Event
RLT proposes an online reinforcement learning approach that freezes VLA and trains only the control head
1 reports · 1 sources
RLT proposes an approach: freeze the large VLA model and train only a lightweight actor-critic control head. The VLA handles vision, language understanding, and coarse actions, while the small network corrects actions online at critical stages to make up for the final few millimeters of precision; the two are connected through a bridging structure. The report does not provide measured results or comparative data for this approach on specific tasks, and its precision-correction effect is currently only a description of the approach.
Report timeline
- RLT: Teaching a frozen VLA the last few millimeters with online reinforcement learning
RLT proposes freezing the large VLA model and training only a lightweight actor-critic control head: the VLA handles visual and language understanding and coarse actions, while the small network corrects actions online during critical stages to make up the last few millimeters of precision. The two are connected through a bridging structure.
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