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

LLM-Friendly Knowledge Representation for Customer Support — 2025 · Proceedings of the 31st International Conference on Computational Linguistics: Industry Track
LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters! — 2025 · arXiv:2502.07374
Language Models Can Easily Learn to Reason from Demonstrations — 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025
Language models can learn implicit multi-hop reasoning, but only if they have lots of training data — 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
Learning from Committee: Reasoning Distillation from a Mixture of Teachers with Peer-Review — 2025 · Findings of the Association for Computational Linguistics: ACL 2025
Learning to Reason via Self-Iterative Process Feedback for Small Language Models — 2025 · Proceedings of the 31st International Conference on Computational Linguistics
Learning to Reason without External Rewards — 2025 · arXiv:2505.19590
Leveraging Large Language Models for Bengali Math Word Problem Solving with Chain of Thought Reasoning — 2025 · arXiv:2505.21354
Logic-of-Thought: Injecting Logic into Contexts for Full Reasoning in Large Language Models — 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)
MetaLadder: Ascending Mathematical Solution Quality via Analogical-Problem Reasoning Transfer — 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025
Metastable Dynamics of Chain-of-Thought Reasoning: Provable Benefits of Search, RL and Distillation — 2025 · arXiv:2502.01694
Mitigating Tail Narrowing in LLM Self-Improvement via Socratic-Guided Sampling — 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)
Monitoring Reasoning Models for Misbehavior and the Risks of Promoting Obfuscation — 2025 · arXiv:2503.11926
Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention — 2025 · arXiv:2502.11089
NaturalReasoning: Reasoning in the Wild with 2.8M Challenging Questions — 2025 · arXiv:2502.13124
Offline Reinforcement Learning for LLM Multi-step Reasoning — 2025 · Findings of the Association for Computational Linguistics: ACL 2025
On Memorization of Large Language Models in Logical Reasoning — 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
Optima: Optimizing Effectiveness and Efficiency for LLM-Based Multi-Agent System — 2025 · Findings of the Association for Computational Linguistics: ACL 2025
PAG: Multi-Turn Reinforced LLM Self-Correction with Policy as Generative Verifier — 2025 · arXiv:2506.10406
PORT: Preference Optimization on Reasoning Traces — 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)
POSITION BIAS MITIGATES POSITION BIAS: Mitigate Position Bias Through Inter-Position Knowledge Distillation — 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
Physics Reasoner: Knowledge-Augmented Reasoning for Solving Physics Problems with Large Language Models — 2025 · Proceedings of the 31st International Conference on Computational Linguistics
ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models — 2025 · arXiv:2505.24864
Progressive Multimodal Reasoning via Active Retrieval — 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
QCRD: Quality-guided Contrastive Rationale Distillation for Large Language Models — 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning — 2025 · arXiv:2504.20073
RAcQUEt: Unveiling the Dangers of Overlooked Referential Ambiguity in Visual LLMs — 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
ReSearch: Learning to Reason with Search for LLMs via Reinforcement Learning — 2025 · arXiv:2503.19470
ReSpAct: Harmonizing Reasoning, Speaking, and Acting Towards Building Large Language Model-Based Conversational AI Agents — 2025 · Proceedings of the 15th International Workshop on Spoken Dialogue Systems Technology
Reasoning Aware Self-Consistency: Leveraging Reasoning Paths for Efficient LLM Sampling — 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)
Reasoning Models Can Be Effective Without Thinking — 2025 · arXiv:2504.09858
ReflectEvo: Improving Meta Introspection of Small LLMs by Learning Self-Reflection — 2025 · Findings of the Association for Computational Linguistics: ACL 2025
Reinforcement Learning for Reasoning in Small LLMs: What Works and What Doesn''t — 2025 · arXiv:2503.16219
Reinforcement Learning vs. Distillation: Understanding Accuracy and Capability in LLM Reasoning — 2025 · arXiv:2505.14216
Relevant or Random: Can LLMs Truly Perform Analogical Reasoning? — 2025 · Findings of the Association for Computational Linguistics: ACL 2025
Rethinking Fine-Tuning when Scaling Test-Time Compute: Limiting Confidence Improves Mathematical Reasoning — 2025 · arXiv:2502.07154
Reverse Thinking Makes LLMs Stronger Reasoners — 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)
S$^2$-MAD: Breaking the Token Barrier to Enhance Multi-Agent Debate Efficiency — 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)
SATBench: Benchmarking LLMs'' Logical Reasoning via Automated Puzzle Generation from SAT Formulas — 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training — 2025 · arXiv:2501.17161
SGIC: A Self-Guided Iterative Calibration Framework for RAG — 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
STaR-SQL: Self-Taught Reasoner for Text-to-SQL — 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
SWE-Fixer: Training Open-Source LLMs for Effective and Efficient GitHub Issue Resolution — 2025 · Findings of the Association for Computational Linguistics: ACL 2025
SaRO: Enhancing LLM Safety through Reasoning-based Alignment — 2025 · arXiv:2504.09420
Safe: Enhancing Mathematical Reasoning in Large Language Models via Retrospective Step-aware Formal Verification — 2025 · arXiv:2506.04592
Satori: Reinforcement Learning with Chain-of-Action-Thought Enhances LLM Reasoning via Autoregressive Search — 2025 · arXiv:2502.02508
Scaling Test-Time Compute Without Verification or RL is Suboptimal — 2025 · arXiv:2502.12118
Self-Evolving Curriculum for LLM Reasoning — 2025 · arXiv:2505.14970
Self-Improving Transformers Overcome Easy-to-Hard and Length Generalization Challenges — 2025 · arXiv:2502.01612
Self-Taught Self-Correction for Small Language Models — 2025 · arXiv:2503.08681
Self-Training Elicits Concise Reasoning in Large Language Models — 2025 · Findings of the Association for Computational Linguistics: ACL 2025
