Show HN: VLDA-learn compact Deltas as actionable transition interfaces for robot

Researchers from Harbin Institute of Technology Shenzhen, Baidu and the Shenzhen Loop Area Institute released LoongForge-VLDA, a vision-language-delta-action model that distills only the state changes between frames into a compact Delta fed to the action head, reporting a 98.5% average on LIBERO versus 96.9% for π0.5 and 84.5% zero-shot on LIBERO-Plus. VLDA also scored 62.9% on 10 hard RoboTwin 2.0 tasks and 73.0% on the R1 Lite real robot and 72.0% on AgileX, with real-world inference running on a single GPU. The method uses a V-JEPA 2 EMA teacher, a local transition filter selecting the top-64 regions via Sinkhorn optimal transport, and a global transition anchor, trained on 8 GPUs with the LoongForge stack and requiring no future frames at inference.

LoongForge-VLDA Vision-Language-Delta-Action: learning compact Deltas as actionable transition interfaces for robotic manipulation. <sup 1</sup Harbin Institute of Technology, Shenzhen · <sup 2</sup Baidu, Inc. · <sup 3</sup Shenzhen Loop Area Institute Equal contribution † Corresponding author 98.5% LIBERO average 84.5% LIBERO-Plus, zero-shot 62.9% RoboTwin 2.0, 10 hard tasks 73.0% Real robot · R1 Lite 72.0% Real robot · AgileX 1 GPU Real-world inference on a single GPU 01 The idea Don't predict the whole future. Capture the change. What does an action actually change? Manipulation is a chain of state transitions, and most of every frame stays the same. VLA models must infer that change implicitly; world-action models predict whole future frames and spend capacity on pixels that never move. VLDA distills only what changes into a compact Delta and hands it to the action head as an explicit interface: understand the scene, express the transition, then execute. Understand Vision and language are encoded into a task-aware representation. Transition · Δ A compact Delta captures the local and global change the task requires. Execute The action head is refined by the Delta, with no future frames needed at inference. 02 The method From a Delta to an action, in three moves. A V-JEPA 2 EMA teacher provides the target transition. Two filters distill it into a Delta; one refinement module feeds it back into the action head. Local Transition Filter Selects the top-64 regions with the largest transition magnitude and matches them to the predicted Delta by Sinkhorn optimal transport, so the Delta is not diluted by static background. Global Transition Anchor Anchors the local patches to a global summary of the transition, keeping the Delta consistent with the task as a whole. Delta-Driven Action Refinement DAMG grounds the Delta on the action manifold with an InfoNCE loss. DSAF seeds the action flow with a Delta-based noise bias and a prefix token. Learn the transition Pre-train the Delta from the teacher, without actions. L1 = L LTF + L GTA Learn to act Train the action head with the Delta as an interface, keeping the stage-1 loss as a regularizer. L2 = L act + λ·L1 + λ DDAR·L DDAR Built on LoongForge. VLDA is trained with the LoongForge embodied stack: DDP, ZeRO-1, FSDP and HSDP, component-wise freezing and checkpointing. Training used 8 GPUs. Inference needs no future observations, and the EMA teacher can be dropped after training. 03 The results Stronger where it counts, consistently. Three simulation benchmarks and a set of ablations. Our row is highlighted. 98.5% LIBERO average Top average, with the biggest lift on long-horizon tasks. VLDA beats π0.5 by 1.6 points on average and edges out every other baseline, reaching 95.2% on the Long suite. Success rate % on LIBERO | Method | Spatial | Object | Goal | Long | Avg | |---|---|---|---|---|---| | VLDA | 99.8 | 99.6 | 99.4 | 95.2 | 98.5 | | π0.5 | 98.8 | 98.2 | 98.0 | 92.4 | 96.9 | | VLA-Adapter | 97.8 | 99.2 | 97.2 | 95.0 | 97.3 | | OpenVLA-OFT | 97.6 | 98.4 | 97.9 | 94.5 | 97.1 | | CronusVLA | 97.3 | 99.6 | 96.9 | 94.0 | 97.0 | | InternVLA-M1 | 98.0 | 99.0 | 93.8 | 92.6 | 95.9 | | UniVLA | 95.4 | 98.8 | 93.6 | 94.0 | 95.4 | | π0 | 96.8 | 98.8 | 95.8 | 85.2 | 94.2 | | GR00T-N1 | 94.4 | 97.6 | 93.0 | 90.6 | 93.9 | +10.9 points