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When Reasoning Meets Compression: Understanding the Effects of LLMs Compression on Large Reasoning Models
arXiv – CS AI|Nan Zhang, Eugene Kwek, Yusen Zhang, Ngoc-Hieu Nguyen, Prasenjit Mitra, Rui Zhang||4 views
🤖AI Summary
Researchers analyzed compression effects on large reasoning models (LRMs) through quantization, distillation, and pruning methods. They found that dynamically quantized 2.51-bit models maintain near-original performance, while identifying critical weight components and showing that protecting just 2% of excessively compressed weights can improve accuracy by 6.57%.
Key Takeaways
- →Dynamically quantized 2.51-bit DeepSeek-R1 models achieve close-to-original reasoning performance with significant compression.
- →Weight count impacts knowledge memorization more than reasoning ability, making pruning and distillation riskier compression methods.
- →The MLP up projection in final layers of distilled models is identified as one of the most critical components for reasoning.
- →Current quantization methods over-compress final-layer modules and MLP gate projections unnecessarily.
- →Protecting just 2% of excessively compressed weights can boost average accuracy by 6.57% over state-of-the-art methods.
#llm-compression#model-quantization#ai-reasoning#deepseek-r1#model-optimization#machine-learning#ai-efficiency#model-pruning#neural-networks
Read Original →via arXiv – CS AI
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