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Automated Curriculum Learning for Multi-Domain RLVR: Leveraging Cross-Domain Transferability
A new arXiv paper proposes using cross-domain transferability of reasoning skills to dynamically adjust multi-domain RLVR training curricula, addressing inefficiency in fixed sampling strategies.
A paper titled 'Transferability for General Reasoning: An Automated Curriculum for Multi-Domain RLVR' has been published on arXiv. The research notes that reinforcement learning with verifiable rewards has been extended from single-domain training to multi-domain reasoning suites spanning mathematics, programming, and science.
However, the training curriculum is typically fixed or hand-tuned, even though reasoning skills transfer unevenly across domains. Existing learnability-based curricula adapt to where the policy is currently improving but are blind to where more sampling should occur to maximize overall gains.
The source is arXiv cs.AI (ID 2606.25178), published on June 25, 2026.
Why it matters
This research provides an automated curriculum learning approach for multi-domain RLVR training with the potential to significantly improve training efficiency and multi-domain reasoning capability.