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EfficientVLA: Training-Free Acceleration and Compression for Vision-Language-Action Models
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EfficientVLA: Training-Free Acceleration and Compression for Vision-Language-Action Models
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Vision-Language-Action (VLA) models, particularly diffusion-based architectures, demonstrate transformative potential for embodied intelligence but are severely hampered by high computational and memory demands stemming from extensive inherent and inference-time redundancies. While existing acceleration efforts often target isolated inefficiencies, such piecemeal solutions typically fail to holistically address the varied computational and memory bottlenecks across the entire VLA pipeline, thereby limiting practical deployability. We introduce EfficientVLA, a structured and training-free inference acceleration framework that systematically eliminates these barriers by cohesively exploiting multifaceted redundancies. EfficientVLA synergistically integrates three targeted strategies: (1) pruning of functionally inconsequential layers from the language module, guided by an analysis of inter-layer redundancies; (2) optimizing the visual processing pathway through a task-aware strategy that selects a compact, diverse set of visual tokens, balancing task-criticality with informational coverage; and (3) alleviating temporal computational redundancy within the iterative diffusion-based action head by strategically caching and reusing key intermediate features. We apply our method to a standard VLA model CogACT, yielding a 1.93X inference speedup and reduces FLOPs to 28.9%, with only a 0.6% success rate drop in the SIMPLER benchmark.
Forward citations
Cited by 29 Pith papers
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Revisiting Parameter Redundancy in Vision-Language-Action Models: Insights from VLM-to-VLA Adaptation
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Mix-QVLA: Task-Evidence-Aware Mixed-Precision Quantization of Vision-Language-Action Models
Mix-QVLA is a task-evidence-aware mixed-precision PTQ framework for VLA models that preserves task-relevant evidence via evidence-mass and attribution-distribution metrics to guide bit allocation under memory and BitO...
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AURA: Action-Gated Memory for Robot Policies at Constant VRAM
AURA-Mem uses an action-gated recurrent memory trained on closed-loop action error to deliver constant 4,224-byte state and 5-9x fewer writes than baselines while matching base policy success on LIBERO-Long.
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Realtime-VLA FLASH: Speculative Inference Framework for Diffusion-based VLAs
A new speculative inference system speeds up diffusion VLAs to 19.1 ms average latency (3.04x faster) on LIBERO by replacing most full 58 ms inferences with 7.8 ms draft rounds while preserving task performance.
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Test-time Sparsity for Extreme Fast Action Diffusion
Test-time sparsity with a parallel pipeline and omnidirectional feature reuse accelerates action diffusion by 5x to 47.5 Hz while cutting FLOPs 92% with no performance loss.
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Characterizing Vision-Language-Action Models across XPUs: Constraints and Acceleration for On-Robot Deployment
VLA models exhibit a compute-bound VLM phase followed by a memory-bound action phase on edge hardware; DP-Cache and V-AEFusion reduce redundancy and enable pipeline parallelism for up to 6x speedup on NPUs with margin...
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HeiSD: Hybrid Speculative Decoding for Embodied Vision-Language-Action Models with Kinematic Awareness
HeiSD delivers up to 2.45x faster inference for embodied VLA models by hybridizing speculative decoding with kinematic boundary detection and error-mitigation tricks while preserving task success rates.
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KERV: Kinematic-Rectified Speculative Decoding for Embodied VLA Models
KERV integrates kinematic Kalman Filter predictions with speculative decoding in VLA models to achieve 27-37% faster inference while maintaining nearly the same task success rates.
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QuantVLA: Scale-Calibrated Post-Training Quantization for Vision-Language-Action Models
QuantVLA is the first post-training quantization framework for VLA models that quantizes the diffusion transformer action head and reports higher task success rates than full-precision baselines with roughly 70% memor...
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PolicyTrim: Boosting Intrinsic Policy Efficiency of Vision-Language-Action Models
PolicyTrim is an RL post-training framework that boosts VLA policy efficiency by 3x chunk utilization and 51.4% fewer steps, yielding up to 5.83x speedup.
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TempoVLA: Learning Speed-Controllable Vision-Language-Action Policies
TempoVLA learns a single VLA policy with controllable execution speed via variable-speed trajectory augmentation and explicit speed conditioning.
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SAFE-Pruner: Semantic Attention-Guided Future-Aware Token Pruning for Efficient Vision-Language-Action Manipulation
SAFE-Pruner forecasts deep-layer visual-token saliency from historical attention maps and refreshes at subtask boundaries, enabling up to 1.89x faster VLA inference with minimal success-rate drop.
