The paper investigates pruning for Vision‑Language‑Action (VLA) models, finding that it causes severe performance loss and safety violations. Spectral analysis reveals structural weight properties that explain this fragility. The authors propose GLUESTICK, a post‑pruning recovery method that interpolates between dense and pruned weights, restoring up to 100% of success rate and safety while saving memory and speeding inference.
arXiv:2506. 17639v2 Announce Type: replace-cross Abstract: Vision-Language-Action models (VLA) have demonstrated remarkable capabilities and strong potential in complex robotic manipulation.
arXiv:2608. 06901v1 Announce Type: cross Abstract: Vision-language models (VLMs) have achieved remarkable generalization across diverse multimodal tasks through large-scale pre-training, yet their rapidly increasing computational and memory requirements pose significant challenges for deployment in constrained environments.
arXiv:2606. 20246v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models pre-trained on massive video-robot datasets have revolutionized robotic manipulation, yet their multi-billion parameter architectures impose prohibitive computational burdens during downstream fine-tuning and real-time inference.
LearnPrunerV2 is a fully learnable token‑pruning framework for Vision‑Language Models that replaces heuristic pruning with lightweight modules in every backbone layer. These modules estimate token importance by combining visual saliency and query relevance via a shadow token, revealing a consistent staircase redundancy pattern across layers. The method introduces Budget‑Deterministic Pruning to set fixed per‑layer budgets, achieving state‑of‑the‑art efficiency while preserving 99.8% of performance even after pruning 77.8% of visual tokens on LLaVA‑v1.5.
arXiv:2610.41968v1 Announce Type: new Abstract: Weight pruning can reduce the storage and inference cost of large language models (LLMs), but preserving quality at high sparsity levels remains challe...
arXiv:2610.08133v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models achieve strong robotic manipulation performance but incur high computational costs from processing long token seq...
LearnPrunerV2 introduces a fully learnable token pruning framework for Vision‑Language Models, embedding lightweight modules in every backbone layer to estimate token importance by combining visual saliency and query relevance via a shadow token. It identifies a consistent staircase redundancy pattern across layers and employs Budget‑Deterministic Pruning to set fixed per‑layer budgets, yielding a hardware‑friendly pruning schedule. Experiments show that LearnPrunerV2 outperforms prior methods, preserving 99.8% of performance while removing 77.8% of visual tokens on LLaVA‑v1.5, thus achieving a superior accuracy‑efficiency trade‑off.
arXiv:2605. 18331v2 Announce Type: replace Abstract: Large Language Models (LLMs) have experienced significant growth and development in recent years.
The paper "ActionCodec: What Makes for Good Action Tokenizers" addresses the lack of design guidance for action tokenization in Vision‑Language‑Action (VLA) models. By deriving information‑theoretic principles—maximizing temporal token overlap, reducing vocabulary redundancy, boosting multimodal mutual information, and ensuring token independence—the authors propose ActionCodec, a tokenizer that improves training efficiency and VLA performance. Experiments show that a SmolVLM2‑2.2B fine‑tuned with ActionCodec reaches a 95.5 % success rate on LIBERO, rising to 97.4 % with architectural tweaks, setting a new state‑of‑the‑art for VLA models without robotics pre‑training.
SACQ is a weight‑only vector‑codebook quantization framework designed for Vision‑Language‑Action models, addressing the challenges of ultra‑low‑bit precision. It incorporates structure‑aware codebook fitting, action‑aware mixed‑bit allocation, and a two‑stage post‑quantization refinement to maintain policy performance. Experiments on GR00T N1.7 and SimplER show that SACQ achieves high success rates (up to 93.85%) while reducing memory usage by 4.83× and improving inference speed.
StarVLA-α is a streamlined Vision‑Language‑Action (VLA) model that reduces architectural and pipeline complexity to facilitate systematic study of VLA design choices. By employing a strong VLM backbone and minimal design, it achieves competitive performance across multiple benchmarks (LIBERO, SimplerEnv, RoboTwin, RoboCasa) and outperforms the baseline π₀.₅ by 20% on the RoboChallenge benchmark. The authors plan to release the code to support future VLA research.
arXiv:2606. 27755v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models enable instruction-driven robotic manipulation, but they inherit oversized language backbones from pretrained VLMs whose capacity far exceeds what is needed for short robotic instructions.