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# Who cites «STaR: Bootstrapping Reasoning With Reasoning» · page 3

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](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/zelikman-2022-star-bootstrapping-reasoning-reasoning/) · [2](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/zelikman-2022-star-bootstrapping-reasoning-reasoning-2/) · **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. [DataComp-LM: In search of the next generation of training sets for language models](https://arxiv.org/abs/2406.11794) *— 2024 · arXiv:2406.11794*
2. [Dialogue Ontology Relation Extraction via Constrained Chain-of-Thought Decoding](https://aclanthology.org/2024.sigdial-1.33/) *— 2024 · Proceedings of the 25th Annual Meeting of the Special Interest Group on Discourse and Dialogue*
3. [Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs](https://arxiv.org/abs/2412.21187) *— 2024 · arXiv:2412.21187*
4. [Dualformer: Controllable Fast and Slow Thinking by Learning with Randomized Reasoning Traces](https://arxiv.org/abs/2410.09918) *— 2024 · arXiv:2410.09918*
5. [DynaThink: Fast or Slow? A Dynamic Decision-Making Framework for Large Language Models](https://aclanthology.org/2024.emnlp-main.814/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
6. [ELAD: Explanation-Guided Large Language Models Active Distillation](https://aclanthology.org/2024.findings-acl.264/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
7. [Easy-to-Hard Generalization: Scalable Alignment Beyond Human Supervision](https://arxiv.org/abs/2403.09472) *— 2024 · arXiv:2403.09472*
8. [EchoPrompt: Instructing the Model to Rephrase Queries for Improved In-context Learning](https://aclanthology.org/2024.naacl-short.35/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)*
9. [Enhancing Chain-of-Thoughts Prompting with Iterative Bootstrapping in Large Language Models](https://aclanthology.org/2024.findings-naacl.257/) *— 2024 · Findings of the Association for Computational Linguistics: NAACL 2024*
10. [FOLIO: Natural Language Reasoning with First-Order Logic](https://aclanthology.org/2024.emnlp-main.1229/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
11. [Feedback Loops With Language Models Drive In-Context Reward Hacking](https://arxiv.org/abs/2402.06627) *— 2024 · arXiv:2402.06627*
12. [Few-Shot Data Synthesis for Open Domain Multi-Hop Question Answering](https://aclanthology.org/2024.eacl-long.12/) *— 2024 · Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)*
13. [Fine-Tuning and Prompt Optimization: Two Great Steps that Work Better Together](https://aclanthology.org/2024.emnlp-main.597/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
14. [From $r$ to $Q^*$: Your Language Model is Secretly a Q-Function](https://arxiv.org/abs/2404.12358) *— 2024 · arXiv:2404.12358*
15. [GameBench: Evaluating Strategic Reasoning Abilities of LLM Agents](https://arxiv.org/abs/2406.06613) *— 2024 · arXiv:2406.06613*
16. [Generating Mental Health Transcripts with SAPE (Spanish Adaptive Prompt Engineering)](https://aclanthology.org/2024.naacl-long.285/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
17. [Generative Reward Models](https://arxiv.org/abs/2410.12832) *— 2024 · arXiv:2410.12832*
18. [Generative Verifiers: Reward Modeling as Next-Token Prediction](https://arxiv.org/abs/2408.15240) *— 2024 · arXiv:2408.15240*
19. [Grokked Transformers are Implicit Reasoners: A Mechanistic Journey to the Edge of Generalization](https://arxiv.org/abs/2405.15071) *— 2024 · arXiv:2405.15071*
20. [HuatuoGPT-o1, Towards Medical Complex Reasoning with LLMs](https://arxiv.org/abs/2412.18925) *— 2024 · arXiv:2412.18925*
21. [I Learn Better If You Speak My Language: Understanding the Superior Performance of Fine-Tuning Large Language Models with LLM-Generated Responses](https://aclanthology.org/2024.emnlp-main.571/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
22. [Imitate, Explore, and Self-Improve: A Reproduction Report on Slow-thinking Reasoning Systems](https://arxiv.org/abs/2412.09413) *— 2024 · arXiv:2412.09413*
