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Sample Complexity of Policy Gradient for Log-Growth Control

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arXiv:2605. 26640v1 Announce Type: cross Abstract: We study the sample complexity of policy gradient for log-growth control -- the problem of learning, from observed state transitions, a feedback gain that optimally stabilizes a scalar linear system driven through a multiplicative-noise actuation channel.

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Authors: Qiuhua Pan, Yukai Shen, Liwei Zhang, Cailian Chen, Xinping Guan
Released: 2026-05-27 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because Sample Complexity of Policy Gradient for Log-Growth Control overlaps with clean result "The marker is a representational handle, not a behavioural one — sharing it between a villain persona and the assistant transfers no misalignment (HIGH confidence)", experiment "Implement Chen et al. persona-vector extraction recipe and compare to project's centroid-difference recipe", experiment "Add C2 control arm (donor sees marker_B without marker_A) to disambiguate paired-marker binding from marker_B leaking alone". Matching terms: line, project, control, does, symmetry. Source: arxiv stat.ML (Machine Learning).
arXiv: 2605.26640

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