Rethinking Inference-Time Scaling in Local Computer-Use Agents: Failure Modes and Compute Tradeoffs
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TL;DR - A systematic OSWorld study finds that inference-time scaling for resource-constrained local computer-use agents often yields diminishing returns and shifts failure modes rather than reliably improving task success. Selective compute allocation and failure-aware controls may be more effective than uniformly increasing computation.
- More context improves trajectory stability and accuracy, but gains saturate as token costs rise.
- Longer execution horizons reduce max-step stalls without substantially improving success, often prolonging erroneous trajectories.
- Structural decomposition adds planning and formatting overhead to local two-stage agents.
- Parallel scaling partially mitigates failures, but at substantial computational cost.
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Rethinking Inference-Time Scaling in Local Computer-Use Agents: Failure Modes and Compute Tradeoffs
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TL;DR - A systematic OSWorld study finds that inference-time scaling for resource-constrained local computer-use agents often yields diminishing returns and shifts failure modes rather than reliably improving task success. Selective compute allocation and failure-aware controls may be more effective than uniformly increasing computation.
- More context improves trajectory stability and accuracy, but gains saturate as token costs rise.
- Longer execution horizons reduce max-step stalls without substantially improving success, often prolonging erroneous trajectories.
- Structural decomposition adds planning and formatting overhead to local two-stage agents.
- Parallel scaling partially mitigates failures, but at substantial computational cost.