🔗 Who cites «Echo Chamber: RL Post-training Amplifies Behaviors Learned in Pretraining»

34 papers of our library cite it (of the 149,439 whose bibliographies we read) · 13 with a page here, the most cited first · ← back to «Echo Chamber: RL Post-training Amplifies Behaviors Learned in Pretraining»

1. Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?arXiv:2504.13837 · NeurIPS 2025 Oral; ICML 2025 AI4MATH workshop best paperYang Yue, Zhiqi Chen, Rui Lu, et al.2025 · 216 citations
2. Learning to Reason under Off-Policy GuidanceConference on Neural Information Processing Systems (NeurIPS)Jianhao Yan, Yafu Li, Zican Hu, et al.2025 · 68 citations
3. ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language ModelsConference on Neural Information Processing Systems (NeurIPS)Mingjie Liu, Shizhe Diao, Ximing Lu, et al.2025 · 58 citations
4. Spurious Rewards: Rethinking Training Signals in RLVRarXiv:2506.10947Rulin Shao, Shuyue Stella Li, Rui Xin, et al.2025 · 53 citations
5. Outcome-based Exploration for LLM ReasoningarXiv:2509.06941Yuda Song, Julia Kempe, Remi Munos2025 · 15 citations
6. ERNIE 5.0 Technical ReportarXiv:2602.04705Haifeng Wang, Hua Wu, Tian Wu, et al.2026 · 7 citations
7. Inferring Events from Time Series using Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Mingtian Tan, Mike A Merrill, Zachary Gottesman, et al.2026 · 1 citation
8. Does RLVR Extend Reasoning Boundaries? Investigating Capability Expansion in Vision-Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Minghe Shen, Zhuo Zhi, Chonghan Liu, et al.2026
9. Don't Tell the Answer, Truly Guide the Reasoning During RL RolloutsFindings of the Association for Computational LinguisticsXinyi Wang, Jinyi Han, Zishang Jiang, et al.2026
10. FeedTrans: Enhance Machine Translation via FeedbackTransactions of the Association for Computational Linguistics (TACL)Zengkui Sun, Jiali Zeng, Jiaan Wang, et al.2026
11. Learning from Cognition: Enhancing RL Efficiency for LLM Reasoning via Hierarchical Metacognitive Decomposition and RefinementAnnual Meeting of the Association for Computational Linguistics (ACL)Zexu Sun, Yongcheng Zeng, Erxue Min, et al.2026
12. Mixed-Policy GRPO for Text-to-SQL with Off-Policy Data GenerationWorkshop on Structured Understanding, Retrieval, and Generation in the LLM EraMarko Sterbentz, Michael Glass, Nhan H Pham, et al.2026
13. Tailored Primitive Initialization is the Secret Key to Reinforcement LearningAnnual Meeting of the Association for Computational Linguistics (ACL)Yihang Yao, Guangtao Zeng, Raina Wu, et al.2026

21 more papers of our library cite it; they have no page here yet.

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