Part 4: Knowledge Distillation from DeepSeek-R1 & Frontier Teachers
← Previous Chapter: Part 3: QLoRA & Axolotl Fine-Tuning | Series Hub | Next Chapter: Part 5: Preference Alignment: DPO, GRPO & KTO → Prerequisite: Read Part 3: QLoRA & Axolotl Fine-Tuning on Commodity GPUs for low-rank parameter tuning and memory budgeting. Answer-first: Distilling long Chain-of-Thought (CoT) reasoning traces from DeepSeek-R1 (671B MoE) into compact 1.5B–8B student models transfers complex deductive capability without hosting frontier hardware. Combining forward-backward token KL divergence with rejection sampling on verifiable tasks enables a 7B student model to recover 88% of teacher mathematical reasoning performance at 1/50th the operational cost. ...