Training-Time Action Conditioning for Efficient Real-Time Chunking

• URL: http://arxiv.org/abs/2512.05964v2
• Date: Tue, 09 Dec 2025 01:07:28 GMT
• Title: Training-Time Action Conditioning for Efficient Real-Time Chunking
• Abstract summary: We propose simulating inference delay at training time and conditioning on action prefixes directly, eliminating inference-time overhead.<n>In simulated experiments, we find that training-time RTC outperforms inference-time RTC at higher inference delays.<n>Our results suggest that training-time action conditioning is a practical drop-in replacement for inference-time inpainting in real-time robot control.
• Score: 45.91723746443767
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Real-time chunking (RTC) enables vision-language-action models (VLAs) to generate smooth, reactive robot trajectories by asynchronously predicting action chunks and conditioning on previously committed actions via inference-time inpainting. However, this inpainting method introduces computational overhead that increases inference latency. In this work, we propose a simple alternative: simulating inference delay at training time and conditioning on action prefixes directly, eliminating any inference-time overhead. Our method requires no modifications to the model architecture or robot runtime, and can be implemented with only a few additional lines of code. In simulated experiments, we find that training-time RTC outperforms inference-time RTC at higher inference delays. In real-world experiments on box building and espresso making tasks with the $π_{0.6}$ VLA, we demonstrate that training-time RTC maintains both task performance and speed parity with inference-time RTC while being computationally cheaper. Our results suggest that training-time action conditioning is a practical drop-in replacement for inference-time inpainting in real-time robot control.

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