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# Who cites «STaR: Bootstrapping Reasoning With Reasoning» · page 2

585 papers of our library cite it, newest first: found in their bibliographies, which we read ourselves. [← the card](https://md.mova.elamur.ai/free/en/docs/papers/zelikman-2022-star-bootstrapping-reasoning-reasoning/)

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1. [LLM-Friendly Knowledge Representation for Customer Support](https://aclanthology.org/2025.coling-industry.42/) *— 2025 · Proceedings of the 31st International Conference on Computational Linguistics: Industry Track*
2. [LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!](https://arxiv.org/abs/2502.07374) *— 2025 · arXiv:2502.07374*
3. [Language Models Can Easily Learn to Reason from Demonstrations](https://aclanthology.org/2025.findings-emnlp.866/) *— 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025*
4. [Language models can learn implicit multi-hop reasoning, but only if they have lots of training data](https://aclanthology.org/2025.emnlp-main.490/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
5. [Learning from Committee: Reasoning Distillation from a Mixture of Teachers with Peer-Review](https://aclanthology.org/2025.findings-acl.217/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
6. [Learning to Reason via Self-Iterative Process Feedback for Small Language Models](https://aclanthology.org/2025.coling-main.203/) *— 2025 · Proceedings of the 31st International Conference on Computational Linguistics*
7. [Learning to Reason without External Rewards](https://arxiv.org/abs/2505.19590) *— 2025 · arXiv:2505.19590*
8. [Leveraging Large Language Models for Bengali Math Word Problem Solving with Chain of Thought Reasoning](https://arxiv.org/abs/2505.21354) *— 2025 · arXiv:2505.21354*
9. [Logic-of-Thought: Injecting Logic into Contexts for Full Reasoning in Large Language Models](https://aclanthology.org/2025.naacl-long.510/) *— 2025 · Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
10. [MetaLadder: Ascending Mathematical Solution Quality via Analogical-Problem Reasoning Transfer](https://aclanthology.org/2025.findings-emnlp.232/) *— 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025*
11. [Metastable Dynamics of Chain-of-Thought Reasoning: Provable Benefits of Search, RL and Distillation](https://arxiv.org/abs/2502.01694) *— 2025 · arXiv:2502.01694*
12. [Mitigating Tail Narrowing in LLM Self-Improvement via Socratic-Guided Sampling](https://aclanthology.org/2025.naacl-long.533/) *— 2025 · Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
13. [Monitoring Reasoning Models for Misbehavior and the Risks of Promoting Obfuscation](https://arxiv.org/abs/2503.11926) *— 2025 · arXiv:2503.11926*
14. [Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention](https://arxiv.org/abs/2502.11089) *— 2025 · arXiv:2502.11089*
15. [NaturalReasoning: Reasoning in the Wild with 2.8M Challenging Questions](https://arxiv.org/abs/2502.13124) *— 2025 · arXiv:2502.13124*
16. [Offline Reinforcement Learning for LLM Multi-step Reasoning](https://aclanthology.org/2025.findings-acl.464/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
17. [On Memorization of Large Language Models in Logical Reasoning](https://aclanthology.org/2025.ijcnlp-long.148/) *— 2025 · Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics*
18. [Optima: Optimizing Effectiveness and Efficiency for LLM-Based Multi-Agent System](https://aclanthology.org/2025.findings-acl.601/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
19. [PAG: Multi-Turn Reinforced LLM Self-Correction with Policy as Generative Verifier](https://arxiv.org/abs/2506.10406) *— 2025 · arXiv:2506.10406*
20. [PORT: Preference Optimization on Reasoning Traces](https://aclanthology.org/2025.naacl-long.549/) *— 2025 · Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
21. [POSITION BIAS MITIGATES POSITION BIAS: Mitigate Position Bias Through Inter-Position Knowledge Distillation](https://aclanthology.org/2025.emnlp-main.78/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
22. [Physics Reasoner: Knowledge-Augmented Reasoning for Solving Physics Problems with Large Language Models](https://aclanthology.org/2025.coling-main.747/) *— 2025 · Proceedings of the 31st International Conference on Computational Linguistics*
23. [ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models](https://arxiv.org/abs/2505.24864) *— 2025 · arXiv:2505.24864*
