Learning to Act While Waiting: RL Finetuning of Generalist Robot Policies Under Inference Latency
While reinforcement learning (RL) allows generalist robot policies to continually improve during deployment, the large model size of modern generalist policies, such as VLAs, poses a fundamental obstacle to effective RL improvement. In particular, their severe inference latency---which can lead to pauses or jerky movements---can alter the effective environment dynamics and, if not correctly accounted for, break the Markov assumption that RL relies on, causing standard RL algorithms to fail completely. In this work, we introduce a latency-aware framework, Asynchronous RL with Intermediate Information (ARLI), that enables RL-based improvement of generalist policies under inference delays. O...
| Authors | Brian Zhu, Momen Khalil, E Harrison et al. |
|---|---|
| Primary category | cs.RO |
| Published | 24 August 2026 |
| Last updated at source | 26 August 2026 |
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