arXiv:2606.03087v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) improves the ability of large language model, yet headline accuracy gains often conceal a hidden cost: previously solved problems quietly become unsolvable as training proceeds. We frame this phenomenon as \emph{correct-set turnover}, representing the coupled dynamics of solution acquisition and regression over the mastered set. Under this view, retention becomes an explicit optimization target alongside acquisition. We analytically and empirically establish the \emph{repair-window principle}:
The increasing complexity and scale of AI models, particularly large language models (LLMs) and reinforcement learning, are revealing novel and often counterintuitive challenges in their training and long-term stability.
Understanding and addressing 'correct-set turnover' is crucial for developing robust, reliable, and continuously improving AI systems, preventing performance degradation over time, and ensuring deployed AI systems maintain their capabilities.
This research shifts the focus from merely achieving high accuracy to explicitly considering the optimization target of retention alongside acquisition in AI training, which can lead to more stable and trustworthy AI models.
- · AI researchers focused on learning stability
- · Developers of mission-critical AI systems
- · Companies investing in long-term AI maintenance
- · AI developers prioritizing only peak performance
- · Organizations with production AI systems exhibiting silent performance decay
AI training methodologies will incorporate metrics and techniques to explicitly counter 'correct-set turnover' and prevent 'forgetting'.
The development of more resilient AI systems will accelerate, leading to higher confidence in their application across various industries.
Improved AI retention mechanisms could reduce the compute and energy costs associated with retraining models for lost knowledge, potentially easing pressure on the energy bottleneck.
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Read at arXiv cs.LG