Toward Plasticity-Preserving KL Regularization for Capability Retention in LLM Reinforcement Learning | Paper Citation Record

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Toward Plasticity-Preserving KL Regularization for Capability Retention in LLM Reinforcement Learning H.; Gonzalez, J.; Zhang, H.; and Stoica, I

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Toward Plasticity-Preserving KL Regularization for Capability Retention in LLM Reinforcement Learning HybridFlow: A Flexible and Efficient RLHF Framework

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Toward Plasticity-Preserving KL Regularization for Capability Retention in LLM Reinforcement Learning 2024 , journal =

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Toward Plasticity-Preserving KL Regularization for Capability Retention in LLM Reinforcement Learning Proceedings of the 29th symposium on operating systems principles , pages=

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Toward Plasticity-Preserving KL Regularization for Capability Retention in LLM Reinforcement Learning Qwen3 Technical Report

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Toward Plasticity-Preserving KL Regularization for Capability Retention in LLM Reinforcement Learning Fine-Tuning Language Models from Human Preferences

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Toward Plasticity-Preserving KL Regularization for Capability Retention in LLM Reinforcement Learning DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

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Toward Plasticity-Preserving KL Regularization for Capability Retention in LLM Reinforcement Learning arXiv preprint arXiv:2509.07430 , year=

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Toward Plasticity-Preserving KL Regularization for Capability Retention in LLM Reinforcement Learning Implicit Hierarchical GRPO: Decoupling Tool Invocation from Execution for Tool-Integrated Mathematical Reasoning

Reference 50

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Toward Plasticity-Preserving KL Regularization for Capability Retention in LLM Reinforcement Learning arXiv preprint arXiv:2510.03865 , year=

Reference 51

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Toward Plasticity-Preserving KL Regularization for Capability Retention in LLM Reinforcement Learning SAGE: Shaping Anchors for Guided Exploration in RLVR of LLMs

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Toward Plasticity-Preserving KL Regularization for Capability Retention in LLM Reinforcement Learning arXiv preprint arXiv:2510.20817 , year=

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Toward Plasticity-Preserving KL Regularization for Capability Retention in LLM Reinforcement Learning expo: Exploration-prioritized policy optimization via adaptive kl regulation and gaussian curriculum sampling

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Toward Plasticity-Preserving KL Regularization for Capability Retention in LLM Reinforcement Learning Stabilizing Knowledge, Promoting Reasoning: Dual-Token Constraints for RLVR

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Toward Plasticity-Preserving KL Regularization for Capability Retention in LLM Reinforcement Learning The Entropy Mechanism of Reinforcement Learning for Reasoning Language Models

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Toward Plasticity-Preserving KL Regularization for Capability Retention in LLM Reinforcement Learning Findings of the Association for Computational Linguistics: NAACL 2025 , pages=

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Toward Plasticity-Preserving KL Regularization for Capability Retention in LLM Reinforcement Learning DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

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Toward Plasticity-Preserving KL Regularization for Capability Retention in LLM Reinforcement Learning Group Sequence Policy Optimization

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Toward Plasticity-Preserving KL Regularization for Capability Retention in LLM Reinforcement Learning SimpleTIR: End-to-End Reinforcement Learning for Multi-Turn Tool-Integrated Reasoning

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Toward Plasticity-Preserving KL Regularization for Capability Retention in LLM Reinforcement Learning arXiv preprint arXiv:2509.21826 , year=

Reference 61

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Toward Plasticity-Preserving KL Regularization for Capability Retention in LLM Reinforcement Learning ResRL: Boosting LLM Reasoning via Negative Sample Projection Residual Reinforcement Learning

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Toward Plasticity-Preserving KL Regularization for Capability Retention in LLM Reinforcement Learning Contextual Rollout Bandits for Reinforcement Learning with Verifiable Rewards

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Toward Plasticity-Preserving KL Regularization for Capability Retention in LLM Reinforcement Learning Advances in Neural Information Processing Systems , volume=

Reference 64

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Toward Plasticity-Preserving KL Regularization for Capability Retention in LLM Reinforcement Learning arXiv preprint arXiv:2507.14783 , year=

