| Challenge: | Recent work on self-instruction tuning has focused on enhancing the general proficiency of models. |
| Approach: | They propose a new instruction-tuning dataset for Logical Chain-of-Thought reasoning with GPT-4 that harvests instructions for prompting GPT to generate chain-of thought rationales. |
| Outcome: | The proposed dataset enables the model to generate chain-of-thought rationales with GPT-4. |
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CoMAT: Chain of Mathematically Annotated Thought Improves Mathematical Reasoning (2025.emnlp-main)
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| Challenge: | Mathematical reasoning remains a significant challenge for large language models (LLMs), despite advances in prompting techniques such as Chain-of-Thought (CoT). |
| Approach: | They propose a framework that enhances reasoning through two stages: Symbolic Conversion and Reasoning Execution. |
| Outcome: | The proposed framework outperforms traditional CoT on six out of seven benchmarks across four LLMs. |
ReFT: Reasoning with Reinforced Fine-Tuning (2024.acl-long)
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| Challenge: | Existing approaches to improve the generalization of large language models are using Supervised Fine-Tuning (SFT) this approach does not show sufficient generalization ability because it only relies on the given CoT data. |
| Approach: | They propose to use Chain-of-Thought annotations to train Large Language Models using supervised fine-tuning to improve generalization. |
| Outcome: | The proposed approach outperforms SFT on GSM8K, MathQA, and SVAMP datasets and shows a superior generalization ability. |
Fine-Tuning on Diverse Reasoning Chains Drives Within-Inference CoT Refinement in LLMs (2025.acl-long)
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| Challenge: | Existing approaches to generate multiple independent CoTs, combining them through ensembling or other post-hoc strategies, have been shown to be effective in boosting performance. |
| Approach: | They propose a method where LLMs are fine-tuned to generate a sequence of Diverse Chains of Thought (DCoT) within a single inference step. |
| Outcome: | The proposed model can generate multiple chains of thought within a single inference step without external feedback. |
Symbolic Chain-of-Thought Distillation: Small Models Can Also “Think” Step-by-Step (2023.acl-long)
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| Challenge: | Symbolic Chain-of-thought Distillation (SCoTD) is a method to train a smaller student model on rationalizations sampled from a significantly larger teacher model. |
| Approach: | They propose a method to train a smaller student model on rationalizations from a larger teacher model. |
| Outcome: | The proposed method improves the performance of a student model in supervised and few-shot settings and especially for challenge sets. |
Enhancing Chain-of-Thought Reasoning with Critical Representation Fine-tuning (2025.acl-long)
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Chenxi Huang, Shaotian Yan, Liang Xie, Binbin Lin, Sinan Fan, Yue Xin, Deng Cai, Chen Shen, Jieping Ye
| Challenge: | Representation Fine-tuning (ReFT) is a proposed method for improving parameter efficiency . however, it yields suboptimal performance, as fixed-position representations have uncertain impact on outputs . |
| Approach: | They propose a method that fine-tunes critical representations in a low-rank linear subspace while freezing the base model. |
| Outcome: | The proposed method improves accuracy of LLaMA-2-7B and ReFT by 18.2 and 3.8 on GSM8K. |
Neural Chain-of-Thought Search: Searching the Optimal Reasoning Path to Enhance Large Language Models (2026.findings-acl)
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| Challenge: | Recent research indicates that Large Reasoning Models suffer from a strategic bottleneck at reasoning path planning. |
| Approach: | They propose a framework that reformulates reasoning as a dynamic search for the optimal thinking strategy. |
| Outcome: | The proposed framework improves accuracy and computational cost while reducing generation length by over 22%. |
Chain-of-Thought Tuning: Masked Language Models can also Think Step By Step in Natural Language Understanding (2023.emnlp-main)
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| Challenge: | Chain-of-Thought (CoT) is a technique that guides large language models to decompose complex tasks into multi-step reasoning processes. |
| Approach: | They propose a two-step reasoning framework based on prompt tuning to implement step-by-step thinking for MLMs on NLU tasks. |
| Outcome: | The proposed framework outperforms baselines and achieves state-of-the-art performance on two NLU tasks. |
Improve Vision Language Model Chain-of-thought Reasoning (2025.acl-long)
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Ruohong Zhang, Bowen Zhang, Yanghao Li, Haotian Zhang, Zhiqing Sun, Zhe Gan, Yinfei Yang, Ruoming Pang, Yiming Yang
| Challenge: | Current training recipes often rely on datasets dominated by short annotations with limited rationales, hindering the models' ability to generalize to tasks requiring comprehensive reasoning. |
| Approach: | They propose a two-stage post-training strategy that augments short answers with CoT reasoning generated by GPT-4o, enhancing the VLM's CoT capabilities through fine-tuning. |
| Outcome: | The proposed strategy enhances the model's CoT capabilities through fine-tuning and reinforcement learning. |
Ada-Instruct: Adapting Instruction Generators for Complex Reasoning (2024.findings-emnlp)
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| Challenge: | Existing methods for generating instructions from a few initial samples with in-context learning are lacking in generating complex instructions of length 100. |
| Approach: | They propose an adaptive instruction generator developed through fine-tuning that generates long, intricate, and distributionally consistent instructions. |
| Outcome: | The proposed method generates long, intricate, and distributionally consistent instructions with ten examples. |
Structured Chain-of-Thought Prompting for Few-Shot Generation of Content-Grounded QA Conversations (2024.findings-emnlp)
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| Challenge: | Structured chain-of-thought (SCoT) prompting is used to generate content-grounded multi-turn questions and answers with a large language model. |
| Approach: | They propose a structured chain-of-thought prompting approach to generating content-grounded multi-turn question-answer conversations with a pre-trained large language model. |
| Outcome: | The proposed approach increases agent faithfulness to grounding documents by 16.8% when used as training data. |