23. [Impossible Distillation for Paraphrasing and Summarization: How to Make High-quality Lemonade out of Small, Low-quality Model](https://aclanthology.org/2024.naacl-long.250/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
24. [Improving Language Model Reasoning with Self-motivated Learning](https://aclanthology.org/2024.lrec-main.774/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
25. [Improving Large Language Models in Event Relation Logical Prediction](https://aclanthology.org/2024.acl-long.512/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
26. [In-Context Learning with Iterative Demonstration Selection](https://aclanthology.org/2024.findings-emnlp.438/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
27. [In-Context Principle Learning from Mistakes](https://arxiv.org/abs/2402.05403) *— 2024 · arXiv:2402.05403*
28. [InternLM-Math: Open Math Large Language Models Toward Verifiable Reasoning](https://arxiv.org/abs/2402.06332) *— 2024 · arXiv:2402.06332*
29. [Interpreting and Improving Large Language Models in Arithmetic Calculation](https://arxiv.org/abs/2409.01659) *— 2024 · arXiv:2409.01659*
30. [Iterative Reasoning Preference Optimization](https://arxiv.org/abs/2404.19733) *— 2024 · arXiv:2404.19733*
31. [LLM2LLM: Boosting LLMs with Novel Iterative Data Enhancement](https://aclanthology.org/2024.findings-acl.388/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
32. [LLMs in the Imaginarium: Tool Learning through Simulated Trial and Error](https://aclanthology.org/2024.acl-long.570/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
33. [LMEME at SemEval-2024 Task 4: Teacher Student Fusion - Integrating CLIP with LLMs for Enhanced Persuasion Detection](https://aclanthology.org/2024.semeval-1.92/) *— 2024 · Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)*
34. [Lean-STaR: Learning to Interleave Thinking and Proving](https://arxiv.org/abs/2407.10040) *— 2024 · arXiv:2407.10040*
35. [Learning Formal Mathematics From Intrinsic Motivation](https://arxiv.org/abs/2407.00695) *— 2024 · arXiv:2407.00695*
36. [Learning to Ask Informative Questions: Enhancing LLMs with Preference Optimization and Expected Information Gain](https://aclanthology.org/2024.findings-emnlp.291/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
37. [Learning to Correct for QA Reasoning with Black-box LLMs](https://aclanthology.org/2024.emnlp-main.504/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
38. [Leveraging Machine-Generated Rationales to Facilitate Social Meaning Detection in Conversations](https://aclanthology.org/2024.acl-long.373/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
39. [M$^3$CoT: A Novel Benchmark for Multi-Domain Multi-step Multi-modal Chain-of-Thought](https://aclanthology.org/2024.acl-long.446/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
40. [Masked Thought: Simply Masking Partial Reasoning Steps Can Improve Mathematical Reasoning Learning of Language Models](https://aclanthology.org/2024.acl-long.320/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
41. [Mastering Board Games by External and Internal Planning with Language Models](https://arxiv.org/abs/2412.12119) *— 2024 · arXiv:2412.12119*
42. [Meta-Prompting: Enhancing Language Models with Task-Agnostic Scaffolding](https://arxiv.org/abs/2401.12954) *— 2024 · arXiv:2401.12954*
43. [Mirror: Multiple-perspective Self-Reflection Method for Knowledge-rich Reasoning](https://aclanthology.org/2024.acl-long.382/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
44. [MoDE-CoTD: Chain-of-Thought Distillation for Complex Reasoning Tasks with Mixture of Decoupled LoRA-Experts](https://aclanthology.org/2024.lrec-main.1003/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
45. [Monte Carlo Tree Search Boosts Reasoning via Iterative Preference Learning](https://arxiv.org/abs/2405.00451) *— 2024 · arXiv:2405.00451*
46. [MuggleMath: Assessing the Impact of Query and Response Augmentation on Math Reasoning](https://aclanthology.org/2024.acl-long.551/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
47. [NExT: Teaching Large Language Models to Reason about Code Execution](https://arxiv.org/abs/2404.14662) *— 2024 · arXiv:2404.14662*
