See What Matters: Differentiable Grid Sample Pruning for Generalizable Vision-Language-Action Model

• URL: http://arxiv.org/abs/2605.11817v2
• Date: Sun, 17 May 2026 20:26:41 GMT
• Title: See What Matters: Differentiable Grid Sample Pruning for Generalizable Vision-Language-Action Model
• Abstract summary: Differentiable Grid Sampler (GridS) is a plug-and-play module that performs task-aware, continuous resampling of visual tokens in Vision-Language-Action (VLA) models.<n>GridS preserves essential spatial information while achieving drastic compression with fewer than 10% original visual tokens.
• Score: 25.03258383353867
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision-Language-Action (VLA) models have shown remarkable promise in robotics manipulation, yet their high computational cost hinders real-time deployment. Existing token pruning methods suffer from a fundamental trade-off: aggressive compression using pruning inevitably discards critical geometric details like contact points, leading to severe performance degradation. This forces a compromise, limiting the achievable compression rate and thus the potential speedup. We argue that breaking this trade-off requires rethinking compression as a geometry-aware, continuous token resampling in the vision encoder. To this end, we propose the Differentiable Grid Sampler (GridS), a plug-and-play module that performs task-aware, continuous resampling of visual tokens in VLA. By adaptively predicting a minimal set of salient coordinates and extracting features via differentiable interpolation, GridS preserves essential spatial information while achieving drastic compression (with fewer than 10% original visual tokens). Experiments on both LIBERO benchmark and a real robotic platform demonstrate that validating the lowest feasible visual token count reported to date, GridS achieves a 76% reduction in FLOPs with no degradation in the success rate. The code is available at https://github.com/Fediory/Grid-Sampler.

Related papers

• Just Noticeable Difference Modeling for Token Compression in Vision-Language-Action Models [62.522141464687195]
  We introduce Action-JND, which extends the principle of just noticeable difference (JND) to embodied perception.<n>A token change is considered tolerable only when the induced action deviation remains within a tolerated margin.<n>The resulting action-tolerance score serves as a plug-and-play criterion for VLA compression paradigms.
  arXiv  Detail & Related papers  (2026-08-21T15:59:37Z)
• CRAFT: Compression via Recursive Adaptive Fusion of Video Tokens for Vision-Language Models [9.660619113725843]
  We propose CRAFT: Compression via Resive Adaptive Fusion Video Tokens.<n>CRAFT consistently outperforms prior state-of-the-art token-compression methods.<n>At about $8times$ compression, it retains roughly $97% of the backbone's average accuracy.
  arXiv  Detail & Related papers  (2026-08-03T03:27:46Z)
• VisCo: Leveraging Large Language Models as Intrinsic Encoders for Visual Token Compression [52.23010472235226]
  Vision-language models (VLMs) process large numbers of visual tokens, resulting in substantial inference and overhead latency.<n>We propose VisCo, a training-efficient self-compression framework that reuses the pretrained VLM itself as an intrinsic compressor.
  arXiv  Detail & Related papers  (2026-07-14T13:21:36Z)
• DepthCache: Depth-Guided Training-Free Visual Token Merging for Vision-Language-Action Model Inference [5.305950698447464]
  Vision-Language-Action (VLA) models enable generalist robotic manipulation but suffer from high inference latency.<n>We present DepthCache, a training-free framework that leverages depth as a structural prior for visual token compression.<n>On the LIBERO benchmark, DepthCache achieves up to 1.28x inference speedup with less than 1% average success rate degradation.
  arXiv  Detail & Related papers  (2026-03-11T06:40:44Z)
• PIO-FVLM: Rethinking Training-Free Visual Token Reduction for VLM Acceleration from an Inference-Objective Perspective [59.24570811503256]
  We propose PIO-FVLM to reduce redundant visual tokens in vision-models (VLMs) to accelerate inference.<n>The proposed PIO-FVLM is training-free and compatible with FlashAttention, friendly to practical application and deployment.<n>On LLaVA-Next-7B, PIO-FVLM retains just 11.1% of visual tokens but maintains 97.2% of the original performance.
  arXiv  Detail & Related papers  (2026-02-04T15:33:10Z)
• Efficient Video Sampling: Pruning Temporally Redundant Tokens for Faster VLM Inference [5.146388234814547]
  Long videos often exceed the token budget of modern language models, leading to severe context limitations and latency issues.<n>We introduce Efficient Video Sampling (EVS), a simple, plug-and-play method for reducing token redundancy in videos by identifying and pruning temporally static patches.<n>EVS substantially reduces token count while maintaining semantic fidelity, enabling faster inference and longer input sequences.
  arXiv  Detail & Related papers  (2025-10-16T12:34:38Z)
• OmniSAT: Compact Action Token, Faster Auto Regression [70.70037017501357]
  We introduce an Omni Swift Action Tokenizer, which learns a compact, transferable action representation.<n>The resulting discrete tokenization shortens the training sequence by 6.8$times$, and lowers the target entropy.
  arXiv  Detail & Related papers  (2025-10-08T03:55:24Z)
• A Glimpse to Compress: Dynamic Visual Token Pruning for Large Vision-Language Models [94.49953824684853]
  We introduce a dynamic pruning framework, GlimpsePrune, inspired by human cognition.<n>It takes a data-driven ''glimpse'' and prunes irrelevant visual tokens in a single forward pass before answer generation.<n>An enhanced GlimpsePrune+ achieves 110% of the baseline performance while maintaining a similarly high pruning rate.
  arXiv  Detail & Related papers  (2025-08-03T02:15:43Z)
• Token Compensator: Altering Inference Cost of Vision Transformer without Re-Tuning [63.43972993473501]
  Token compression expedites the training and inference of Vision Transformers (ViTs) However, when applied to downstream tasks, compression degrees are mismatched between training and inference stages. We propose a model arithmetic framework to decouple the compression degrees between the two stages.
  arXiv  Detail & Related papers  (2024-08-13T10:36:43Z)
• VoCo-LLaMA: Towards Vision Compression with Large Language Models [31.398537194299752]
  Vision-Language Models (VLMs) have achieved remarkable success in various multi-modal tasks, but they are often bottlenecked by the limited context window.<n>We propose VoCo-LLaMA, the first approach to compress vision tokens using LLMs.<n>Our method achieves minimal performance loss with a compression ratio of 576$times$, resulting in up to 94.8$%$ fewer FLOPs and 69.6$%$ acceleration in inference time.
  arXiv  Detail & Related papers  (2024-06-18T05:05:12Z)
• DiffRate : Differentiable Compression Rate for Efficient Vision Transformers [98.33906104846386]
  Token compression aims to speed up large-scale vision transformers (e.g. ViTs) by pruning (dropping) or merging tokens. DiffRate is a novel token compression method that has several appealing properties prior arts do not have.
  arXiv  Detail & Related papers  (2023-05-29T10:15:19Z)

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.