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Position: Deployed Reinforcement Learning should be Continual

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arXiv:2606. 04029v1 Announce Type: new Abstract: Reinforcement Learning (RL) has received increasing attention and adoption in real-world use cases.

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Authors: Parnian Behdin, Kevin Roice, Golnaz Mesbahi
Released: 2026-06-05 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because Position: Deployed Reinforcement Learning should be Continual 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 "Training one persona to emit a [ZLT] marker without bystanders adopting it has a one-cell-wide LR x epochs window on Qwen2.5-7B-Instruct (LOW confidence)", experiment "Follow-up to #354: cascading chunk-binding — does A→B, B→C, C→D propagate the full chain on a recipient trained only to emit A?". Matching terms: eval, source, trained, position, never. Source: arxiv cs.LG (Machine Learning).
arXiv: 2606.04029

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