24. [Progressive Multimodal Reasoning via Active Retrieval](https://aclanthology.org/2025.acl-long.180/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
25. [QCRD: Quality-guided Contrastive Rationale Distillation for Large Language Models](https://aclanthology.org/2025.emnlp-main.724/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
26. [RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning](https://arxiv.org/abs/2504.20073) *— 2025 · arXiv:2504.20073*
27. [RAcQUEt: Unveiling the Dangers of Overlooked Referential Ambiguity in Visual LLMs](https://aclanthology.org/2025.emnlp-main.1206/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
28. [ReSearch: Learning to Reason with Search for LLMs via Reinforcement Learning](https://arxiv.org/abs/2503.19470) *— 2025 · arXiv:2503.19470*
29. [ReSpAct: Harmonizing Reasoning, Speaking, and Acting Towards Building Large Language Model-Based Conversational AI Agents](https://aclanthology.org/2025.iwsds-1.7/) *— 2025 · Proceedings of the 15th International Workshop on Spoken Dialogue Systems Technology*
30. [Reasoning Aware Self-Consistency: Leveraging Reasoning Paths for Efficient LLM Sampling](https://aclanthology.org/2025.naacl-long.184/) *— 2025 · Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
31. [Reasoning Models Can Be Effective Without Thinking](https://arxiv.org/abs/2504.09858) *— 2025 · arXiv:2504.09858*
32. [ReflectEvo: Improving Meta Introspection of Small LLMs by Learning Self-Reflection](https://aclanthology.org/2025.findings-acl.871/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
33. [Reinforcement Learning for Reasoning in Small LLMs: What Works and What Doesn''t](https://arxiv.org/abs/2503.16219) *— 2025 · arXiv:2503.16219*
34. [Reinforcement Learning vs. Distillation: Understanding Accuracy and Capability in LLM Reasoning](https://arxiv.org/abs/2505.14216) *— 2025 · arXiv:2505.14216*
35. [Relevant or Random: Can LLMs Truly Perform Analogical Reasoning?](https://aclanthology.org/2025.findings-acl.1230/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
36. [Rethinking Fine-Tuning when Scaling Test-Time Compute: Limiting Confidence Improves Mathematical Reasoning](https://arxiv.org/abs/2502.07154) *— 2025 · arXiv:2502.07154*
37. [Reverse Thinking Makes LLMs Stronger Reasoners](https://aclanthology.org/2025.naacl-long.434/) *— 2025 · Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
38. [S$^2$-MAD: Breaking the Token Barrier to Enhance Multi-Agent Debate Efficiency](https://aclanthology.org/2025.naacl-long.475/) *— 2025 · Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
39. [SATBench: Benchmarking LLMs'' Logical Reasoning via Automated Puzzle Generation from SAT Formulas](https://aclanthology.org/2025.emnlp-main.1716/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
40. [SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training](https://arxiv.org/abs/2501.17161) *— 2025 · arXiv:2501.17161*
41. [SGIC: A Self-Guided Iterative Calibration Framework for RAG](https://aclanthology.org/2025.acl-long.1376/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
42. [STaR-SQL: Self-Taught Reasoner for Text-to-SQL](https://aclanthology.org/2025.acl-long.1187/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
43. [SWE-Fixer: Training Open-Source LLMs for Effective and Efficient GitHub Issue Resolution](https://aclanthology.org/2025.findings-acl.62/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
44. [SaRO: Enhancing LLM Safety through Reasoning-based Alignment](https://arxiv.org/abs/2504.09420) *— 2025 · arXiv:2504.09420*
45. [Safe: Enhancing Mathematical Reasoning in Large Language Models via Retrospective Step-aware Formal Verification](https://arxiv.org/abs/2506.04592) *— 2025 · arXiv:2506.04592*
46. [Satori: Reinforcement Learning with Chain-of-Action-Thought Enhances LLM Reasoning via Autoregressive Search](https://arxiv.org/abs/2502.02508) *— 2025 · arXiv:2502.02508*
47. [Scaling Test-Time Compute Without Verification or RL is Suboptimal](https://arxiv.org/abs/2502.12118) *— 2025 · arXiv:2502.12118*
48. [Self-Evolving Curriculum for LLM Reasoning](https://arxiv.org/abs/2505.14970) *— 2025 · arXiv:2505.14970*
49. [Self-Improving Transformers Overcome Easy-to-Hard and Length Generalization Challenges](https://arxiv.org/abs/2502.01612) *— 2025 · arXiv:2502.01612*
50. [Self-Taught Self-Correction for Small Language Models](https://arxiv.org/abs/2503.08681) *— 2025 · arXiv:2503.08681*