over π0.5, zero-shot Robust when the world is perturbed. Ahead of π0.5 on all seven perturbation dimensions, and ahead of the strongest baseline, GuidedVLA, on six of them +9.1 on average . The gap widens on the Long suite: +13.4 over π0.5 and +12.1 over GuidedVLA. Zero-shot success rate % on LIBERO-Plus | Method | Camera | Robot | Language | Light | Background | Noise | Layout | Avg | |---|---|---|---|---|---|---|---|---| | VLDA | 72.4 | 75.3 | 84.0 | 96.0 | 95.5 | 89.4 | 85.7 | 84.5 | | GuidedVLA | 73.7 | 51.4 | 62.6 | 94.6 | 89.0 | 85.2 | 79.9 | 75.4 | | π0.5 | 59.7 | 65.5 | 75.3 | 87.0 | 82.4 | 72.1 | 80.3 | 73.6 | | DreamVLA | 65.0 | 40.8 | 63.5 | 85.7 | 82.6 | 84.9 | 74.0 | 69.9 | | OpenVLA-OFT | 56.4 | 31.9 | 79.5 | 88.7 | 93.3 | 75.8 | 74.2 | 69.6 | | π0 | 13.8 | 6.0 | 58.8 | 85.0 | 81.4 | 79.0 | 68.9 | 53.6 | +20.2 points over π0.5, 10 hard tasks Biggest gains on complex state-transition tasks. Best or tied-best on every selected task. Put Bottles Dustbin jumps +46, Stacking Three +30, Place Dual Shoes +27 and Open Microwave +27. Across all 50 tasks VLDA scores 50.86% against 43.84% for π0.5. Success rate % on RoboTwin 2.0 | Method | Ranking RGB | Ranking Size | Grab Roller | Open Laptop | Open Microwave | Place Dual Shoes | Put Bottles Dustbin | Shake Bottle | Stacking Three | Stacking Two | Avg | |---|---|---|---|---|---|---|---|---|---|---|---| | VLDA | 53 | 16 | 96 | 60 | 64 | 34 | 55 | 100 | 65 | 86 | 62.9 | | π0.5 | 35 | 14 | 89 | 35 | 37 | 7 | 9 | 100 | 35 | 66 | 42.7 | | π0 | 5 | 1 | 80 | 46 | 50 | 0 | 13 | 60 | 24 | 41 | 32.0 | | RDT | 0 | 0 | 43 | 32 | 20 | 4 | 4 | 45 | 17 | 30 | 19.5 | +24 points on Basket Relocation Every module earns its place. Removing any single piece costs accuracy. LT-Filter and GT-Anchor each help alone, and together they help most, notably on the real robot. Ablations LIBERO average unless noted ~~98.5~~ 97.2 ~~98.5~~ 97.3 ~~98.5~~ 97.8 ~~84.5~~ 82.0 LT-Filter and GT-Anchor | LT-Filter | GT-Anchor | LIBERO Spatial | Object | Goal | Long | Ring | Sweep | Sort | Basket | |---|---|---|---|---|---|---|---|---|---| | ✗ | ✗ | 98.6 | 98.4 | 98.0 | 92.8 | 76 | 52 | 48 | 48 | | ✓ | ✗ | 99.4 | 99.4 | 99.0 | 94.4 | 84 | 60 | 56 | 68 | | ✗ | ✓ | 98.8 | 99.0 | 98.8 | 93.8 | 80 | 60 | 52 | 60 | | ✓ | ✓ | 99.8 | 99.6 | 99.4 | 95.2 | 92 | 68 | 60 | 72 | Simulation columns are LIBERO success rate % ; real-world columns are Ring Stacking, Table Sweeping, Conveyor Sorting and Basket Relocation on R1 Lite. Each module helps alone; together they help most. 04 In the real world Still ahead on real robots. Four long-horizon tasks on a GALAXEA R1 Lite, 25 trials each, plus a second platform, AgileX Cobot Magic. Numbers are final-stage success rates % . Ring Stacking 92% vs. π0.5 80% Table Sweeping 68% vs. π0.5 52% Conveyor Sorting 60% vs. π0.5 44% Basket Relocation 72% vs. π0.5 48% +17.0 points over π0.5, R1 Lite Averaged over four tasks, VLDA reaches 73.0% against 56.0% for π0.5. On a second robot the lead holds. Basket Relocation uses an out-of-distribution mat position, and VLDA still reaches 72% at the final placement. | Platform average | OpenVLA-OFT | π0.5 | VLDA | |---|---|---|---| | GALAXEA R1 Lite | 16.0 | 56.0 | 73.0 | | AgileX Cobot Magic | 17.0 | 54.0 | 72.0 | | Δ vs. π0.5 pp | | | +17.0 / +18.0 | What the Delta looks at The Delta concentrates on the regions that move and interact, not on the static scene. 05 Build with VLDA Code and citation. GitHub ↗ https://github.com/baidu-baige/LoongForge/tree/master/cookbook/loongforge-vlda LoongForge-VLDA Cookbook Training code, configs and recipes for VLDA live in LoongForge. git clone https://github.com/baidu-baige/LoongForge.git Citation @misc{zhang2026visionlanguagedeltaactionlearningcompactdeltas, title={Vision-Language-Delta-Action: Learning Compact Deltas as Actionable Transition Interfaces for Robotic Manipulation}, author={Lingsen Zhang and Wei Li and Jie He and Henghua Zhang and Kai Mo and Shiyong Li and Yanpeng Wang and Rui Shao}, year={2026}, eprint={2610.25874}, archivePrefix={arXiv}, primaryClass={cs.RO}, url={https://arxiv.org/abs/2610.25874}, } Read the preprint on arXiv ↗ https://arxiv.org/abs/2610.25874 .