---
title: "Who cites STaR: Bootstrapping Reasoning With Reasoning —…"
description: "The papers of the Elamur Research library that cite «STaR: Bootstrapping Reasoning With Reasoning», found in their bibliographies."
canonical: "https://research.elamur.ai/free/en/papers/zelikman-2022-star-bootstrapping-reasoning-reasoning/cited-by/page/2/"
---
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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.research.elamur.ai/free/en/papers/zelikman-2022-star-bootstrapping-reasoning-reasoning/)

Pages: [1](https://md.research.elamur.ai/free/en/papers/zelikman-2022-star-bootstrapping-reasoning-reasoning/cited-by/) · **2** · [3](https://md.research.elamur.ai/free/en/papers/zelikman-2022-star-bootstrapping-reasoning-reasoning/cited-by/page/3/) · … · [6](https://md.research.elamur.ai/free/en/papers/zelikman-2022-star-bootstrapping-reasoning-reasoning/cited-by/page/6/)

1. [Thinking Economically: A Hierarchical Framework for Adaptive-Complexity Reasoning in LLMs](https://aclanthology.org/2026.findings-acl.1965/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
2. [Thinking Twice Makes Large Language Models Safer and More Helpful](https://aclanthology.org/2026.findings-acl.1812/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
3. [Training Specialist Models without Reasoning Trajectories for Domain Expert Distillation](https://arxiv.org/abs/2609.13770) *— 2026 · arXiv:2609.13770*
4. [Trajectory Learnability for Offline On-Policy Distillation with Imperfect Teachers](https://arxiv.org/abs/2609.18321) *— 2026 · arXiv:2609.18321*
5. [VERIFY-RL: Verifiable Recursive Decomposition for Reinforcement Learning in Mathematical Reasoning](https://arxiv.org/abs/2602.07559) *— 2026 · arXiv:2602.07559*
6. [VISTA: Verifier-Informed Student-to-Teacher Adaptation for On-Policy Self-Distillation](https://arxiv.org/abs/2608.28306) *— 2026 · arXiv:2608.28306*
7. [WIST: Web-Grounded Iterative Self-Play Tree for Domain-Targeted Reasoning Improvement](https://aclanthology.org/2026.acl-long.1456/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
8. [Weak-to-Strong Elicitation via Mismatched Wrong Drafts](https://arxiv.org/abs/2605.17314) *— 2026 · arXiv:2605.17314*
9. [When Internalization Fails: Finding Better Targets for Reasoning Compression](https://aclanthology.org/2026.findings-acl.734/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
10. [Which Reasoning Trajectories Teach Students to Reason Better? A Simple Metric of Informative Alignment](https://aclanthology.org/2026.acl-long.1950/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
11. [dOPSD: On-Policy Self-Distillation for Diffusion Language Models](https://arxiv.org/abs/2607.04428) *— 2026 · arXiv:2607.04428*
12. [A Model Can Help Itself: Reward-Free Self-Training for LLM Reasoning](https://arxiv.org/abs/2510.18814) *— 2025 · arXiv:2510.18814*
13. [A Survey of Frontiers in LLM Reasoning: Inference Scaling, Learning to Reason, and Agentic Systems](https://arxiv.org/abs/2504.09037) *— 2025 · arXiv:2504.09037*
14. [A Survey of Post-Training Scaling in Large Language Models](https://aclanthology.org/2025.acl-long.140/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
15. [A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence](https://arxiv.org/abs/2507.21046) *— 2025 · arXiv:2507.21046*
16. [A Survey of Slow Thinking-based Reasoning LLMs using Reinforced Learning and Inference-time Scaling Law](https://arxiv.org/abs/2505.02665) *— 2025 · arXiv:2505.02665*
17. [A Survey on Efficient Large Language Model Training: From Data-centric Perspectives](https://aclanthology.org/2025.acl-long.1493/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
18. [A Survey on Feedback-based Multi-step Reasoning for Large Language Models on Mathematics](https://arxiv.org/abs/2502.14333) *— 2025 · arXiv:2502.14333*
19. [A Survey on Test-Time Scaling in Large Language Models: What, How, Where, and How Well?](https://arxiv.org/abs/2503.24235) *— 2025 · arXiv:2503.24235*
20. [A Theory of Learning with Autoregressive Chain of Thought](https://arxiv.org/abs/2503.07932) *— 2025 · arXiv:2503.07932*
21. [AQuilt: Weaving Logic and Self-Inspection into Low-Cost, High-Relevance Data Synthesis for Specialist LLMs](https://aclanthology.org/2025.emnlp-main.293/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
22. [Absolute Zero: Reinforced Self-play Reasoning with Zero Data](https://arxiv.org/abs/2505.03335) *— 2025 · arXiv:2505.03335*
23. [AbstentionBench: Reasoning LLMs Fail on Unanswerable Questions](https://arxiv.org/abs/2506.09038) *— 2025 · arXiv:2506.09038*
