Structural Rationale Distillation via Reasoning Space Compression
📰 ArXiv cs.AI
Learn to distill reasoning from large language models into smaller ones using Distillation through Reasoning Path Compression (D-RPC), improving consistency and reducing noisy supervision
Action Steps
- Implement D-RPC to constrain teacher models to follow a compact reasoning path
- Use dynamically maintained reasoning space compression to reduce variability in teacher rationales
- Apply D-RPC to distill reasoning from large language models into smaller ones
- Evaluate the performance of student models trained with D-RPC
- Compare the results with traditional distillation methods to assess the effectiveness of D-RPC
Who Needs to Know This
NLP engineers and researchers can benefit from this technique to improve the efficiency of their language models and reduce the burden of noisy supervision on student models
Key Insight
💡 D-RPC constrains teacher models to follow a compact reasoning path, reducing noisy supervision and improving consistency in student models
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🤖 Improve language model efficiency with Distillation through Reasoning Path Compression (D-RPC) 📚
Key Takeaways
Learn to distill reasoning from large language models into smaller ones using Distillation through Reasoning Path Compression (D-RPC), improving consistency and reducing noisy supervision
Full Article
Title: Structural Rationale Distillation via Reasoning Space Compression
Abstract:
arXiv:2605.07139v1 Announce Type: cross Abstract: When distilling reasoning from large language models (LLMs) into smaller ones, teacher rationales for similar problems often vary wildly in structure and strategy. Like a chef who makes the same dish differently each time, this inconsistency burdens the student with noisy supervision that is hard to internalize. We propose Distillation through Reasoning Path Compression (D-RPC), which constrains the teacher to follow a compact, dynamically mainta
Abstract:
arXiv:2605.07139v1 Announce Type: cross Abstract: When distilling reasoning from large language models (LLMs) into smaller ones, teacher rationales for similar problems often vary wildly in structure and strategy. Like a chef who makes the same dish differently each time, this inconsistency burdens the student with noisy supervision that is hard to internalize. We propose Distillation through Reasoning Path Compression (D-RPC), which constrains the teacher to follow a compact, dynamically mainta
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