When Self-Belief Misleads: Active Label Acquisition for Reinforcement Learning with Verifiable Rewards
arXiv:2605.25864v1 Announce Type: cross Abstract: Large Language Models (LLMs) have achieved remarkable advancements in reasoning capabilities empowered by Reinforcement Learning with Verifiable Rewards (RLVR). Nonetheless, RLVR intrinsically relies on ground-truth labels for reward computation, the acquisition of which is often prohibitively expensive in real-world scenarios. While unsupervised RLVR paradigms attempt to circumvent this by training on pseudo-labels, they are notoriously susceptible to training collapse. Moreover, different samples often exhibit varying annotation values. In th
This paper addresses a fundamental limitation in current RLVR applications, the cost and reliability of ground-truth labels, which is becoming more acute as LLMs scale and their applications proliferate.
Improving the efficiency and robustness of AI training, particularly for advanced reasoning models, directly impacts the pace of AI development and deployment across various industries.
New methods for active label acquisition in RLVR could significantly reduce annotation costs and improve model stability, enabling more practical and scalable AI system development.
- · AI research labs
- · Companies developing LLM applications
- · Data annotation services
- · AI infrastructure providers
- · Companies reliant on expensive, manual data labeling
- · AI models prone to training collapse
Reduced cost and faster development cycles for complex AI systems leveraging Reinforcement Learning with Verifiable Rewards.
Accelerated deployment of more capable and reliable AI agents and autonomous systems in real-world environments.
Increased competition and innovation in AI-driven services, possibly leading to market consolidation around superior AI platforms.
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