51. [Self-Training Elicits Concise Reasoning in Large Language Models](https://aclanthology.org/2025.findings-acl.1289/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
52. [Self-Training Large Language Models for Tool-Use Without Demonstrations](https://aclanthology.org/2025.findings-naacl.69/) *— 2025 · Findings of the Association for Computational Linguistics: NAACL 2025*
53. [Self-Training Large Language Models with Confident Reasoning](https://aclanthology.org/2025.findings-emnlp.806/) *— 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025*
54. [Self-Training Meets Consistency: Improving LLMs'' Reasoning with Consistency-Driven Rationale Evaluation](https://aclanthology.org/2025.naacl-long.528/) *— 2025 · Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
55. [Small Models Struggle to Learn from Strong Reasoners](https://aclanthology.org/2025.findings-acl.1301/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
56. [Step-KTO: Optimizing Mathematical Reasoning through Stepwise Binary Feedback](https://aclanthology.org/2025.mathnlp-main.2/) *— 2025 · Proceedings of The 3rd Workshop on Mathematical Natural Language Processing (MathNLP 2025)*
57. [Superficial Self-Improved Reasoners Benefit from Model Merging](https://aclanthology.org/2025.emnlp-main.301/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
58. [Syzygy of Thoughts: Improving LLM CoT with the Minimal Free Resolution](https://arxiv.org/abs/2504.09566) *— 2025 · arXiv:2504.09566*
59. [T1: Advancing Language Model Reasoning through Reinforcement Learning and Inference Scaling](https://arxiv.org/abs/2501.11651) *— 2025 · arXiv:2501.11651*
60. [TRACT: Regression-Aware Fine-tuning Meets Chain-of-Thought Reasoning for LLM-as-a-Judge](https://aclanthology.org/2025.acl-long.147/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
61. [Teach Small Models to Reason by Curriculum Distillation](https://aclanthology.org/2025.emnlp-main.376/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
62. [The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity](https://arxiv.org/abs/2506.06941) *— 2025 · arXiv:2506.06941*
63. [Theorem Prover as a Judge for Synthetic Data Generation](https://aclanthology.org/2025.acl-long.1448/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
64. [Theoretical Physics Benchmark (TPBench) -- a Dataset and Study of AI Reasoning Capabilities in Theoretical Physics](https://arxiv.org/abs/2502.15815) *— 2025 · arXiv:2502.15815*
65. [Think Clearly: Improving Reasoning via Redundant Token Pruning](https://aclanthology.org/2025.findings-emnlp.1169/) *— 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025*
66. [Thinking Machines: A Survey of LLM based Reasoning Strategies](https://arxiv.org/abs/2503.10814) *— 2025 · arXiv:2503.10814*
67. [Topology-of-Question-Decomposition: Enhancing Large Language Models with Information Retrieval for Knowledge-Intensive Tasks](https://aclanthology.org/2025.coling-main.191/) *— 2025 · Proceedings of the 31st International Conference on Computational Linguistics*
68. [Towards Medical Complex Reasoning with LLMs through Medical Verifiable Problems](https://aclanthology.org/2025.findings-acl.751/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
69. [Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models](https://arxiv.org/abs/2503.09567) *— 2025 · arXiv:2503.09567*
70. [Towards System 2 Reasoning in LLMs: Learning How to Think With Meta Chain-of-Thought](https://arxiv.org/abs/2501.04682) *— 2025 · arXiv:2501.04682*
71. [Towards Thinking-Optimal Scaling of Test-Time Compute for LLM Reasoning](https://arxiv.org/abs/2502.18080) *— 2025 · arXiv:2502.18080*
72. [Towards the Pedagogical Steering of Large Language Models for Tutoring: A Case Study with Modeling Productive Failure](https://aclanthology.org/2025.findings-acl.1348/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
73. [VLA-RL: Towards Masterful and General Robotic Manipulation with Scalable Reinforcement Learning](https://arxiv.org/abs/2505.18719) *— 2025 · arXiv:2505.18719*
74. [Vision-Language Models Can Self-Improve Reasoning via Reflection](https://aclanthology.org/2025.naacl-long.447/) *— 2025 · Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
75. [WebEvolver: Enhancing Web Agent Self-Improvement with Co-evolving World Model](https://aclanthology.org/2025.emnlp-main.454/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
