arXiv:2606.25178v2 Announce Type: replace Abstract: Reinforcement learning with verifiable rewards (RLVR) has been extended from single-domain training to multi-domain reasoning suites spanning mathematics, programming, and science. However, the training curriculum (how often each domain is sampled) 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 whether a gradient step on the selected domain benefits the remaining domains. In this paper, we
The proliferation of multi-domain AI applications necessitates more efficient and transferable learning methods, moving beyond fixed curricula in complex reasoning tasks.
Improving how AI agents learn and transfer knowledge across diverse domains directly impacts the scalability and general intelligence of AI systems, accelerating their deployment in real-world applications.
The shift from fixed or hand-tuned training curricula to automated, transfer-aware curriculum generation fundamentally alters how multi-domain reasoning agents are developed and optimized.
- · AI development firms
- · Robotics
- · Generative AI
- · Software companies
- · Manual AI curriculum designers
- · AI models with limited domain transfer
More robust and adaptable AI agents capable of mastering multiple complex tasks with less specialized training.
Accelerated development and deployment of autonomous AI agents across various industries, including scientific research, engineering, and service sectors.
Enhanced overall AI capabilities that contribute to the emergence of more general artificial intelligence, capable of solving novel problems with reduced human oversight.
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Read at arXiv cs.AI