Title: VLA-Cache: Efficient Vision-Language-Action Manipulation via Adaptive Token Caching
Venue: NeurIPS 2025
Accepted: yes
Keywords: Vision-Language-Action Model

Efficient Inference

Robotics
Abstract: Vision-Language-Action (VLA) models have demonstrated strong multi-modal reasoning capabilities, enabling direct action generation from visual perception and language instructions in an end-to-end manner. However, their substantial computational cost poses a challenge for real-time robotic control, where rapid decision-making is essential. This paper introduces VLA-Cache, a training-free inference acceleration method that reduces computational overhead by adaptively caching and reusing static visual tokens across frames. Exploiting the temporal continuity in robotic manipulation, VLA-Cache identifies minimally changed tokens between adjacent frames and reuses their cached key-value representations, thereby circumventing redundant computations. Additionally, to maintain action precision, VLA-Cache selectively re-computes task-relevant tokens that are environmentally sensitive, ensuring the fidelity of critical visual information. To further optimize efficiency, we introduce a layer adaptive token reusing strategy that dynamically adjusts the reuse ratio based on attention concentration across decoder layers, prioritizing critical tokens for recomputation. Extensive experiments on two simulation platforms (LIBERO and SIMPLER) and a real-world robotic system demonstrate that VLA-Cache achieves up to 1.7× speedup in CUDA latency and a 15\% increase in control frequency, with negligible loss on task success rate. The code and videos can be found at our project page: https://vla-cache.github.io.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes VLA-Cache, which is a training-free inference acceleration method that can help speedup for robotic control. VLA-Cache selects visually static tokens and task-relevant tokens to construct reusable tokens, and reuse KV-cache for better computational efficiency.  Additional techniques like layer adaptive reusing is also added to improve the performance. The authors evaluate the method on several benchmarks to demonstrate the improvement on the inference speed.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n-\tIn general, the paper is clear and easy to follow\n\n-\tThe idea of adaptively reusing the KV cache of certain regions is interesting and seems to be novel.\n\nWeaknesses:\n\n-\tAlthough the method can improve theoretical FLOPs and CUDA latency, the improvement on actual control frequency seems to be limited.\n\n-\tSee questions below.\n\nQUESTIONS:\n1.\tFor calculating the task-relevant tokens, what visual tokens are you used to calculate the attention matrix? It seems that we need visual tokens at different layers to do this, which is the target for reduction. Do you use the raw image tokens just like OpenVLA or do you use the attention score from the last step? \n\n2.\tI think the main idea is try to reuse the tokens that won’t cast too much influence on the final tokens. It would be interesting to see the performance of a variant which doesn’t calculate visually static and re-use all tokens that belong to Full set – task relevant tokens.\n\n3.\tFor wrist camera used in OpenVLA-OFT (L284), can you explain how to handle it? How many tokens can be reused? It seems like most tokens will not be visually static anymore.\n\n4.\tFor control frequency result in Table 2/3/5, most of the improvements seem to be not prominent (+0.36Hz / 0.2Hz for OpenVLA, +2.2Hz for CogACT). How many tokens are reused? Why is the case?\n\nLIMITATIONS:\nN/A", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces VLA-Cache, a training-free inference acceleration method for Vision-Language-Action (VLA) models. \nThe key contributions are: 1) Static Token Selection. 2) Evicting Task-Relevant Tokens. 3) Layer Adaptive Token Reusing.\nThe method is evaluated on two simulation platforms (LIBERO and SIMPLER) and a real-world robotic setting. \nResults demonstrate that VLA-Cache achieves a speedup of up to 1.7× in CUDA latency and a 15% increase in control frequency, with negligible loss in task success rate. The paper also shows the real-world applicability of VLA-Cache by deploying it on a Kinova Jaco2 robot arm.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n1. The paper is well-organized and features a clear and comprehensible writing style. The author presents the motivation for the proposed method clearly and understandably. Subsequently, each component of the method, together with the experimental settings and results, is elaborated in detail.\n\n2. This paper introduces a novel training-free inference acceleration method, comprising three key components: Static Token Selection, Evicting Task-Relevant Tokens, and Layer-Adaptive Token Reusing.