24. [AgentGym: Evaluating and Training Large Language Model-based Agents across Diverse Environments](https://aclanthology.org/2025.acl-long.1355/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
25. [Agentic-R1: Distilled Dual-Strategy Reasoning](https://aclanthology.org/2025.emnlp-main.604/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
26. [Algorithm Discovery With LLMs: Evolutionary Search Meets Reinforcement Learning](https://arxiv.org/abs/2504.05108) *— 2025 · arXiv:2504.05108*
27. [Aligning LLMs with Individual Preferences via Interaction](https://aclanthology.org/2025.coling-main.511/) *— 2025 · Proceedings of the 31st International Conference on Computational Linguistics*
28. [AlphaOne: Reasoning Models Thinking Slow and Fast at Test Time](https://aclanthology.org/2025.emnlp-main.570/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
29. [Automatic Generation of Inference Making Questions for Reading Comprehension Assessments](https://aclanthology.org/2025.bea-1.31/) *— 2025 · Proceedings of the 20th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2025)*
30. [Benchmarking Uncertainty Metrics for LLM Target-Aware Search](https://aclanthology.org/2025.findings-emnlp.226/) *— 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025*
31. [Better Process Supervision with Bi-directional Rewarding Signals](https://aclanthology.org/2025.findings-acl.747/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
32. [Beyond Distillation: Pushing the Limits of Medical LLM Reasoning with Minimalist Rule-Based RL](https://arxiv.org/abs/2505.17952) *— 2025 · arXiv:2505.17952*
33. [Beyond Instruction Following: Evaluating Inferential Rule Following of Large Language Models](https://aclanthology.org/2025.ccl-1.79/) *— 2025 · Proceedings of the 24th China National Conference on Computational Linguistics (CCL 2025)*
34. [Beyond Markovian: Reflective Exploration via Bayes-Adaptive RL for LLM Reasoning](https://arxiv.org/abs/2505.20561) *— 2025 · arXiv:2505.20561*
35. [Breaking the Reasoning Barrier A Survey on LLM Complex Reasoning through the Lens of Self-Evolution](https://aclanthology.org/2025.findings-acl.386/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
36. [Bridging the Creativity Understanding Gap: Small-Scale Human Alignment Enables Expert-Level Humor Ranking in LLMs](https://aclanthology.org/2025.findings-emnlp.884/) *— 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025*
37. [CARE-STaR: Constraint-aware Self-taught Reasoner](https://aclanthology.org/2025.findings-acl.1116/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
38. [CER: Confidence Enhanced Reasoning in LLMs](https://aclanthology.org/2025.acl-long.390/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
39. [Can 1B LLM Surpass 405B LLM? Rethinking Compute-Optimal Test-Time Scaling](https://arxiv.org/abs/2502.06703) *— 2025 · arXiv:2502.06703*
40. [Can LLMs Deceive CLIP? Benchmarking Adversarial Compositionality of Pre-trained Multimodal Representation via Text Updates](https://aclanthology.org/2025.acl-long.1280/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
41. [Can Large Language Models Invent Algorithms to Improve Themselves?: Algorithm Discovery for Recursive Self-Improvement through Reinforcement Learning](https://aclanthology.org/2025.naacl-long.519/) *— 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)*
42. [Can Large Reasoning Models Self-Train?](https://arxiv.org/abs/2505.21444) *— 2025 · arXiv:2505.21444*
43. [Can MLLMs Absorb Math Reasoning Abilities from LLMs as Free Lunch?](https://arxiv.org/abs/2510.14387) *— 2025 · arXiv:2510.14387*
44. [Capture the Key in Reasoning to Enhance CoT Distillation Generalization](https://aclanthology.org/2025.acl-long.21/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
45. [Chain of Methodologies: Scaling Test Time Computation without Training](https://aclanthology.org/2025.findings-acl.276/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
46. [Chain-of-Discussion: A Multi-Model Framework for Complex Evidence-Based Question Answering](https://aclanthology.org/2025.coling-main.734/) *— 2025 · Proceedings of the 31st International Conference on Computational Linguistics*
47. [Chain-of-Thought Matters: Improving Long-Context Language Models with Reasoning Path Supervision](https://aclanthology.org/2025.findings-emnlp.170/) *— 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025*
48. [CoE: A Clue of Emotion Framework for Emotion Recognition in Conversations](https://aclanthology.org/2025.acl-long.1148/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
49. [CoRe-MMRAG: Cross-Source Knowledge Reconciliation for Multimodal RAG](https://aclanthology.org/2025.acl-long.1583/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