Self-Training Large Language Models for Tool-Use Without Demonstrations — 2025 · Findings of the Association for Computational Linguistics: NAACL 2025
Self-Training Large Language Models with Confident Reasoning — 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025
Self-Training Meets Consistency: Improving LLMs'' Reasoning with Consistency-Driven Rationale Evaluation — 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)
Small Models Struggle to Learn from Strong Reasoners — 2025 · Findings of the Association for Computational Linguistics: ACL 2025
Step-KTO: Optimizing Mathematical Reasoning through Stepwise Binary Feedback — 2025 · Proceedings of The 3rd Workshop on Mathematical Natural Language Processing (MathNLP 2025)
Superficial Self-Improved Reasoners Benefit from Model Merging — 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
Syzygy of Thoughts: Improving LLM CoT with the Minimal Free Resolution — 2025 · arXiv:2504.09566
T1: Advancing Language Model Reasoning through Reinforcement Learning and Inference Scaling — 2025 · arXiv:2501.11651
TRACT: Regression-Aware Fine-tuning Meets Chain-of-Thought Reasoning for LLM-as-a-Judge — 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Teach Small Models to Reason by Curriculum Distillation — 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity — 2025 · arXiv:2506.06941
Theorem Prover as a Judge for Synthetic Data Generation — 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Theoretical Physics Benchmark (TPBench) -- a Dataset and Study of AI Reasoning Capabilities in Theoretical Physics — 2025 · arXiv:2502.15815
Think Clearly: Improving Reasoning via Redundant Token Pruning — 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025
Thinking Machines: A Survey of LLM based Reasoning Strategies — 2025 · arXiv:2503.10814
Topology-of-Question-Decomposition: Enhancing Large Language Models with Information Retrieval for Knowledge-Intensive Tasks — 2025 · Proceedings of the 31st International Conference on Computational Linguistics
Towards Medical Complex Reasoning with LLMs through Medical Verifiable Problems — 2025 · Findings of the Association for Computational Linguistics: ACL 2025
Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models — 2025 · arXiv:2503.09567
Towards System 2 Reasoning in LLMs: Learning How to Think With Meta Chain-of-Thought — 2025 · arXiv:2501.04682
Towards Thinking-Optimal Scaling of Test-Time Compute for LLM Reasoning — 2025 · arXiv:2502.18080
Towards the Pedagogical Steering of Large Language Models for Tutoring: A Case Study with Modeling Productive Failure — 2025 · Findings of the Association for Computational Linguistics: ACL 2025
VLA-RL: Towards Masterful and General Robotic Manipulation with Scalable Reinforcement Learning — 2025 · arXiv:2505.18719
Vision-Language Models Can Self-Improve Reasoning via Reflection — 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)
WebEvolver: Enhancing Web Agent Self-Improvement with Co-evolving World Model — 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
Zero-to-Strong Generalization: Eliciting Strong Capabilities of Large Language Models Iteratively without Gold Labels — 2025 · Proceedings of the 31st International Conference on Computational Linguistics
s1: Simple test-time scaling — 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
A Chain-of-Thought Is as Strong as Its Weakest Link: A Benchmark for Verifiers of Reasoning Chains — 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
A Closer Look at the Self-Verification Abilities of Large Language Models in Logical Reasoning — 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
A Trusted Multi-View Evidential Fusion Framework for Commonsense Reasoning — 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Abstraction-of-Thought Makes Language Models Better Reasoners — 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024
Advancing Large Language Model Attribution through Self-Improving — 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
Advancing Process Verification for Large Language Models via Tree-Based Preference Learning — 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
Agent Q: Advanced Reasoning and Learning for Autonomous AI Agents — 2024 · arXiv:2408.07199
Agent-as-a-Judge: Evaluate Agents with Agents — 2024 · arXiv:2410.10934
AgentGym: Evolving Large Language Model-based Agents across Diverse Environments — 2024 · arXiv:2406.04151
Aligning Large and Small Language Models via Chain-of-Thought Reasoning — 2024 · Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
AlphaVerus: Bootstrapping Formally Verified Code Generation through Self-Improving Translation and Treefinement — 2024 · arXiv:2412.06176
An Empirical Study of Data Ability Boundary in LLMs' Math Reasoning — 2024 · arXiv:2403.00799
An Empirical Study of Multilingual Reasoning Distillation for Question Answering — 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
AppWorld: A Controllable World of Apps and People for Benchmarking Interactive Coding Agents — 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
AutoAct: Automatic Agent Learning from Scratch for QA via Self-Planning — 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Automated Design of Agentic Systems — 2024 · arXiv:2408.08435
Automatically Correcting Large Language Models: Surveying the Landscape of Diverse Automated Correction Strategies — 2024 · Transactions of the Association for Computational Linguistics
Benchmarking Automated Theorem Proving with Large Language Models — 2024 · Proceedings of the 1st Workshop on NLP for Science (NLP4Science)
Benchmarking GPT-4 on Algorithmic Problems: A Systematic Evaluation of Prompting Strategies — 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Breaking Language Barriers in Multilingual Mathematical Reasoning: Insights and Observations — 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024
CodeIt: Self-Improving Language Models with Prioritized Hindsight Replay — 2024 · arXiv:2402.04858
DARG: Dynamic Evaluation of Large Language Models via Adaptive Reasoning Graph — 2024 · arXiv:2406.17271
DART-Math: Difficulty-Aware Rejection Tuning for Mathematical Problem-Solving — 2024 · arXiv:2407.13690

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