arXiv:2510.21978v2 Announce Type: replace Abstract: Reinforcement learning with verifiable rewards (RLVR) has delivered impressive gains in mathematical and multimodal reasoning and has become a standard post-training paradigm for contemporary language and vision-language models. However, the RLVR recipe introduces a significant risk of capability regression, in which models forget foundational skills after prolonged training without employing regularization strategies. We empirically confirm this concern, observing that open-source reasoning models suffer performance degradation on core capab
The increased deployment of advanced AI models and their specialized training paradigms, like RLVR, is revealing complex challenges such as capability forgetting that require immediate research attention.
This highlights a fundamental technical hurdle in AI development, where optimizing for specific complex tasks can inadvertently degrade general foundational abilities, impacting model reliability and general applicability.
AI development paradigms may need to incorporate more sophisticated regularization or multi-objective training approaches to prevent capability forgetting, altering how models are built and optimized.
- · Researchers in continual learning and regularization techniques
- · AI frameworks that incorporate robust forgetting mitigation
- · Developers of more general-purpose AI agents
- · Companies relying solely on RLVR without mitigation strategies
- · AI models that become overly specialized and lose versatility
- · Applications requiring broad foundational AI capabilities
Further research and development will focus on integrating forgetting mitigation into standard AI training pipelines.
The cost and complexity of training high-performance, general-purpose AI models may increase due to the need for advanced regularization.
This could lead to a bifurcation of AI models: highly specialized, powerful but narrow systems versus less performant but broadly capable general models.
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Read at arXiv cs.LG