Explain in Your Own Words: Improving Reasoning via Token-Selective Dual Knowledge Distillation
#language models #reasoning #knowledge distillation #token selection #explanation generation
📌 Key Takeaways
- Researchers propose a new method called 'Explain in Your Own Words' (EIYOW) to enhance reasoning in language models.
- The approach uses token-selective dual knowledge distillation to improve model performance on reasoning tasks.
- It focuses on selecting important tokens and distilling knowledge from both teacher and student models.
- The method aims to make models better at generating explanations and reasoning steps independently.
📖 Full Retelling
arXiv:2603.13260v1 Announce Type: cross
Abstract: Knowledge Distillation (KD) can transfer the reasoning abilities of large models to smaller ones, which can reduce the costs to generate Chain-of-Thoughts for reasoning tasks. KD methods typically ask the student to mimic the teacher's distribution over the entire output. However, a student with limited capacity can be overwhelmed by such extensive supervision causing a distribution mismatch, especially in complex reasoning tasks. We propose Tok
🏷️ Themes
AI Reasoning, Knowledge Distillation
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Original Source
arXiv:2603.13260v1 Announce Type: cross
Abstract: Knowledge Distillation (KD) can transfer the reasoning abilities of large models to smaller ones, which can reduce the costs to generate Chain-of-Thoughts for reasoning tasks. KD methods typically ask the student to mimic the teacher's distribution over the entire output. However, a student with limited capacity can be overwhelmed by such extensive supervision causing a distribution mismatch, especially in complex reasoning tasks. We propose Tok
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