Beyond Reasoning Gains: Mitigating General-Capability Forgetting in Large Reasoning Models
📰 ArXiv cs.AI
Mitigate general-capability forgetting in large reasoning models by employing regularization strategies during reinforcement learning with verifiable rewards (RLVR) training
Action Steps
- Apply regularization techniques to RLVR training to prevent capability regression
- Configure RLVR training paradigm to incorporate verifiable rewards and mitigate forgetting
- Test models for general-capability forgetting after prolonged RLVR training
- Compare performance of models with and without regularization strategies
- Run experiments to evaluate the effectiveness of different regularization techniques in preventing capability regression
Who Needs to Know This
AI researchers and engineers working on large language and vision-language models can benefit from this knowledge to improve model performance and prevent capability regression
Key Insight
💡 Regularization strategies can help mitigate general-capability forgetting in large reasoning models during RLVR training
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🚀 Prevent capability regression in large reasoning models with regularization strategies during RLVR training! 🤖
Key Takeaways
Mitigate general-capability forgetting in large reasoning models by employing regularization strategies during reinforcement learning with verifiable rewards (RLVR) training
Full Article
Title: Beyond Reasoning Gains: Mitigating General-Capability Forgetting in Large Reasoning Models
Abstract:
arXiv:2510.21978v2 Announce Type: replace-cross 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 conf
Abstract:
arXiv:2510.21978v2 Announce Type: replace-cross 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 conf
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