Organizations: INFIFORCE Intelligent Technology Co., Ltd. Hangzhou, China · Huazhong University of Science and Technology, Wuhan, China · The University of Hong Kong, Hong Kong SAR, China · Tsinghua University, Beijing, China
Vision-Language-Action (VLA) models demonstrate remarkable potential for generalizable robotic manipulation. The execution of complex multi-step behaviors in VLA models can be improved by robust instruction grounding, a critical component for effective control. However, current paradigms predominantly rely on coarse, high-level task instructions during supervised fine-tuning. This instruction grounding gap leaves models without explicit intermediate guidance, leading to severe compounding errors in long-horizon tasks. Therefore, bridging this instruction gap and providing scalable post-training for VLA models is urgent. To tackle this problem, we propose \method, the first subtask-aware VLA framework integrated with a scalable offline post-training pipeline. Our framework leverages a large language model to decompose high-level demonstrations into fine-grained atomic subtasks. This approach utilizes a pretrained predictive world model to score candidate action chunks against subtask goals in the latent space, mitigating error accumulation while significantly improving long-horizon robustness. Furthermore, this approach enables highly efficient Group Relative Policy Optimization without the prohibitive expenses associated with online rollouts on physical robots. Extensive simulations validate that our AtomVLA maintains strong robustness under perturbations. When evaluated against fundamental baseline models, it achieves an average success rate of 97.0% on the LIBERO benchmark and 48.0% on the LIBERO-PRO benchmark. Finally, experiments conducted in the real world using the Galaxea R1 Lite platform confirm its broad applicability across diverse tasks, especially long-horizon tasks. All datasets, checkpoints, and code will be released to the public domain following the acceptance of this work for future research.
Figures & tables
Fig. 2: (a) Typical VLA models rely on SFT Training. (b) AtomVLA (Ours) leverages a language model for fine-grained decomposition of atomic subtask instructions and a world model for RL post-training.
Fig. 3: Training pipeline. Stage I: high-level instructions are decomposed into fine-grained atomic subtask instructions using LLM (GPT-4o). Subsequently, these subtask instructions are integrated with the original high-level instruction to guide the SFT training of the model. Stage II: A predictive latent world model evaluates candidate action rollouts to provide reward for offline post-training.
Input
LIBERO-Long
Image
80.4%
Image + High-level Task
90.0%
Image + High-level Task + Atomic Task
92.2%
TABLE I: Different Input on LIBERO-Long. Bold denotes the best performance among all methods.
Chunk Size
Spatial
Object
Goal
Long
Avg.
4
96.4%
99.6%
97.6%
94.4%
97.0%
8
95.6%
99.2%
98.4%
93.0%
96.6%
16
96.0%
99.8%
98.8%
91.6%
96.6%
32
96.0%
99.0%
98.8%
91.2%
96.3%
TABLE II: Comparison of AtomVLA on the LIBERO benchmark with different numbers of action chunk horizon. Bold denotes the best performance among all methods.
Method
w/o Pre-Training
Model Size
LIBERO-Spatial
LIBERO-Object
LIBERO-Goal
LIBERO-Long
Avg.
TraceVLA [ 33 ]
✗
7 B
84.6%
85.2%
75.1%
54.1%
74.8%
OpenVLA [ 3 ]
✓
7 B
84.7%
88.4%
79.2%
53.7%
76.5%
SpatialVLA [ 19 ]
✓
4 B
88.2%
89.9%
78.6%
55.5%
78.1%
CoT-VLA [ 34 ]
✓
7 B
87.5%
91.6%
87.6%
69.0%
83.9%
π0 [ 6 ]
✓
4 B
96.8%
98.8%
95.8%
85.2%
94.2%
ThinkAct [ 36 ]
✗
7 B
88.3%
91.4%
87.1%
70.9%
84.4%
TABLE III: Comparisons with state-of-the-art methods on the LIBERO benchmark [ 31 ] . Bold denotes the best performance among all methods.
Method
LIBERO-Goal
LIBERO-Spatial
LIBERO-10
LIBERO-Object
Avg.
Obj
Pos
Sem
Task
Obj
Pos
Sem
Task
Obj
Pos
Sem
Task
Obj
Pos
Sem
Task
π0 [ 6 ]
0.94
0.00
0.93
0.00
0.95
0.00
0.97
0.00
0.79
0.00
0.82
0.00
0.94
0.00
0.90
0.00
0.45
MolmoAct [ 37 ]
0.68
0.00
0.85
0.00
0.90
0.00
0.88
0.00
0.54
0.00
0.74
0.06
0.92
0.06
0.96
0.00
0.41
NORA [ 35 ]
0.58
0.00
0.88
0.00
0.92
0.00
0.91
0.00
0.46
0.00
0.74
0.00
0.86
0.00
0.92
0.00
0.39
X-VLA [ 38 ]
0.68
0.01
0.98
0.09
0.97
0.00
0.96
0.00
0.62
0.00
0.95
0.10
0.89
0.02
0.98
0.08
0.46
AtomVLA (Ours)
0.81
0.02
0.98
0.11
0.95
0.16
0.95
0.01
0.55
0.01
0.95
0.09
0.93
0.10
0.99
0.00
0.48
TABLE IV: Comparisons with state-of-the-art methods on the LIBERO-PRO benchmark [ 32 ] . Bold denotes the best performance among all methods.
Fig. 4: Visualization of real-world tasks. The top two rows illustrate basic tasks to stack bowls, place fruit into a basket, hang the cup, and open the drawer. The bottom two rows demonstrate hard, long-horizon tasks to fold a T-shirt and a towel.
Reward
Spatial
Object
Goal
Long
Avg.
SFT
94.1%
95.4%
92.4%
90.0%
93.0%
Post-Training
Esub(k)+D(k)
96.3%
98.0%
96.2%
93.5%
96.0%
Egoal(k)+D(k)
97.1%
97.9%
96.0%
93.2%
96.1%
Esub(k)+Egoal(k)+D(k)
96.4%
99.6%
97.6%
94.4%
97.0%
TABLE V: Ablation of reward formulations on LIBERO Benchmark [ 31 ] . Bold denotes the best performance among all methods.
TABLE VI: Experimental results of AtomVLA and baseline on six real-world Galaxea R1 Lite robot manipulation tasks. Bold denotes the best performance among all methods.
Fig. 6: The four types of variation introduced under the GE setting to evaluate the robustness of AtomVLA. (a) Object position variation. (b) Unseen distractor object. (c) Target height variation. (d) Instruction language variation.
Appendix figures & tables5 assets
Supplementary material from the paper’s appendix.
Appendix
Fig. 7: Atomic subtasks on the LIBERO Dataset.
Hyperparameter
Value
# GPUs
4× NVIDIA H100 (80GB VRAM)
learning rate (LR)
5e−4
batch size
8 (per GPU)
# epoch number
2 epoch
input images
1 third-person camera image, 1 wrist-mounted camera image