State of Vision-Language-Action (VLA) Research at ICLR 2026
Author: Moritz Reuss Date: October 2025
What is a Vision-Language-Action Model?
A VLA is a model that uses a pretrained backbone, which was trained on large-scale vision-language data and is subsequently trained with generating control commands.
This definition emphasizes internet-scale pretraining as the crucial differentiator, setting VLAs apart from standard multimodal policies that merely combine separate pretrained encoders.
Large Behavior Models (LBMs): trained on large-scale robotic demonstrations without requiring vision-language pretraining. All VLAs trained on massive robotic data are LBMs, but not vice versa.
The Explosive Growth of VLA Research
- ICLR 2024: 1 rejected submission
- ICLR 2025: 9 total submissions
- ICLR 2026: 164 submissions (18x increase year-over-year)
Practitioner’s Guide to Benchmarks
LIBERO: Nearly saturated (95%+ standard). Minimal discrimination between 95–98% results.
CALVIN:
- ABC (cross-setup) >4.0 standard, >4.5 state-of-the-art
- D (fine-tuning) >3.75 standard, >4.0 very good
- ABCD (multi-setup) >4.5 relevant
SIMPLER: Highly variable (40–99% on Bridge alone). Google Robot ~70–80% for current SotA.
ICLR 2026 Research Trends
1. Discrete Diffusion VLAs
Parallel action sequence generation vs. sequential autoregressive. Generates 100-step sequences in few forward passes rather than 100 iterations. Combined with ECoT for simultaneous sub-goal and reasoning generation.
2. Reasoning VLAs and Embodied Chain-of-Thought (ECoT)
Spatially-grounded predictions (bounding boxes, 2D trajectories) + subtask decomposition. Helps align representations between static VLM pretraining and embodied control.
3. New Discrete Tokenizers
Residual Vector Quantization + frequency/time-domain losses, spline-based parameterization, DCT-inspired objectives for physically plausible motion.
4. Efficient VLAs
Smaller architectures, better tokenizers, quantization, distillation, hypernetworks. Task-specific policy generation conditioned on instructions + initial observations.
5. RL for VLAs
Residual RL with small correction policies on top of frozen VLAs. Stage-aware RL decomposing tasks into semantic phases (reach→grasp→transport→place) with stage-level rewards.
6. VLA + Video Prediction
Initializing from video foundation models + future-frame and action prediction. Limitation: diffusion/flow-based video models face slow inference.
7. Evaluation and Benchmarking
Real-to-sim translation for automatic environment construction. World models as evaluation environments.
8. Cross-Action-Space Learning
Soft-prompting tokens for different datasets, unified vision-motion representations via shared codebooks, hierarchical mixture-of-experts for embodiment adaptation.
9. Other Notable Directions
- Memory modules aggregating prior context for robustness
- Policy composition via score-summing (diffusion/flow formulations)
- VLM backbone selection uncorrelated with standard VLM benchmarks
The Hidden Gap Between Frontier and Research VLAs
Despite open-source VLAs matching frontier performance on simulation, significant gaps emerge in zero-shot open-world behavior. Root causes: benchmark saturation, limited open-source data diversity, insufficient understanding of high-quality demonstration data, narrow evaluation scope.
Two Underrepresented Problems
- Data quality: Few submissions address demonstration data curation despite acknowledged OXE data quality concerns
- In-context learning: Language alone insufficient for complex physical tasks; limited VLA work explores this despite LLM/VLM success