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A1: A Fully Transparent Open-Source, Adaptive and Efficient Truncated Vision-Language-Action Model
A1 is a transparent VLA framework achieving state-of-the-art robot manipulation success with up to 72% lower latency via adaptive layer truncation and inter-layer flow matching.
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SnapFlow: One-Step Action Generation for Flow-Matching VLAs via Progressive Self-Distillation
SnapFlow compresses multi-step denoising in flow-matching VLAs into one step via progressive self-distillation using two-step Euler shortcuts from marginal velocities, matching 10-step teacher success rates with 9.6x ...
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Open-Loop Planning, Closed-Loop Verification: Speculative Verification for VLA
SV-VLA uses infrequent heavy VLA planning of action chunks plus a lightweight closed-loop verifier to achieve both efficiency and robustness in dynamic robot control.
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OxyGen: Unified KV Cache Management for VLA Inference under Multi-Task Parallelism
OxyGen unifies KV cache management in MoT VLAs to enable cross-task KV sharing and cross-frame continuous batching, delivering up to 3.7x speedup with 200+ tokens/s language and 70 Hz action on on-device platforms.
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TIDAL: Temporally Interleaved Diffusion and Action Loop for High-Frequency VLA Control
TIDAL raises VLA control feedback from ~2.4 Hz to ~9 Hz by caching semantic intent and interleaving one-step flow generation with execution, doubling dynamic interception success in simulation.
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VLSA: Vision-Language-Action Models with Plug-and-Play Safety Constraint Layer
AEGIS wraps VLA robot policies in a CBF-based safety layer that uses VLM obstacle identification, raising collision avoidance from 18.69% to 77.85% and task success by 17.25 points on the new SafeLIBERO benchmark.
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ActDistill: General Action-Guided Self-Derived Distillation for Efficient Vision-Language-Action Models
ActDistill transfers action knowledge from heavy VLA teacher models to lightweight students via graph-encapsulated hierarchies and action-guided dynamic routing, delivering over 50% computation reduction and 1.67x spe...
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Look, Zoom, Understand: The Robotic Eyeball for Embodied Perception
EyeVLA transfers open-world VLM understanding to a PTZ camera control policy via hierarchical action tokens and GRPO reinforcement learning, reaching 96% task completion on 50 real scenes with only 500 training samples.
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SQAP-VLA: A Synergistic Quantization-Aware Pruning Framework for High-Performance Vision-Language-Action Models
A training-free framework jointly quantizes a VLA model to 4 bits and prunes visual tokens, recovering or exceeding full-precision success rates at 1.93x speedup.
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TempoVLA: Learning Speed-Controllable Vision-Language-Action Policies
A single vision-language-action policy can execute robot manipulation at commanded speeds from 0.5x to 2x by training on merged/split demonstration actions conditioned on a speed scalar.
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SAFE-Pruner: Semantic Attention-Guided Future-Aware Token Pruning for Efficient Vision-Language-Action Manipulation
SAFE-Pruner forecasts deep-layer token saliency in VLA models via semantic attention consistency and adaptive subtask detection to achieve up to 1.89x speedup with under 1.7% success rate loss.
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ElegantVLA: Learning When to Think for Efficient Vision-Language-Action Models
ElegantVLA accelerates VLA models up to 3.77x by dynamically scheduling compute across vision, language, and action components without retraining the base model.
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Fast-dVLA: Accelerating Discrete Diffusion VLA to Real-Time Performance
Parameter differences from two training runs on a small task set are treated as auxiliary capability vectors that are merged into a pretrained VLA model, yielding auxiliary-task gains at the cost of ordinary supervise...
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Sparse ActionGen: Accelerating Diffusion Policy with Real-time Pruning
Sparse ActionGen accelerates diffusion policies up to 4x for robot control via rollout-adaptive pruning and zig-zag activation reuse without performance loss.
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Large VLM-based Vision-Language-Action Models for Robotic Manipulation: A Survey
This survey organizes large VLM-based VLA models for robotic manipulation into monolithic and hierarchical paradigms, reviews their integrations and datasets, and outlines future directions.
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Efficient Vision-Language-Action Models for Embodied Manipulation: A Systematic Survey
A survey that groups VLA efficiency techniques into four categories: model architecture, perception features, action generation, and training/inference strategies.
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