AI BriefingFine-tuning Qwen3-1.7B
Key point
By refining and synthesizing 327 noisy traces, Qwen3-1.7B outperformed GLM-5.
Details
Instead of training directly on 327 noisy production traces, they first refined them, then generated synthetic data based on the results to fine-tune Qwen3-1.7B.
- Step 1: Removed irrelevant traces to filter out off-topic items.
- Step 2: 4 teacher LLMs re-labeled the noisy output, and an arbiter selected the final candidates.
- Step 3: Using the refined traces as context, about 10,000 synthetic examples were created.
- Step 4: The student model was trained on multi-turn tool-calling criteria.
On a 78-test set, Qwen3-1.7B tuned recorded LLM-as-a-Judge 0.846, staged_tool_call 0.769, and function match 76/78.
The comparison target, GLM-5 (744B teacher), scored 0.808, 0.695, and 69/78 respectively, while the untuned Qwen3-1.7B only reached 0.513, 0.535, and 45/78.
The key is a pipeline that, instead of distilling raw logs directly, creates synthetic data based on refined signals and trains the student model with that data.
This summary was generated automatically by AI. Check the original for the author's claims and context. Copyright belongs to the original author.
Our guide explains how the AI works. Report summary errors, attribution issues, or removal requests via Contact.
Related
Qwen3.5-based Small Decision Model 'Kev' Released with TypeSafe API Compatibility and Performance Metrics