Siemens and UC Berkeley Unveil ARLI to Enable RL Fine-Tuning of VLAs Under Inference Latency

Siemens and UC Berkeley's Sergey Levine team introduced ARLI (Asynchronous RL with Intermediate Information), a framework that enables effective reinforcement learning fine-tuning of vision-language-action (VLA) robot policies despite asynchronous inference latency, as detailed in the paper 'Learning to Act While Waiting.'

2026-09-22 ~ 2026-09-22 · 2 related posts

• Siemens and Berkeley introduce ARLI for RL fine-tuning of VLAs under inference latency — svlevine · 2026-09-22
• ARLI: Latency-Aware RL Finetuning Fixes VLA Robot Policies Where Standard RL Fails — oier_mees · 2026-09-22