Engineering brief
Distillation: A Cheaper Path to Reasoning Models for Small Teams
At a glance
The brief is enough
- Relevance
- Practical value
- Radar
- Time horizon
- Now
- Warnings
- None
Model distillation lets smaller teams build reasoning models by learning from larger teachers. Hard distillation is simpler but may lose information; this tutorial shows a 16% to 32% MATH-500 accuracy gain with DeepSeek R1 as teacher on a 0.6B model, using only 2-3 hours of
Provides a compute-efficient way to build reasoning models without expensive RLVR training from scratch.
Watch if you want to implement distillation for reasoning models and need a step-by-step code guide.
Summary
The rule of thumb. Distillation can yield better results on small reasoning models than training RLVR from scratch. Demonstrating this, a 0.6B Qwen model distilled from DeepSeek R1 jumped from 16% to 32% accuracy on MATH-500 after three epochs, using a fraction of the compute required for reinforcement learning.
The tradeoff. Two distillation methods exist—hard and soft. Hard distillation trains on the teacher's final tokens; soft uses the full probability distribution. Soft is more data-efficient but requires the same tokenizer and access to logits. Hard is simpler and works across model families, making it the common choice. The video uses hard distillation with DeepSeek R1 as teacher and Qwen 0.6B as student.
The implementation. The workflow is straightforward: generate teacher solutions for a dataset (12,000 math problems), tokenize once, filter sequences longer than 2,048 tokens (cutting to ~6,600 examples), and train with cross-entropy loss on the answer tokens. The training loop is similar to supervised fine-tuning, with epochs, shuffling, and gradient clipping.
The cost advantage. Distillation took 2–3 hours on a mid-range GPU and about 15GB of memory. In contrast, the RLVR training in an earlier chapter required 12 hours and 70GB. This makes distillation accessible to teams with limited hardware or budget.
What's overlooked. Proper data filtering is critical: without it, memory and training time explode. The validation set of only 25 examples is too small to detect overfitting—teams should evaluate on a held-out benchmark like MATH-500. Also, soft distillation's potential is often missed because it demands the same tokenizer; teams using different model families default to hard distillation without considering alternatives.
The bottom line. Distillation is a practical, cost-effective way to get reasoning capabilities into small models. The ceiling depends on the teacher's quality and the domain. For teams with moderate compute, it's likely a better first step than attempting RLVR on a small model from scratch.
My take
I find the rule of thumb compelling: for small models, distillation can beat training RLVR from scratch. But the results are on a narrow math benchmark, real reasoning tasks may not transfer as easily. The cost of generating teacher outputs from DeepSeek R1 via API adds up. Filtering long sequences is a practical detail many overlook; without it, memory blows up. I'd recommend teams start with hard distillation for simplicity, but monitor overfitting on a held-out benchmark, not just a 25-sample validation set.
What to do with this
- Generate teacher outputs for your target dataset using a large model like DeepSeek R1 or Qwen 235B via API or local inference.
- Filter training examples to a max sequence length (e.g., 2048 tokens) to control memory usage during distillation.
- Train a small student model with cross-entropy loss on the teacher outputs for 2–3 epochs, evaluating on a held-out benchmark.
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