48. [O1 Replication Journey -- Part 2: Surpassing O1-preview through Simple Distillation, Big Progress or Bitter Lesson?](https://arxiv.org/abs/2411.16489) *— 2024 · arXiv:2411.16489*
49. [O1 Replication Journey: A Strategic Progress Report -- Part 1](https://arxiv.org/abs/2410.18982) *— 2024 · arXiv:2410.18982*
50. [On the Diversity of Synthetic Data and its Impact on Training Large Language Models](https://arxiv.org/abs/2410.15226) *— 2024 · arXiv:2410.15226*
51. [OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset](https://arxiv.org/abs/2402.10176) *— 2024 · arXiv:2402.10176*
52. [OpenR: An Open Source Framework for Advanced Reasoning with Large Language Models](https://arxiv.org/abs/2410.09671) *— 2024 · arXiv:2410.09671*
53. [P-FOLIO: Evaluating and Improving Logical Reasoning with Abundant Human-Written Reasoning Chains](https://aclanthology.org/2024.findings-emnlp.966/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
54. [Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Security](https://arxiv.org/abs/2401.05459) *— 2024 · arXiv:2401.05459*
55. [Planning In Natural Language Improves LLM Search For Code Generation](https://arxiv.org/abs/2409.03733) *— 2024 · arXiv:2409.03733*
56. [Preference Optimization for Reasoning with Pseudo Feedback](https://arxiv.org/abs/2411.16345) *— 2024 · arXiv:2411.16345*
57. [Progress or Regress? Self-Improvement Reversal in Post-training](https://arxiv.org/abs/2407.05013) *— 2024 · arXiv:2407.05013*
58. [Prompt Engineering a Prompt Engineer](https://aclanthology.org/2024.findings-acl.21/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
59. [Quiet-STaR: Language models can teach themselves to think before speaking](https://arxiv.org/abs/2403.09629) *— 2024*
60. [RAG-HAT: A Hallucination-Aware Tuning Pipeline for LLM in Retrieval-Augmented Generation](https://aclanthology.org/2024.emnlp-industry.113/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track*
61. [RAT: Retrieval Augmented Thoughts Elicit Context-Aware Reasoning in Long-Horizon Generation](https://arxiv.org/abs/2403.05313) *— 2024 · arXiv:2403.05313*
62. [RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold](https://arxiv.org/abs/2406.14532) *— 2024 · arXiv:2406.14532*
63. [RankPrompt: Step-by-Step Comparisons Make Language Models Better Reasoners](https://aclanthology.org/2024.lrec-main.1183/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
64. [Re-ReST: Reflection-Reinforced Self-Training for Language Agents](https://aclanthology.org/2024.emnlp-main.861/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
65. [ReConcile: Round-Table Conference Improves Reasoning via Consensus among Diverse LLMs](https://aclanthology.org/2024.acl-long.381/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
66. [ReST-MCTS*: LLM Self-Training via Process Reward Guided Tree Search](https://arxiv.org/abs/2406.03816) *— 2024 · arXiv:2406.03816*
67. [Recursive Introspection: Teaching Language Model Agents How to Self-Improve](https://arxiv.org/abs/2407.18219) *— 2024 · arXiv:2407.18219*
68. [Rethinking the Bounds of LLM Reasoning: Are Multi-Agent Discussions the Key?](https://arxiv.org/abs/2402.18272) *— 2024 · arXiv:2402.18272*
69. [Rewarding Progress: Scaling Automated Process Verifiers for LLM Reasoning](https://arxiv.org/abs/2410.08146) *— 2024 · arXiv:2410.08146*
70. [SIaM: Self-Improving Code-Assisted Mathematical Reasoning of Large Language Models](https://arxiv.org/abs/2408.15565) *— 2024 · arXiv:2408.15565*
71. [STaR-GATE: Teaching Language Models to Ask Clarifying Questions](https://arxiv.org/abs/2403.19154) *— 2024 · arXiv:2403.19154*
72. [Scalable Reinforcement Post-Training Beyond Static Human Prompts: Evolving Alignment via Asymmetric Self-Play](https://arxiv.org/abs/2411.00062) *— 2024 · arXiv:2411.00062*
73. [Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters](https://arxiv.org/abs/2408.03314) *— 2024 · arXiv:2408.03314*
74. [Scaling of Search and Learning: A Roadmap to Reproduce o1 from Reinforcement Learning Perspective](https://arxiv.org/abs/2412.14135) *— 2024 · arXiv:2412.14135*
