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# Who cites «STaR: Bootstrapping Reasoning With Reasoning»

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/)

Pages: **1** · [2](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/zelikman-2022-star-bootstrapping-reasoning-reasoning-2/) · [3](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/zelikman-2022-star-bootstrapping-reasoning-reasoning-3/) · [4](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/zelikman-2022-star-bootstrapping-reasoning-reasoning-4/) · [5](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/zelikman-2022-star-bootstrapping-reasoning-reasoning-5/) · [6](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/zelikman-2022-star-bootstrapping-reasoning-reasoning-6/)

1. [A Survey of Agentic Reasoning for Large Language Models: Towards Recursively Self-Improving and Collective Agents](https://arxiv.org/abs/2601.12538) *— 2026 · arXiv:2601.12538*
2. [A Survey of Reinforcement Learning for Large Language Models under Data Scarcity: Challenges and Solutions](https://aclanthology.org/2026.acl-long.1045/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
3. [AgentFactory: A Self-Evolving Framework Through Executable Subagent Accumulation and Reuse](https://aclanthology.org/2026.acl-demo.81/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)*
4. [Beyond GRPO and On-Policy Distillation: An Empirical Sparse-to-Dense Reward Principle for Language-Model Post-Training](https://arxiv.org/abs/2605.12483) *— 2026 · arXiv:2605.12483*
5. [Beyond MCQ: An Open-Ended Arabic Cultural QA Benchmark with Dialect Variants](https://aclanthology.org/2026.lrec-1.408/) *— 2026 · Proceedings of the Fifteenth Language Resources and Evaluation Conference*
6. [Beyond Outcome Verification: Verifiable Process Reward Models for Structured Reasoning](https://aclanthology.org/2026.findings-acl.1611/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
7. [CLARO: Controlled Attribute-Driven Reasoning Optimization for Efficient Chain-of-Thought](https://aclanthology.org/2026.findings-acl.1335/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
8. [Children's English Reading Story Generation via Supervised Fine-Tuning of Compact LLMs with Controllable Difficulty and Safety](https://md.mova.elamur.ai/free/en/docs/papers/shen-2026-children-s-english-reading-story/) *— our card · 2026 · Proceedings of the 21st Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2026)*
9. [Claim Verification in the Age of Large Language Models: A Survey](https://aclanthology.org/2026.acl-srw.2/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)*
10. [Crossing the Reward Bridge: Expanding Reinforcement Learning with Verifiable Rewards Across Diverse Domains](https://aclanthology.org/2026.acl-long.178/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
11. [DASH: Divergence-Adaptive Supervision Horizons for On-Policy Self-Distillation of Reasoning Models](https://arxiv.org/abs/2608.06243) *— 2026 · arXiv:2608.06243*
12. [Disagreement-Driven Joint Refinement of Retrieval and Decision Rules for Imbalanced Counseling Risk Classification](https://aclanthology.org/2026.clinicalnlp-1.32/) *— 2026 · Proceedings of the 8th Workshop on Clinical Natural Language Processing (Clinical NLP) @ LREC 2026*
13. [Discovering and Causally Validating Emotion-Sensitive Neurons in Large Audio-Language Models](https://aclanthology.org/2026.acl-long.687/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
14. [Dynamic Cheatsheet: Test-Time Learning with Adaptive Memory](https://aclanthology.org/2026.eacl-long.333/) *— 2026 · Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)*
15. [Embodied-Reasoner: Synergizing Visual Search, Reasoning, and Action for Embodied Interactive Tasks](https://aclanthology.org/2026.acl-long.1910/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
16. [Enhancing Auto-regressive Chain-of-Thought through Loop-Aligned Reasoning](https://aclanthology.org/2026.eacl-long.97/) *— 2026 · Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)*
17. [EpiCaR: Knowing What You Don''t Know Matters for Better Reasoning in LLMs](https://aclanthology.org/2026.acl-long.1026/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
18. [EvoScientist: Towards Multi-Agent Evolving AI Scientists for End-to-End Scientific Discovery](https://arxiv.org/abs/2603.08127) *— 2026 · arXiv:2603.08127*
19. [From Belief Entrenchment to Robust Reasoning in LLM Agents](https://aclanthology.org/2026.tacl-1.58/) *— 2026 · Transactions of the Association for Computational Linguistics*
