Improve Student’s Reasoning Generalizability through Cascading Decomposed CoTs Distillation (2024.emnlp-main)
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| Challenge: | Existing studies have tried to distill these capabilities into smaller language models (SLMs) however, these capabilities are often associated with more parameters, which is not practical to emergent in smaller models. |
| Approach: | They propose to decompose the traditional single-step learning process into two cascaded learning steps by restructuring the training objectives and concatenating the question with the rationale as input. |
| Outcome: | Extensive experiments show that the proposed method improves reasoning generalizability and diversity of the model. |
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| Challenge: | Existing distillation methods for Large Language Models (LLMs) focus on fine-tuning student SLMs on correct data, resulting in students struggling to learn the key instead of analyzing mistakes according to correct solutions. |
| Approach: | They propose a method that exposes key reasoning steps rather than simple fine-tuning students' CoTs data by using a set of prompts with similar reasoning paths but divergent conclusions. |
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Distilling Reasoning Capabilities into Smaller Language Models (2023.findings-acl)
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| Challenge: | a step-by-step reasoning approach like chain of thought has proved to be effective in eliciting reasoning abilities in large language models. |
| Approach: | They propose a knowledge distillation approach that leverages CoT reasoning capabilities of larger models and distills them into smaller models. |
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MoDE-CoTD: Chain-of-Thought Distillation for Complex Reasoning Tasks with Mixture of Decoupled LoRA-Experts (2024.lrec-main)
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| Challenge: | Current Chain-of-thought Distillation methods hinder CoT reasoning performance . student models are separately distilled from specific reasoning tasks . parameter update of student models severely harms CoT ability on unseen reasoning tasks. |
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Mixed Distillation Helps Smaller Language Models Reason Better (2024.findings-emnlp)
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| Challenge: | Recent large language models (LLMs) have demonstrated impressive multiple step-by-step reasoning capabilities in recent NLP reasoning tasks. |
| Approach: | They propose a mixed distillation framework that distills multiple step-by-step reasoning abilities into smaller language models (SLMs) they leverage LLMs to generate multiple step by step reasoning rationales by sampling automatically. |
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| Challenge: | Existing solutions to this problem are inference-time pruning and tuning-based distillation. |
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Pru-CoT: Towards Efficient Reasoning Distillation via Pruning Chain-of-Thought (2026.findings-acl)
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| Challenge: | Existing heuristics fail to capture global causal logic due to rigid rules and limited search spaces. |
| Approach: | They propose a framework that extracts the essential logical structure from reasoning chains. |
| Outcome: | Experiments show that Pru-CoT models generate more compact reasoning paths compared to models trained on verbose data. |
Small Language Models Fine-tuned to Coordinate Larger Language Models improve Complex Reasoning (2023.emnlp-main)
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| Challenge: | Recent attempts at prompt decomposition toward solving complex, multi-step reasoning problems depend on the ability of the LLM to simultaneously decompose and solve the problem. |
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Distilling Long-CoT Reasoning through Collaborative Step-wise Multi-Teacher Decoding (2026.findings-acl)
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| Challenge: | Existing curation-based approaches to inference are inefficient and fail to adapt dynamically, leading to redundant sampling and missed opportunities for complementary reasoning. |
| Approach: | They propose a collaborative multi-teacher decoding framework that performs step-wise reasoning synthesis guided by predictive perplexity–based scoring and beam search. |
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Unveiling the Key Factors for Distilling Chain-of-Thought Reasoning (2025.findings-acl)
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Xinghao Chen, Zhijing Sun, Guo Wenjin, Miaoran Zhang, Yanjun Chen, Yirong Sun, Hui Su, Yijie Pan, Dietrich Klakow, Wenjie Li, Xiaoyu Shen
| Challenge: | Large Language Models (LLMs) excel in reasoning tasks through Chain-of-Thought prompting. |
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| Outcome: | The proposed model is based on four teacher models and seven student models across seven mathematical and commonsense reasoning datasets. |
Towards Efficient CoT Distillation: Self-Guided Rationale Selector for Better Performance with Fewer Rationales (2025.findings-emnlp)
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| Challenge: | Existing work on rationale quality underestimates the importance of CoT distillation, focusing primarily on data quantity, which may result in transferring noisy or incorrect information to the student model. |
| Approach: | They propose a method which can discern and select high quality rationales for distillation and a Rationale Difficulty metric to measure the ability of the student model to generate the correct answer under a given rationale. |
| Outcome: | The proposed method achieves 4.6% accuracy improvement over baseline data on seven datasets over three tasks, controlling accuracy, diversity, and difficulty. |