Papers with transfer reasoning

2 papers
Enhancing Long-Chain Reasoning Distillation through Error-Aware Self-Reflection (2026.findings-acl)

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Challenge: Existing studies treat SLMs as student models and use long-form Chains-of-Thought (CoTs) as supervision signals for Supervised Fine-Tuning (SFT). Existing research focuses on distilling reasoning ability from LLMs to enhance the mathematical reasoning performance of small-scale models.
Approach: They propose a framework that refines teacher CoTs through an error-aware reflection process to enable the student model to construct more tailored teacher Cots.
Outcome: Experiments on multiple mathematical reasoning benchmarks show that ORION improves performance by more than 2% over all baselines.
Breaking the Language Barrier: Improving Cross-Lingual Reasoning with Structured Self-Attention (2023.findings-emnlp)

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Challenge: Recent studies show that multilingual language models (MultiLMs) are capable of logically reasoning over natural language statements, reasoning with their implicit knowledge, and performing multi-step reasoning when the model size is large enough.
Approach: They propose a mechanism that encourages cross-lingual attention in code-switched sequences and improves reasoning performance by up to 14%.
Outcome: The proposed approach improves reasoning performance by 14% and 4% on the RuleTaker and LeapOfThought datasets.

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