arXiv:2605.28631v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) can yield large reasoning gains from very few training instances, yet its strong sensitivity to which instances are used makes data selection a central bottleneck. Most existing selection pipelines rely on training-time optimization signals and/or require access to verifiable rewards or ground-truth answers over large candidate pools, which is costly and often infeasible in specialized domains. We study RLVR data selection in a setting where selection must be performed before any RL training a
The increasing complexity and cost of training advanced AI models, particularly in specialized and data-scarce domains, make efficient data selection a critical challenge now.
This research offers a method to significantly reduce the computational cost and data requirements for reinforcement learning with verifiable rewards, accelerating AI development in areas with limited verifiable data.
AI development pipelines can now potentially achieve robust performance with fewer training instances and without costly, large-scale data annotation or extensive pre-training optimization signals.
- · AI developers in specialized domains (e.g., scientific discovery, robotics)
- · Organizations with limited data resources
- · AI research and development
- · AI start-ups with compute constraints
- · Companies reliant on brute-force large-scale data acquisition
- · Traditional, high-cost data labeling services
The adoption of training-free data selection methods decreases the barrier to entry for developing powerful RLVR systems.
This could democratize advanced AI capabilities, allowing smaller entities to compete more effectively in specialized AI applications.
Reduced dependence on massive datasets might shift the competitive advantage from data quantity to data quality and algorithmic efficiency.
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Read at arXiv cs.LG