🔗 Who cites «Eureka: Human-Level Reward Design via Coding Large Language Models»

94 papers of our library cite it (of the 149,439 whose bibliographies we read) · 8 with a page here, the most cited first · ← back to «Eureka: Human-Level Reward Design via Coding Large Language Models»

1. The AI Scientist: Towards Fully Automated Open-Ended Scientific DiscoveryarXiv:2408.06292Chris Lu, Cong Lu, Robert Tjarko Lange, et al.2024 · 372 citations
2. Executable Code Actions Elicit Better LLM AgentsarXiv:2402.01030Xingyao Wang, Yangyi Chen, Lifan Yuan, et al.2024 · 165 citations
3. A Survey of Reinforcement Learning from Human FeedbackTransactions on Machine Learning ResearchTimo Kaufmann, Paul Weng, Viktor Bengs, et al.2023 · 92 citations
4. Automated Design of Agentic SystemsarXiv:2408.08435Shengran Hu, Cong Lu, Jeff Clune2024 · 89 citations
5. LLMs Can't Plan, But Can Help Planning in LLM-Modulo FrameworksarXiv:2402.01817 · Proceedings of the 41 st International Conference on Machine Learning, Vienna, Austria. PMLR 235, 2024Subbarao Kambhampati, Karthik Valmeekam, Lin Guan, et al.2024 · 88 citations
6. AndroidWorld: A Dynamic Benchmarking Environment for Autonomous AgentsarXiv:2405.14573Christopher Rawles, Sarah Clinckemaillie, Yifan Chang, et al.2024 · 82 citations
7. AutoSOTA: An End-to-End Automated Research System for State-of-the-Art AI Model DiscoveryarXiv:2604.05550Yu Li, Chenyang Shao, Xinyang Liu, et al.2026 · 5 citations
8. Recitation over Reasoning: How Cutting-Edge Language Models Can Fail on Elementary School-Level Reasoning Problems?International Joint Conference on Natural Language Processing (IJCNLP)Kai Yan, Yufei Xu, Zhengyin Du, et al.2025 · 5 citations

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

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