Knowledge Distillation for Large Language Models
We propose a resource-efficient framework for compressing large language models through knowledge distillation, combined with guided chain-of-thought reinforcement learning. Using Qwen 3B as the teacher and Qwen 0.5B as the student, we apply knowledge distillation across English Dolly-15k, Spanish Dolly-15k, and code BugNet and PyTorrent datasets, with hyperparameters tuned in the English setting to optimize student performance. Across tasks, the distilled student retains a substantial portion of the teacher's capability while remaining significantly smaller: 70% to 91% in English, up to 95% in Spanish, and up to 93.5% Rouge-L in code. For coding tasks, integrating chain-of-thought prompt...
| Authors | Alejandro Paredes La Torre, Barbara Flores, Diego Rodriguez |
|---|---|
| Primary category | cs.CL |
| Published | 14 March 2026 |
| Last updated at source | 14 March 2026 |
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