Instruction drift is a sampling error in VLA models that CAPS mitigates via power distributions and SNR-based metacognitive MCMC switching, yielding better long-horizon results without retraining.
Using the Taylor expansionln(1−γ)≈ −γ(asγ→0 +), we express the limit behavior ofN min as: lim γ→0+ Nmin = lim γ→0+ K ln(1−γ) ≈lim γ→0+ K −γ =∞(33) where K=αlnϵ−lnC <0 is a constant
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Drift is a Sampling Error: SNR-Aware Power Distributions for Long-Horizon Robotic Planning
Instruction drift is a sampling error in VLA models that CAPS mitigates via power distributions and SNR-based metacognitive MCMC switching, yielding better long-horizon results without retraining.