ELASTIC learns a state-dependent meta-policy that allocates sequential and parallel test-time compute for generative control policies, outperforming fixed baselines in simulation and reducing real-robot latency by 34% at matched success.
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ELASTIC: Efficiently Learning to Adaptively Scale Test-Time Compute for Generative Control Policies
ELASTIC learns a state-dependent meta-policy that allocates sequential and parallel test-time compute for generative control policies, outperforming fixed baselines in simulation and reducing real-robot latency by 34% at matched success.