20. [How Long Reasoning Chains Influence LLMs' Judgment of Answer Factuality](https://aclanthology.org/2026.acl-long.2082/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
21. [Knowing When to Abstain: Medical LLMs Under Clinical Uncertainty](https://aclanthology.org/2026.eacl-long.291/) *— 2026 · Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)*
22. [Learning from Mistakes: Negative Reasoning Samples Enhance Out-of-Domain Generalization](https://aclanthology.org/2026.acl-long.1370/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
23. [Long-Chain Reasoning Distillation via Adaptive Prefix Alignment](https://aclanthology.org/2026.acl-long.822/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
24. [NeuReasoner: Towards Explainable, Controllable, and Unified Reasoning via Mixture-of-Neurons](https://aclanthology.org/2026.acl-long.1033/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
25. [On-Policy Context Distillation for Language Models](https://arxiv.org/abs/2602.12275) *— 2026 · arXiv:2602.12275*
26. [Psyche-R1: Towards Reliable Psychological LLMs through Unified Empathy, Expertise, and Reasoning](https://aclanthology.org/2026.acl-long.1141/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
27. [ReasMark: A Robust Watermark for Attributing LLM Reasoning Under Knowledge Distillation Attacks](https://aclanthology.org/2026.acl-long.2185/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
28. [Reinforcement Learning via Self-Distillation](https://arxiv.org/abs/2601.20802) *— 2026 · arXiv:2601.20802*
29. [Routing with Generated Data: Annotation-Free LLM Skill Estimation and Expert Selection](https://aclanthology.org/2026.acl-long.1498/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
30. [SWE-Swiss: A Multi-Task Fine-Tuning and RL Recipe for High-Performance Issue Resolution](https://aclanthology.org/2026.findings-acl.790/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
31. [Self-Consistency from Only Two Samples: CoT–PoT Ensembling for Efficient LLM Reasoning](https://aclanthology.org/2026.findings-acl.1642/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
32. [Self-Correcting RAG: Enhancing Faithfulness via MMKP Context Selection and NLI-Guided MCTS](https://aclanthology.org/2026.findings-acl.1052/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
33. [Self-Distilled Reasoner: On-Policy Self-Distillation for Large Language Models](https://arxiv.org/abs/2601.18734) *— 2026 · arXiv:2601.18734*
34. [Skill-Conditioned Gated Self-Distillation for LLM Reasoning](https://arxiv.org/abs/2605.28791) *— 2026 · arXiv:2605.28791*
35. [Solve-Detect-Verify: Inference-Time Scaling with Flexible Generative Verifier](https://aclanthology.org/2026.acl-long.2190/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
36. [SpeakRL: Synergizing Reasoning, Speaking, and Acting in Language Models with Reinforcement Learning](https://aclanthology.org/2026.iwsds-1.32/) *— 2026 · Proceedings of the 16th International Workshop on Spoken Dialogue System Technology*
37. [SynthAgent: Adapting Web Agents with Synthetic Supervision](https://aclanthology.org/2026.acl-long.716/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
38. [TRAC: Teacher-Guided Token Reward with Adaptive Calibration for Robust Policy Optimization](https://aclanthology.org/2026.acl-long.2210/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
39. [TURTLEAI: Benchmarking Multimodal Models for Visual Programming in Turtle Graphics](https://aclanthology.org/2026.findings-acl.334/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
40. [The Retrieval Bottleneck: Scaling Laws for Reinforcement Learning in RAG](https://aclanthology.org/2026.acl-long.1478/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
41. [Thinking Beyond the Local: Multi-View Instructed Adaptive Reasoning in KG-Enhanced LLMs](https://aclanthology.org/2026.findings-eacl.325/) *— 2026 · Findings of the Association for Computational Linguistics: EACL 2026*
42. [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*
43. [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)*
44. [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*
45. [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)*
46. [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*
47. [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*
48. [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)*
49. [A Survey on Feedback-based Multi-step Reasoning for Large Language Models on Mathematics](https://arxiv.org/abs/2502.14333) *— 2025 · arXiv:2502.14333*
50. [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*
51. [A Theory of Learning with Autoregressive Chain of Thought](https://arxiv.org/abs/2503.07932) *— 2025 · arXiv:2503.07932*