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Toward Plasticity-Preserving KL Regularization for Capability Retention in LLM Reinforcement Learning Can One Domain Help Others? A Data-Centric Study on Multi-Domain Reasoning via Reinforcement Learning

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Toward Plasticity-Preserving KL Regularization for Capability Retention in LLM Reinforcement Learning arXiv preprint arXiv:2602.12566 , year=

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Toward Plasticity-Preserving KL Regularization for Capability Retention in LLM Reinforcement Learning arXiv preprint arXiv:2602.02301 , year=

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Toward Plasticity-Preserving KL Regularization for Capability Retention in LLM Reinforcement Learning arXiv preprint arXiv:2505.17508 , year=

Reference 69

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Toward Plasticity-Preserving KL Regularization for Capability Retention in LLM Reinforcement Learning Advances in Neural Information Processing Systems , volume=

Reference 70

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Toward Plasticity-Preserving KL Regularization for Capability Retention in LLM Reinforcement Learning MemAgent: Reshaping Long-Context LLM with Multi-Conv RL-based Memory Agent

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Toward Plasticity-Preserving KL Regularization for Capability Retention in LLM Reinforcement Learning Advances in Neural Information Processing Systems , volume=

Reference 72

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Toward Plasticity-Preserving KL Regularization for Capability Retention in LLM Reinforcement Learning Measuring Mathematical Problem Solving With the MATH Dataset

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Toward Plasticity-Preserving KL Regularization for Capability Retention in LLM Reinforcement Learning Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=

Reference 74

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Toward Plasticity-Preserving KL Regularization for Capability Retention in LLM Reinforcement Learning WildChat: 1M Chat

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Toward Plasticity-Preserving KL Regularization for Capability Retention in LLM Reinforcement Learning Advances in Neural Information Processing Systems , volume=

Reference 76

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Toward Plasticity-Preserving KL Regularization for Capability Retention in LLM Reinforcement Learning arXiv preprint arXiv:2512.15489 , year =

Reference 77

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Toward Plasticity-Preserving KL Regularization for Capability Retention in LLM Reinforcement Learning arXiv preprint arXiv:2509.20357 , year=

Reference 78

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Toward Plasticity-Preserving KL Regularization for Capability Retention in LLM Reinforcement Learning Skywork-Reward: Bag of Tricks for Reward Modeling in LLMs

Reference 79

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Toward Plasticity-Preserving KL Regularization for Capability Retention in LLM Reinforcement Learning arXiv preprint arXiv:2512.05962 , year=

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Toward Plasticity-Preserving KL Regularization for Capability Retention in LLM Reinforcement Learning Uniform-Correct Policy Optimization: Breaking RLVR's Indifference to Diversity

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Toward Plasticity-Preserving KL Regularization for Capability Retention in LLM Reinforcement Learning arXiv preprint arXiv:2602.19895 , year=

Reference 82

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Toward Plasticity-Preserving KL Regularization for Capability Retention in LLM Reinforcement Learning Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing , pages=

Reference 83

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Toward Plasticity-Preserving KL Regularization for Capability Retention in LLM Reinforcement Learning International Conference on Learning Representations , volume=

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Observation ee7e51f3-7ccf-4a53-b5e3-504e3936633f · outbound

This paper cites Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback.

Toward Plasticity-Preserving KL Regularization for Capability Retention in LLM Reinforcement Learning Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-04T21:54:44.359793Z

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Observation 567c4997-dfc7-44bc-90ee-c1b8dd195bdc · outbound

This paper cites arXiv preprint arXiv:2507.14843 , year=.

Toward Plasticity-Preserving KL Regularization for Capability Retention in LLM Reinforcement Learning arXiv preprint arXiv:2507.14843 , year=

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-04T21:54:44.363782Z

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Observation 4a43e7f0-9217-49ef-97ad-685026069a89 · outbound

This paper cites Understanding R1-Zero-Like Training: A Critical Perspective.

Toward Plasticity-Preserving KL Regularization for Capability Retention in LLM Reinforcement Learning Understanding R1-Zero-Like Training: A Critical Perspective

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-04T21:54:44.368038Z

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