Elastic Queries Reinforcement Learning: Self-Aware Policy Execution for VLA Models

A generative policy acting in a closed loop must decide what action to produce, how much inference to spend producing it, and when to observe again. Flow-based vision-language-action (VLA) policies typically fix the latter two choices across an entire task, even as the value of computation and feedback changes across states. We formulate each policy query as a variable-duration decision over latent input, denoising steps, and execution length, and introduce Elastic Queries Reinforcement Learning (EQRL) to learn these choices while keeping the VLA frozen. A lightweight actor jointly learns latent steering and two schedule residuals around a difficulty-guided reference, using duration-aware query-level RL with a number-of-function-evaluations (NFE) objective. A local compute–feedback analysis distinguishes schedules with equal amortized inference cost. On the long-horizon LIBERO-10 task of putting both moka pots on the stove, EQRL raises five-seed success from 0.411 to 0.861 while reducing requested NFE from 0.500 to 0.441. On four LIBERO-90 tasks and ALOHA Cube Transfer, EQRL improves aggregate success and learning AUC while reducing requested NFE by 39.4% and 14.0% relative to DSRL. Scheduling ablations support the contributions of joint control and difficulty guidance, and physical-robot evaluations match or exceed DSRL's success while reducing inference cost and execution time across five tasks.