52. [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*
53. [Absolute Zero: Reinforced Self-play Reasoning with Zero Data](https://arxiv.org/abs/2505.03335) *— 2025 · arXiv:2505.03335*
54. [AbstentionBench: Reasoning LLMs Fail on Unanswerable Questions](https://arxiv.org/abs/2506.09038) *— 2025 · arXiv:2506.09038*
55. [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)*
56. [Algorithm Discovery With LLMs: Evolutionary Search Meets Reinforcement Learning](https://arxiv.org/abs/2504.05108) *— 2025 · arXiv:2504.05108*
57. [Aligning LLMs with Individual Preferences via Interaction](https://aclanthology.org/2025.coling-main.511/) *— 2025 · Proceedings of the 31st International Conference on Computational Linguistics*
58. [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*
59. [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*
60. [Beyond Distillation: Pushing the Limits of Medical LLM Reasoning with Minimalist Rule-Based RL](https://arxiv.org/abs/2505.17952) *— 2025 · arXiv:2505.17952*
61. [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)*
62. [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*
63. [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*
64. [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)*
65. [Can 1B LLM Surpass 405B LLM? Rethinking Compute-Optimal Test-Time Scaling](https://arxiv.org/abs/2502.06703) *— 2025 · arXiv:2502.06703*
66. [Can Large Reasoning Models Self-Train?](https://arxiv.org/abs/2505.21444) *— 2025 · arXiv:2505.21444*
67. [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*
68. [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*
69. [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)*
70. [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)*
71. [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*
72. [Cognitive Behaviors that Enable Self-Improving Reasoners, or, Four Habits of Highly Effective STaRs](https://arxiv.org/abs/2503.01307) *— 2025 · arXiv:2503.01307*
73. [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*
74. [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)*
75. [Curriculum Reinforcement Learning from Easy to Hard Tasks Improves LLM Reasoning](https://arxiv.org/abs/2506.06632) *— 2025 · arXiv:2506.06632*
76. [DOGe: Defensive Output Generation for LLM Protection Against Knowledge Distillation](https://arxiv.org/abs/2505.19504) *— 2025 · arXiv:2505.19504*
77. [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)*
78. [DeepSeek: Paradigm Shifts and Technical Evolution in Large AI Models](https://arxiv.org/abs/2507.09955) *— 2025 · arXiv:2507.09955*
79. [Demystifying Long Chain-of-Thought Reasoning in LLMs](https://arxiv.org/abs/2502.03373) *— 2025 · arXiv:2502.03373*
80. [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*
81. [Echo Chamber: RL Post-training Amplifies Behaviors Learned in Pretraining](https://arxiv.org/abs/2504.07912) *— 2025 · arXiv:2504.07912*
82. [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*
83. [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*
84. [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*
85. [Falcon-H1: A Family of Hybrid-Head Language Models Redefining Efficiency and Performance](https://arxiv.org/abs/2507.22448) *— 2025 · arXiv:2507.22448*
86. [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)*
87. [Generalizing Verifiable Instruction Following](https://arxiv.org/abs/2507.02833) *— 2025 · arXiv:2507.02833*
88. [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)*
89. [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)*
90. [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*
91. [Harnessing the Reasoning Economy: A Survey of Efficient Reasoning for Large Language Models](https://arxiv.org/abs/2503.24377) *— 2025 · arXiv:2503.24377*
92. [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*
93. [How Far Are AI Scientists from Changing the World?](https://arxiv.org/abs/2507.23276) *— 2025 · arXiv:2507.23276*
94. [How Is LLM Reasoning Distracted by Irrelevant Context? An Analysis Using a Controlled Benchmark](https://md.mova.elamur.ai/free/en/docs/papers/yang-2025-llm-reasoning-distracted-irrelevant-context/) *— our card · 2025 · arXiv:2505.18761*
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. [Kimi K2: Open Agentic Intelligence](https://arxiv.org/abs/2507.20534) *— 2025 · arXiv:2507.20534*
100. [LEMMA: Learning from Errors for MatheMatical Advancement in LLMs](https://aclanthology.org/2025.findings-acl.605/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*

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