The Speedup Paradox: Rethinking Inference Speed-Quality Trade-off in Embodied Tasks
This paper proposes TISED, a framework that unifies lossy inference optimization techniques and reveals paradoxical effects in embodied tasks: optimization may lengthen completion time in static tasks, while moderate optimization can improve success rate in dynamic tasks, with hardware configuration shifting the sweet spot.
[2606.28529] The Speedup Paradox: Rethinking Inference Speed-Quality Trade-off in Embodied Tasks
[Submitted on 26 Jun 2026]
Title:The Speedup Paradox: Rethinking Inference Speed-Quality Trade-off in Embodied Tasks
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Abstract:Embodied foundation models have recently been widely used to improve robot generalization and task success rates. Previous works apply lossy efficient-inference techniques such as quantization, pruning, and asynchronous inference, accepting small action quality degradation in exchange for lower per-step computation cost and inter-action latency. However, unlike traditional static ML tasks, embodied tasks involve repeated interaction with the environment, and task-level performance is determined not only by per-step cost, but also by closed-loop effects unique to embodied execution, which remain insufficiently characterized in current efficient-inference studies. In this work, we propose TISED (\underline{T}ask-level \underline{I}nference \underline{S}peedup \underline{E}ffect \underline{D}ecomposition), an analytical framework that unifies diverse lossy inference optimization techniques and decomposes their effects on static and dynamic tasks, and uncovers some paradoxical effects on task-level performance: (1) on \textit{static tasks}, optimization sometimes can lengthen end-to-end per-task completion time even as per-step latency drops; (2) on \textit{dynamic tasks}, moderate lossy optimization can raise task success rate even above the baseline; and (3) the monotonicity and sweet-spot location of both effects can shift with hardware configuration. Together, our findings provide a new perspective on adapting inference optimization techniques to embodied tasks.
Comments: 23 pages
Subjects:
Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2606.28529 [cs.RO]
(or arXiv:2606.28529v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.28529
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Yujin Wang [view email] [v1] Fri, 26 Jun 2026 18:28:52 UTC (3,034 KB)
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