Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
VLA-Cache: Efficient Vision-Language-Action Manipulation via Adaptive Token Caching
Authors: Siyu Xu, Yunke Wang, Chenghao Xia, Dihao Zhu, Tao Huang, Chang Xu
NeurIPS 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 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. |
| Researcher Affiliation | Academia | 1School of Computer Science, University of Sydney, Australia 2John Hopcropt Center for Computer Science, Shanghai Jiao Tong University, China |
| Pseudocode | Yes | Algorithm 1 Dynamic Token Selection |
| Open Source Code | Yes | The code and videos can be found at our project page: https://vla-cache.github.io. |
| Open Datasets | Yes | We evaluate VLA-Cache on robotic manipulation tasks across two simulated environments (LIBERO [17] and SIMPLER [18]) |
| Dataset Splits | Yes | We collected 218 valid demonstrations for the training dataset, with slight random adjustments to the initial positions of the pot and the robotic arm in each episode. During evaluation, each trial is recorded as a success (1) or failure (0); there is no partial credit. ... We run 20 trials for Pick Pot and Put Sausage, and 30 trials for Place Cube and Wipe Table, for a total of 100 trials per method. |
| Hardware Specification | Yes | All experiments are conducted on an NVIDIA RTX 4090 GPU. |
| Software Dependencies | No | Simulated evaluations for Cog ACT and SIMPLER are conducted on a single NVIDIA RTX 4090 GPU in BF16 precision. During inference, we use DDIM sampling with 10 steps and a classifier-free guidance (CFG) coefficient of 1.5. Similarly, for Open VLA and LIBERO, inference is performed on a single NVIDIA RTX 4090 GPU in BF16 precision. ... Training on the real robot used Lo RA-based fine-tuning for 50,000 steps... We employed Py Bullet as an inverse kinematics (IK) controller. The paper mentions software components and techniques but lacks specific version numbers for key libraries or frameworks needed for replication. |
| Experiment Setup | Yes | Unless specified otherwise, we use a static token similarity threshold Ο = 0.996, top-k = 100 for retained static tokens, and a task-relevance threshold Οtask = 0.5. These parameters are applied consistently across all simulated and real-world settings, including SIMPLER with Cog Act. For real-world Jaco2 experiments, we slightly reduce the similarity threshold to Ο = 0.85 to accommodate environmental noise. Training on the real robot used Lo RA-based fine-tuning for 50,000 steps |