75. [SeRTS: Self-Rewarding Tree Search for Biomedical Retrieval-Augmented Generation](https://aclanthology.org/2024.findings-emnlp.71/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
76. [Search, Verify and Feedback: Towards Next Generation Post-training Paradigm of Foundation Models via Verifier Engineering](https://arxiv.org/abs/2411.11504) *— 2024 · arXiv:2411.11504*
77. [Self-Data Distillation for Recovering Quality in Pruned Large Language Models](https://arxiv.org/abs/2410.09982) *— 2024 · arXiv:2410.09982*
78. [Self-Explore: Enhancing Mathematical Reasoning in Language Models with Fine-grained Rewards](https://aclanthology.org/2024.findings-emnlp.78/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
79. [Self-Improvement in Language Models: The Sharpening Mechanism](https://arxiv.org/abs/2412.01951) *— 2024 · arXiv:2412.01951*
80. [Self-Refine Instruction-Tuning for Aligning Reasoning in Language Models](https://aclanthology.org/2024.emnlp-main.139/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
81. [Self-Supervised Alignment with Mutual Information: Learning to Follow Principles without Preference Labels](https://arxiv.org/abs/2404.14313) *— 2024 · arXiv:2404.14313*
82. [Self-Training with Direct Preference Optimization Improves Chain-of-Thought Reasoning](https://aclanthology.org/2024.acl-long.643/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
83. [Small Language Models Need Strong Verifiers to Self-Correct Reasoning](https://aclanthology.org/2024.findings-acl.924/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
84. [Smaller, Weaker, Yet Better: Training LLM Reasoners via Compute-Optimal Sampling](https://arxiv.org/abs/2408.16737) *— 2024 · arXiv:2408.16737*
85. [SocREval: Large Language Models with the Socratic Method for Reference-free Reasoning Evaluation](https://aclanthology.org/2024.findings-naacl.175/) *— 2024 · Findings of the Association for Computational Linguistics: NAACL 2024*
86. [StateFlow: Enhancing LLM Task-Solving through State-Driven Workflows](https://arxiv.org/abs/2403.11322) *— 2024 · arXiv:2403.11322*
87. [Stream of Search (SoS): Learning to Search in Language](https://arxiv.org/abs/2404.03683) *— 2024 · arXiv:2404.03683*
88. [TangoFlux: Super Fast and Faithful Text to Audio Generation with Flow Matching and Clap-Ranked Preference Optimization](https://arxiv.org/abs/2412.21037) *— 2024 · arXiv:2412.21037*
89. [Teaching Large Language Models to Reason with Reinforcement Learning](https://arxiv.org/abs/2403.04642) *— 2024 · arXiv:2403.04642*
90. [TextGrad: Automatic "Differentiation" via Text](https://arxiv.org/abs/2406.07496) *— 2024 · arXiv:2406.07496*
91. [The Llama 3 Herd of Models](https://arxiv.org/abs/2407.21783) *— 2024 · arXiv:2407.21783*
92. [The pitfalls of next-token prediction](https://arxiv.org/abs/2403.06963) *— 2024 · arXiv:2403.06963*
93. [Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizing](https://arxiv.org/abs/2404.12253) *— 2024 · arXiv:2404.12253*
94. [Training Language Models to Self-Correct via Reinforcement Learning](https://arxiv.org/abs/2409.12917) *— 2024 · arXiv:2409.12917*
95. [Tulu 3: Pushing Frontiers in Open Language Model Post-Training](https://arxiv.org/abs/2411.15124) *— 2024 · arXiv:2411.15124*
96. [V-STaR: Training Verifiers for Self-Taught Reasoners](https://arxiv.org/abs/2402.06457) *— 2024 · arXiv:2402.06457*
97. [Weak-to-Strong Reasoning](https://aclanthology.org/2024.findings-emnlp.490/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
98. [Weak-to-Strong Search: Align Large Language Models via Searching over Small Language Models](https://arxiv.org/abs/2405.19262) *— 2024 · arXiv:2405.19262*
99. [When Can LLMs Actually Correct Their Own Mistakes? A Critical Survey of Self-Correction of LLMs](https://aclanthology.org/2024.tacl-1.78/) *— 2024 · Transactions of the Association for Computational Linguistics*
100. [mCoT: Multilingual Instruction Tuning for Reasoning Consistency in Language Models](https://aclanthology.org/2024.acl-long.649/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*

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