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EVOM: Agentic Meta-Evolution of Actor-Critic Architectures for Reinforcement Learning

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arXiv:2606. 26327v1 Announce Type: new Abstract: In actor-critic reinforcement learning, network architectures are typically manually designed.

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Authors: Boyun Zhang, Chao Wang, Kai Wu
Released: 2026-06-26 (1mo ago)
Topic: Related to current project
Relation to my work: Read next because EVOM: Agentic Meta-Evolution of Actor-Critic Architectures for Reinforcement Learning overlaps with clean result "Only continuous soft prefixes hit both EM axes at once on Qwen-2.5-7B-Instruct: discrete prompt searches split between the alignment objective and the distributional objective, and both discretizations of the soft prefix collapse (MODERATE confidence)", 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 "Add C2 control arm (donor sees marker_B without marker_A) to disambiguate paired-marker binding from marker_B leaking alone". Matching terms: eval, line, rate, control, trained, candidate. Source: arxiv cs.LG (Machine Learning).
arXiv: 2606.26327

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