50. [CoTD-PO: Chain-of-Thought Distillation with Preference Optimization](https://aclanthology.org/2025.findings-emnlp.1087/) *— 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025*
51. [Code Execution as Grounded Supervision for LLM Reasoning](https://aclanthology.org/2025.emnlp-main.1260/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
52. [Cognitive Behaviors that Enable Self-Improving Reasoners, or, Four Habits of Highly Effective STaRs](https://arxiv.org/abs/2503.01307) *— 2025 · arXiv:2503.01307*
53. [Concise and Organized Perception Facilitates Reasoning in Large Language Models](https://aclanthology.org/2025.findings-naacl.193/) *— 2025 · Findings of the Association for Computational Linguistics: NAACL 2025*
54. [Creative Planning with Language Models: Practice, Evaluation and Applications](https://aclanthology.org/2025.naacl-tutorial.1/) *— 2025 · Proceedings of the 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 5: Tutorial Abstracts)*
55. [Critic-CoT: Boosting the Reasoning Abilities of Large Language Model via Chain-of-Thought Critic](https://aclanthology.org/2025.findings-acl.89/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
56. [Curriculum Reinforcement Learning from Easy to Hard Tasks Improves LLM Reasoning](https://arxiv.org/abs/2506.06632) *— 2025 · arXiv:2506.06632*
57. [DICE: Structured Reasoning in LLMs through SLM-Guided Chain-of-Thought Correction](https://aclanthology.org/2025.emnlp-main.355/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
58. [DOGe: Defensive Output Generation for LLM Protection Against Knowledge Distillation](https://arxiv.org/abs/2505.19504) *— 2025 · arXiv:2505.19504*
59. [DP-Ens DurationQA: Dual-Prompt Fine-Tuning with Log-Probability Ensemble for Duration Question Answering](https://aclanthology.org/2025.vlsp-1.42/) *— 2025 · Proceedings of the 11th International Workshop on Vietnamese Language and Speech Processing*
60. [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://arxiv.org/abs/2501.12948) *— 2025 · arXiv:2501.12948 · Nature volume 645, pages 633-638 (2025)*
61. [DeepSeek: Paradigm Shifts and Technical Evolution in Large AI Models](https://arxiv.org/abs/2507.09955) *— 2025 · arXiv:2507.09955*
62. [Demystifying Long Chain-of-Thought Reasoning in LLMs](https://arxiv.org/abs/2502.03373) *— 2025 · arXiv:2502.03373*
63. [Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?](https://arxiv.org/abs/2504.13837) *— 2025 · arXiv:2504.13837 · NeurIPS 2025 Oral; ICML 2025 AI4MATH workshop best paper*
64. [Dynamic Risk Assessments for Offensive Cybersecurity Agents](https://arxiv.org/abs/2505.18384) *— 2025 · arXiv:2505.18384*
65. [Echo Chamber: RL Post-training Amplifies Behaviors Learned in Pretraining](https://arxiv.org/abs/2504.07912) *— 2025 · arXiv:2504.07912*
66. [Empowering Persuasion Detection in Slavic Texts through Two-Stage Generative Reasoning](https://aclanthology.org/2025.bsnlp-1.20/) *— 2025 · Proceedings of the 10th Workshop on Slavic Natural Language Processing (Slavic NLP 2025)*
67. [Enhanced Data Synthesis for LLM through Reasoning Structures Generated by Hierarchical GFlowNet](https://aclanthology.org/2025.findings-acl.821/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
68. [Enhancing LLM-based Hatred and Toxicity Detection with Meta-Toxic Knowledge Graph](https://aclanthology.org/2025.findings-acl.1270/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
69. [Enhancing Logical Reasoning in Language Models via Symbolically-Guided Monte Carlo Process Supervision](https://aclanthology.org/2025.emnlp-main.1624/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
70. [Evaluating Language Model Reasoning about Confidential Information](https://arxiv.org/abs/2508.19980) *— 2025 · arXiv:2508.19980*
71. [Evaluating Test-Time Scaling LLMs for Legal Reasoning: OpenAI o1, DeepSeek-R1, and Beyond](https://aclanthology.org/2025.findings-emnlp.742/) *— 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025*
72. [ExeSQL: Self-Taught Text-to-SQL Models with Execution-Driven Bootstrapping for SQL Dialects](https://aclanthology.org/2025.findings-emnlp.1320/) *— 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025*
73. [Facilitating Long Context Understanding via Supervised Chain-of-Thought Reasoning](https://aclanthology.org/2025.emnlp-main.615/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
74. [FairCoT: Enhancing Fairness in Text-to-Image Generation via Chain of Thought Reasoning with Multimodal Large Language Models](https://aclanthology.org/2025.findings-emnlp.42/) *— 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025*
75. [FaithfulPersona: Balancing Faithfulness and Personalization in Code Explanations through Self-Critique](https://aclanthology.org/2025.findings-naacl.53/) *— 2025 · Findings of the Association for Computational Linguistics: NAACL 2025*
