The paper identifies four missing interfaces (data autolabelling, embodiment retargeting, physics-grounded world models, and video-based reward inference) as the central bottleneck beyond VLA scaling for robot intelligence.
Prioritized level replay
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
Gated synthetic augmentations can substitute for additional human-authored RLVR tasks at a cost-adjusted trade rate of 1.4x-11.6x while retaining held-out generalization on ten benchmarks spanning code, instruction following, reasoning, and agentic function calling.
A curriculum sampling questions with high variance in success rate improves reinforcement learning performance for LLM reasoning tasks.
citing papers explorer
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Robots Need More than VLA and World Models
The paper identifies four missing interfaces (data autolabelling, embodiment retargeting, physics-grounded world models, and video-based reward inference) as the central bottleneck beyond VLA scaling for robot intelligence.
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Trading Human Curation for Synthetic Augmentation in RLVR
Gated synthetic augmentations can substitute for additional human-authored RLVR tasks at a cost-adjusted trade rate of 1.4x-11.6x while retaining held-out generalization on ten benchmarks spanning code, instruction following, reasoning, and agentic function calling.