| Challenge: | Existing methods for generating high-quality CoT data rely on costly human annotations and error-prone CoT. |
| Approach: | They propose a method that extracts verifiable, step-by-step reasoning traces from code execution and transforms them into a natural language CoT reasoning. |
| Outcome: | The proposed method produces highly accurate reasoning data and reduces overall token length during inference by reducing meaningless repetition and overthinking. |
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Shailja Thakur, Vaibhav Saxena, Rohan Kulkarni, Shivdeep Singh, Parameswaran Selvam, Hiroshi Kanayama, Hima Patel
| Challenge: | Current synthetic Chain-of-Thought (CoT) training data often consists of plausible-sounding explanations generated by teacher models, not verifiable accounts of actual program behavior. |
| Approach: | They propose to ground CoT generation directly in program execution traces to improve reasoning capabilities. |
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Chain-of-Thought Matters: Improving Long-Context Language Models with Reasoning Path Supervision (2025.findings-emnlp)
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Dawei Zhu, Xiyu Wei, Guangxiang Zhao, Wenhao Wu, Haosheng Zou, Junfeng Ran, null XWang, Lin Sun, Xiangzheng Zhang, Sujian Li
| Challenge: | Recent advances in Large Language Models (LLMs) have highlighted the challenge of handling long-context tasks. |
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Reasoning with OmniThought: A Large CoT Dataset with Verbosity and Cognitive Difficulty Annotations (2026.acl-long)
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| Challenge: | Existing resources often fail to provide extensive reasoning problems with coherent CoT processes distilled from multiple teacher models. |
| Approach: | They propose a large-scale dataset featuring 2 million CoT processes generated by multiple powerful LRMs. |
| Outcome: | The proposed dataset features 2 million CoT processes and is validated by multiple powerful LRMs. |
Improving Large Language Models Function Calling and Interpretability via Guided-Structured Templates (2025.emnlp-main)
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Hy Dang, Tianyi Liu, Zhuofeng Wu, Jingfeng Yang, Haoming Jiang, Tao Yang, Pei Chen, Zhengyang Wang, Helen Wang, Huasheng Li, Bing Yin, Meng Jiang
| Challenge: | Large language models (LLMs) have strong reasoning and tool-use capabilities, yet fail in real-world tool-interactions due to incorrect parameterization, poor tool selection, or misinterpretation of user intent. |
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One Missing Piece for Open-Source Reasoning Models: A Dataset to Mitigate Cold-Starting Short CoT LLMs in RL (2025.acl-industry)
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Hyungjoo Chae, Dongjin Kang, Jihyuk Kim, Beong-woo Kwak, Sunghyun Park, Haeju Park, Jinyoung Yeo, Moontae Lee, Kyungjae Lee
| Challenge: | Existing large reasoning models are limited by their closed nature and high API costs and safety issues. |
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Self-Training Elicits Concise Reasoning in Large Language Models (2025.findings-acl)
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| Challenge: | Chain-of-thought reasoning has enabled large language models to use additional computation through intermediate tokens to solve complex tasks, but current models often generate more tokens than necessary to accomplish the task, incurring extraneous inference costs. |
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Let’s Be Self-generated via Step by Step: A Curriculum Learning Approach to Automated Reasoning with Large Language Models (2025.findings-acl)
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| Challenge: | Existing efforts to improve CoT prompting have limitations that require extensive human effort or performance needs to be improved. |
| Approach: | They propose a prompt approach for automatic reasoning called LBS3 inspired by curriculum learning which better reflects human learning habits. |
| Outcome: | The proposed approach achieves strongly competitive performance compared to baselines in reasoning-intensive tasks with varying open- and closed-source LLMs. |
ReEfBench: Quantifying the Reasoning Efficiency of LLMs (2026.acl-long)
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| Challenge: | Existing methods for Chain-of-Thought evaluations do not distinguish between genuine reasoning and mere verbosity. |
| Approach: | They propose a framework for the non-intrusive, comprehensive process-centric evaluation of reasoning grounded in First-Order Logic. |
| Outcome: | The proposed framework identifies four distinct behavioral prototypes and diagnoses the failure modes. |
CAC-CoT: Connector-Aware Compact Chain-of-Thought for Efficient Reasoning Data Synthesis Across Dual-System Cognitive Tasks (2025.findings-emnlp)
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| Challenge: | Long chain-of-thought (CoT) prompting often slows or even degrades performance on fast, intuitive "System-1" tasks. |
| Approach: | They introduce a method that deliberately restricts reasoning to a small, fixed set of connector phrases, steering the model toward concise and well-structured explanations. |
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ChainLM: Empowering Large Language Models with Improved Chain-of-Thought Prompting (2024.lrec-main)
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| Challenge: | Existing CoT synthesis approaches focus on simpler reasoning tasks and result in inconsistent CoT prompts. |
| Approach: | They propose a framework for automatic generation of superior CoT prompts based on three major evolution strategies . they propose 'step-level debating' method where multiple debaters discuss each reasoning step to arrive at the correct answer. |
| Outcome: | The proposed framework can generate superior CoT prompts from a CoT dataset. |