Training Vision-Language-Action Models with Dense Embodied Chain-of-Thought Supervision
Cross-embodiment transfer in vision-language-action (VLA) models remains challenging because low-level state and action spaces differ fundamentally across robot platforms. We observe that the high-level cognitive process underlying manipulation, including scene perception, object identification, task planning, and sub-task decomposition, is largely shared across embodiments. Based on this observation, we present ZR-0, a 2.6 billion parameter end-to-end VLA model that uses dense Embodied Chain-of-Thought (ECoT) supervision to align cross-embodiment representations within the vision-language mod
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- LinkedLinked via unknownvlm-starter →
- FuzzySimilar title/name (fuzzy) · 59%VioletVision-3B →
“Fuzzy title match (0.73): “Training Vision-Language-Action Models with Dense Embodied C” ≈ “VioletVision-3B””
- PossiblePossibly related (embedding) · 45%I trained a vision-language model to play Snake, and so can you. [P] →
- FuzzySimilar title/name (fuzzy) · 84%liguodongiot/llm-action →
“Fuzzy title match (0.92): “Training Vision-Language-Action Models with Dense Embodied C” ≈ “liguodongiot/llm-action””
- FuzzySimilar title/name (fuzzy) · 84%pytorch/vision →
“Fuzzy title match (0.92): “Training Vision-Language-Action Models with Dense Embodied C” ≈ “pytorch/vision””
- FuzzySimilar title/name (fuzzy) · 84%roboflow/supervision →
“Fuzzy title match (0.92): “Training Vision-Language-Action Models with Dense Embodied C” ≈ “roboflow/supervision””