76. [Zero-to-Strong Generalization: Eliciting Strong Capabilities of Large Language Models Iteratively without Gold Labels](https://aclanthology.org/2025.coling-main.251/) *— 2025 · Proceedings of the 31st International Conference on Computational Linguistics*
77. [s1: Simple test-time scaling](https://aclanthology.org/2025.emnlp-main.1025/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
78. [A Chain-of-Thought Is as Strong as Its Weakest Link: A Benchmark for Verifiers of Reasoning Chains](https://aclanthology.org/2024.acl-long.254/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
79. [A Closer Look at the Self-Verification Abilities of Large Language Models in Logical Reasoning](https://aclanthology.org/2024.naacl-long.52/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
80. [A Trusted Multi-View Evidential Fusion Framework for Commonsense Reasoning](https://aclanthology.org/2024.lrec-main.152/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
81. [Abstraction-of-Thought Makes Language Models Better Reasoners](https://aclanthology.org/2024.findings-emnlp.110/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
82. [Advancing Large Language Model Attribution through Self-Improving](https://aclanthology.org/2024.emnlp-main.223/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
83. [Advancing Process Verification for Large Language Models via Tree-Based Preference Learning](https://aclanthology.org/2024.emnlp-main.125/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
84. [Agent Q: Advanced Reasoning and Learning for Autonomous AI Agents](https://arxiv.org/abs/2408.07199) *— 2024 · arXiv:2408.07199*
85. [Agent-as-a-Judge: Evaluate Agents with Agents](https://arxiv.org/abs/2410.10934) *— 2024 · arXiv:2410.10934*
86. [AgentGym: Evolving Large Language Model-based Agents across Diverse Environments](https://arxiv.org/abs/2406.04151) *— 2024 · arXiv:2406.04151*
87. [Aligning Large and Small Language Models via Chain-of-Thought Reasoning](https://aclanthology.org/2024.eacl-long.109/) *— 2024 · Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)*
88. [AlphaVerus: Bootstrapping Formally Verified Code Generation through Self-Improving Translation and Treefinement](https://arxiv.org/abs/2412.06176) *— 2024 · arXiv:2412.06176*
89. [An Empirical Study of Data Ability Boundary in LLMs' Math Reasoning](https://arxiv.org/abs/2403.00799) *— 2024 · arXiv:2403.00799*
90. [An Empirical Study of Multilingual Reasoning Distillation for Question Answering](https://aclanthology.org/2024.emnlp-main.442/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
91. [AppWorld: A Controllable World of Apps and People for Benchmarking Interactive Coding Agents](https://aclanthology.org/2024.acl-long.850/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
92. [AutoAct: Automatic Agent Learning from Scratch for QA via Self-Planning](https://aclanthology.org/2024.acl-long.165/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
93. [Automated Design of Agentic Systems](https://arxiv.org/abs/2408.08435) *— 2024 · arXiv:2408.08435*
94. [Automatically Correcting Large Language Models: Surveying the Landscape of Diverse Automated Correction Strategies](https://aclanthology.org/2024.tacl-1.27/) *— 2024 · Transactions of the Association for Computational Linguistics*
95. [Benchmarking Automated Theorem Proving with Large Language Models](https://aclanthology.org/2024.nlp4science-1.18/) *— 2024 · Proceedings of the 1st Workshop on NLP for Science (NLP4Science)*
96. [Benchmarking GPT-4 on Algorithmic Problems: A Systematic Evaluation of Prompting Strategies](https://aclanthology.org/2024.lrec-main.195/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
97. [Breaking Language Barriers in Multilingual Mathematical Reasoning: Insights and Observations](https://aclanthology.org/2024.findings-emnlp.411/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
98. [CodeIt: Self-Improving Language Models with Prioritized Hindsight Replay](https://arxiv.org/abs/2402.04858) *— 2024 · arXiv:2402.04858*
99. [DARG: Dynamic Evaluation of Large Language Models via Adaptive Reasoning Graph](https://arxiv.org/abs/2406.17271) *— 2024 · arXiv:2406.17271*
100. [DART-Math: Difficulty-Aware Rejection Tuning for Mathematical Problem-Solving](https://arxiv.org/abs/2407.13690) *— 2024 · arXiv:2407.13690*

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