76. [Falcon-H1: A Family of Hybrid-Head Language Models Redefining Efficiency and Performance](https://arxiv.org/abs/2507.22448) *— 2025 · arXiv:2507.22448*
77. [Fine-Tuned Thoughts: Leveraging Chain-of-Thought Reasoning for Industrial Asset Health Monitoring](https://aclanthology.org/2025.findings-emnlp.1126/) *— 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025*
78. [FineReason: Evaluating and Improving LLMs” Deliberate Reasoning through Reflective Puzzle Solving](https://aclanthology.org/2025.acl-long.333/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
79. [From Long to Short: LLMs Excel at Trimming Own Reasoning Chains](https://arxiv.org/abs/2509.06174) *— 2025 · arXiv:2509.06174*
80. [Future Policy Approximation for Offline Reinforcement Learning in LLM Reasoning](https://arxiv.org/abs/2509.19893) *— 2025 · arXiv:2509.19893*
81. [GenTool: Enhancing Tool Generalization in Language Models through Zero-to-One and Weak-to-Strong Simulation](https://aclanthology.org/2025.findings-acl.61/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
82. [Generalizing Verifiable Instruction Following](https://arxiv.org/abs/2507.02833) *— 2025 · arXiv:2507.02833*
83. [Generating Complex Question Decompositions in the Face of Distribution Shifts](https://aclanthology.org/2025.naacl-long.55/) *— 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)*
84. [Generative Reward Modeling via Synthetic Criteria Preference Learning](https://aclanthology.org/2025.acl-long.1297/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
85. [Genius: A Generalizable and Purely Unsupervised Self-Training Framework For Advanced Reasoning](https://aclanthology.org/2025.acl-long.644/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
86. [HARP: Hesitation-Aware Reframing in Transformer Inference Pass](https://aclanthology.org/2025.naacl-long.612/) *— 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)*
87. [HS-STaR: Hierarchical Sampling for Self-Taught Reasoners via Difficulty Estimation and Budget Reallocation](https://aclanthology.org/2025.emnlp-main.282/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
88. [Harnessing the Reasoning Economy: A Survey of Efficient Reasoning for Large Language Models](https://arxiv.org/abs/2503.24377) *— 2025 · arXiv:2503.24377*
89. [How Ambiguous Are the Rationales for Natural Language Reasoning? A Simple Approach to Handling Rationale Uncertainty](https://aclanthology.org/2025.coling-main.671/) *— 2025 · Proceedings of the 31st International Conference on Computational Linguistics*
90. [How Far Are AI Scientists from Changing the World?](https://arxiv.org/abs/2507.23276) *— 2025 · arXiv:2507.23276*
91. [How Is LLM Reasoning Distracted by Irrelevant Context? An Analysis Using a Controlled Benchmark](https://md.research.elamur.ai/free/en/papers/yang-2025-llm-reasoning-distracted-irrelevant-context/) *— our card · 2025 · arXiv:2505.18761*
92. [How Much Backtracking is Enough? Exploring the Interplay of SFT and RL in Enhancing LLM Reasoning](https://arxiv.org/abs/2505.24273) *— 2025 · arXiv:2505.24273*
93. [Idola Tribus of AI: Large Language Models tend to perceive order where none exists](https://aclanthology.org/2025.findings-emnlp.681/) *— 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025*
94. [In-Token Rationality Optimization: Towards Accurate and Concise LLM Reasoning via Self-Feedback](https://arxiv.org/abs/2511.09865) *— 2025 · arXiv:2511.09865*
95. [Inference-Time Scaling for Complex Tasks: Where We Stand and What Lies Ahead](https://arxiv.org/abs/2504.00294) *— 2025 · arXiv:2504.00294*
96. [Interactive Evolution: A Neural-Symbolic Self-Training Framework For Large Language Models](https://aclanthology.org/2025.acl-long.635/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
97. [InternLM-XComposer2.5-Reward: A Simple Yet Effective Multi-Modal Reward Model](https://aclanthology.org/2025.findings-acl.340/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
98. [Investigating the Shortcomings of LLMs in Step-by-Step Legal Reasoning](https://aclanthology.org/2025.findings-naacl.435/) *— 2025 · Findings of the Association for Computational Linguistics: NAACL 2025*
99. [Is the Top Still Spinning? Evaluating Subjectivity in Narrative Understanding](https://aclanthology.org/2025.emnlp-main.10/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
100. [Keep Guessing? When Considering Inference Scaling, Mind the Baselines](https://aclanthology.org/2025.findings-naacl.332/) *— 2025 · Findings of the Association for Computational Linguistics: NAACL 2025*

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