Complementary Future Token Pruning for World Action Models
cs.RO, cs.CV
Submitted: 2026-10-14
Updated: 2026-10-14
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- Cosmos 3: Omnimodal World Models for Physical AI
- $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
- RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control
- LoRA: Low-Rank Adaptation of Large Language Models
- DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset
- OpenVLA: An Open-Source Vision-Language-Action Model
- Cosmos Policy: Fine-Tuning Video Models for Visuomotor Control and Planning
- Unified Video Action Model
- Bridging the Semantic-Action Gap in Visual Token Pruning for Efficient VLA Inference
- RoboCasa: Large-Scale Simulation of Everyday Tasks for Generalist Robots
- SpecPrune-VLA: Accelerating Vision-Language-Action Models via Action-Aware Self-Speculative Pruning
- Sparse VideoGen: Accelerating Video Diffusion Transformers with Spatial-Temporal Sparsity
- RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies
- GigaWorld-Policy: An Efficient Action-Centered World--Action Model
- World Action Models are Zero-shot Policies
- Fast-WAM: Do World Action Models Need Test-time Future Imagination?
- SparseVLM: Visual Token Sparsification for Efficient Vision-Language Model Inference
- FLARE: Robot Learning with Implicit World Modeling
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