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Beyond Reasoning Gains: Mitigating General-Capability Forgetting in Large Reasoning Models
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 re
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