Distilling Long-CoT Reasoning through Collaborative Step-wise Multi-Teacher Decoding
📰 ArXiv cs.AI
Learn how to distill large reasoning models using collaborative step-wise multi-teacher decoding for efficient Long-CoT reasoning
Action Steps
- Implement CoRD, a collaborative multi-teacher decoding framework
- Train multiple teacher models with diverse reasoning traces
- Apply step-wise decoding to distill large reasoning models
- Evaluate the performance of the distilled models using metrics such as accuracy and computational efficiency
- Compare the results with existing curation-based approaches to identify improvements
Who Needs to Know This
AI researchers and engineers working on large-scale reasoning models can benefit from this approach to improve efficiency and reduce computational costs
Key Insight
💡 Collaborative step-wise multi-teacher decoding can improve the efficiency of Long-CoT reasoning by reducing redundant sampling and leveraging complementary reasoning
Share This
🤖 Distill large reasoning models efficiently with CoRD, a collaborative multi-teacher decoding framework! 🚀
Key Takeaways
Learn how to distill large reasoning models using collaborative step-wise multi-teacher decoding for efficient Long-CoT reasoning
Full Article
Title: Distilling Long-CoT Reasoning through Collaborative Step-wise Multi-Teacher Decoding
Abstract:
arXiv:2605.02290v1 Announce Type: new Abstract: Distilling large reasoning models is essential for making Long-CoT reasoning practical, as full-scale inference remains computationally prohibitive. Existing curation-based approaches select complete reasoning traces post-hoc, overlooking collaboration among heterogeneous teachers and lacking dynamic exploration, which leads to redundant sampling and missed complementary reasoning. We introduce CoRD, a collaborative multi-teacher decoding framework
Abstract:
arXiv:2605.02290v1 Announce Type: new Abstract: Distilling large reasoning models is essential for making Long-CoT reasoning practical, as full-scale inference remains computationally prohibitive. Existing curation-based approaches select complete reasoning traces post-hoc, overlooking collaboration among heterogeneous teachers and lacking dynamic exploration, which leads to redundant sampling and missed complementary reasoning. We introduce CoRD, a collaborative multi-teacher decoding framework
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