT.
  arXiv  Detail & Related papers  (2024-11-26T00:15:37Z)
• E^2VPT: An Effective and Efficient Approach for Visual Prompt Tuning [55.50908600818483]
  Fine-tuning large-scale pretrained vision models for new tasks has become increasingly parameter-intensive. We propose an Effective and Efficient Visual Prompt Tuning (E2VPT) approach for large-scale transformer-based model adaptation. Our approach outperforms several state-of-the-art baselines on two benchmarks.
  arXiv  Detail & Related papers  (2023-07-25T19:03:21Z)
• Energy-efficient Task Adaptation for NLP Edge Inference Leveraging Heterogeneous Memory Architectures [68.91874045918112]
  adapter-ALBERT is an efficient model optimization for maximal data reuse across different tasks. We demonstrate the advantage of mapping the model to a heterogeneous on-chip memory architecture by performing simulations on a validated NLP edge accelerator.
  arXiv  Detail & Related papers  (2023-03-25T14:40:59Z)
• HyperImpute: Generalized Iterative Imputation with Automatic Model Selection [77.86861638371926]
  We propose a generalized iterative imputation framework for adaptively and automatically configuring column-wise models. We provide a concrete implementation with out-of-the-box learners, simulators, and interfaces.
  arXiv  Detail & Related papers  (2022-06-15T19:10:35Z)

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.