Expanding RL with Verifiable Rewards Across Diverse Domains

Arxiv paper investigating how reinforcement learning with verifiable rewards (RLVR) generalises beyond the easy cases (math, code) to more diverse domains. The technical paper whose conceptual shadow Royzen's domain-claim entry sits in.

Grounds the 'verifiable-reward domain' framing in the ML research literature. Useful for readers who want the technical story behind practitioner claims that finance, code, and math are uniquely favourable.

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Technical complement to the practitioner entries on verifiable-reward domains. The paper asks the question the library should keep asking: which diverse domains does RLVR actually generalise to, and what breaks when it doesn't? Rhetorically, this entry is included to prevent the library from collapsing "verifiable reward" into a slogan. There is a research program behind it with real empirical findings — both supporting and complicating the practitioner framings. Read alongside • Royzen: Standard Signal — the finance-domain-favourability claim. • Rubrics as Rewards (RaR) — extending the framing past crisp-outcome domains. • Wallach/Jacobs et al — the measurement-validity pushback.

Title and URL verified. First-author and full author list not confirmed; arxiv date is best-estimate from the arxiv ID (2503 = March 2025). Confirm before citing.