\n\n3. The paper establishes and conducts extensive experiments across three benchmarks: LIBERO, SIMPLER, and real-robot. These experiments serve to demonstrate the effectiveness of the proposed method.\n\n\nWeaknesses:\n\n1. The thresholds and k in Equations (5),(7),(9) need to be set carefully to better retain stable tokens. Otherwise, important visual areas may be misclassified as static regions, or static regions may be wrongly regarded as task-relevant ones for action prediction.\n\n2. Calculating the attention matrix for Evicting Task-Relevant Tokens involves computing K, Q, V. It is unclear whether KV-cache is used here. If it is used, this might lead to inaccuracies in calculating the attention matrix. If not, this would increase the computational load for KV.\n\n3. It is not clear whether this method is applicable to diffusion-based VLA models.\n\n4. While the method reduces FLOPs, Latency, and improves Control Frequency while maintaining task success rates across LIBERO, SIMPLER, and real-robot experiments, the overall performance improvement appears to be limited. For instance, in the real-robot experiment, the success rate for the PickPot task decreased from 95% to 90%, while the Control Frequency increased only slightly from 4.02 to 4.21.\n\nQUESTIONS:\nSee above.\n\nLIMITATIONS:\nYES", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes VLA-Cache, an inference-time acceleration method designed to improve the efficiency of Vision-Language-Action (VLA) models. The core idea leverages the spatial redundancy of static regions across visual frames, reusing representations of static tokens via a key-value (KV) caching mechanism to reduce redundant computation. The authors also introduce an attention-based filtering strategy and a layer-adaptive reuse policy to maintain semantic accuracy of key tokens.\n\nSTRENGTHS AND WEAKNESSES:\n# Strengths\nClear innovation: Proposes a plug-and-play token reuse strategy that requires no retraining, making it lightweight and practical.\nWell-designed method: Clever integration of visual staticness and semantic importance avoids performance degradation due to over-caching.\n\nComprehensive evaluation metrics: Includes analysis on FLOPs, CUDA latency, control frequency, and task success rate.\n\nStrong baselines and comparisons: Systematic comparison with multiple token-level acceleration baselines such as FastV and SparseVLM.\n# Major Weaknesses\n- No real-world robotic experiments: The method is only validated in simulation environments, with no deployment or testing on real robots, raising concerns about its real-world feasibility and robustness.\n- Limited validation scope: The method is only tested on a single visual backbone (e.g., LLaMA2), lacking generalization experiments on other architectures (e.g., Gemma, Qwen-VL, CogVideo).\n- Reliance on attention as a proxy for task relevance is heuristic in nature, and no alternatives or robustness analyses are provided.\n- Contribution is local rather than fundamental: While practical, the method is more of an engineering optimization and offers limited insight into the learning mechanisms of VLA models.\n\nQUESTIONS:\nAdd at least one real-world robotic experiment, even on a low-cost platform, to demonstrate practical applicability and robustness.\n\nExtend validation to a wider range of VLA backbones, such as Gemma, Qwen-VL, or CogAct, to assess generality and compatibility.\n\nAnalyze the limitations of using attention scores as a proxy for semantic relevance. Consider introducing alternative signals such as object masks or affordance maps.\n\nExplore combinations with other token optimization strategies, such as token merging or patch dropout, to enhance performance further.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-03"}

comments: SUMMARY:
This paper considers a lazy strategy for computing K-V values in transformers, specifically for the usage of robotic VLA. The proposed algorithm finds the static tokens and filters those task-relevant. Experimental results show that the VLA-Cache technique accelerate existing VLA algorithms. Overall, I think the motivation of the paper is good.

STRENGTHS AND WEAKNESSES:
1. I didn't find the opensourcing code of this paper. As is known to all, the code implementation matters a lot when computational efficiency is concerned. Will the authors opensource the code? 

2. The proposed algorithm may not work if the robot sensing data is not 2D images, nor if the sensor is mounted on the robot rather than a third-view perspective. If the sensing data is point cloud, then the proposed algorithm is also inapplicable, because the data is unordered. 

3. I think the proposed algorithm should also work on other kind of VLA modules, such as DP and RDT. Please consider add them.

QUESTIONS:
Please see my comments above.

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-03