Technical analysis of generational upgrading
The core enhancements of Qwen3 over Qwen 2.5 are reflected in three dimensions:
- structural innovation::
- Introducing MoE Architecture to Achieve 10X Increase in Parameter Efficiency
- Attention head configuration optimization (e.g. 32B model query head increased to 64)
- 14B and above models cancel word embedding binding (tie_embedding)
- Training Breakthroughs::
- Context window expanded from 8K to 128K
- Training with progressive length extension (4K → 32K → 128K)
- 3X increase in computing resource investment in intensive learning phase
- data engineering::
- Introduction of self-supervised quality filtering in the synthetic data generation process
- Percentage of data in STEM fields increased to 181 TP3T
- Code data add TypeScript/Rust and other modern languages
The performance showsgenerational compression effect::
- Qwen3-4B performance rivals Qwen2.5-72B
- The MoE version 30B model training cost is only 1/5 of the dense version 72B
- 17.31 TP3T improvement in 32B model accuracy on GSM 8K math benchmarks
These improvements bring Qwen3 to the Gemini 1.5 Pro level of complex inference while maintaining inference speed.
This answer comes from the articleQwen3 Released: A New Generation of Big Language Models for Thinking Deeply and Responding FastThe
May not be reproduced without permission:AI productivity tools " What are the key improvements in model architecture and training methodology of Qwen3 over its predecessor, Qwen 2.5?
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