license: apple-amlr
base_model:
- Qwen/Qwen3-4B-Thinking-2507
tags:
- self-distillation
- code-generation
- ssd
library_name: transformers
SSD-Qwen3-4B-Thinking
This model was produced using Simple Self-Distillation (SSD), a method that improves code generation by fine-tuning a language model on its own sampled outputs—without rewards, verifiers, teacher models, or reinforcement learning.
- Base model: Qwen/Qwen3-4B-Thinking-2507
- Variant: thinking
- Self-distillation sampling: temperature=1.1, top_p=0.95, top_k=20
- Evaluation sampling: temperature=0.7, top_p=0.95, top_k=20
Method
SSD samples solutions from the base model using non-unit temperature and top-k/top-p truncation, then fine-tunes on those samples via standard supervised learning. Despite its simplicity, SSD yields large gains on competitive programming benchmarks, with improvements concentrating on harder problems. The mechanism traces to resolving a precision–exploration conflict: SSD reshapes token distributions in a context-dependent way so that a single global decoding configuration becomes far more effective at evaluation time.
Results
LiveCodeBench (%)
| Model | LCBv6 pass@1 | LCBv6 pass@5 | LCBv5 pass@1 | LCBv5 pass@5 |
|---|---|---|---|---|
| Qwen3-4B-Thinking-2507 (base) | 54.5 | 67.5 | 59.6 | 70.3 |
| + SSD (this model) | 57.8 (+3.3) | 71.4 (+3.9) | 63.1 (+3.5) | 74.7 (+4.4) |
Paper
Embarrassingly Simple Self-Distillation Improves Code Generation
Ruixiang Zhang, Richard He Bai, Huangjie Zheng, Navdeep Jaitly, Ronan Collobert, Yizhe Zhang
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("apple/SSD-Qwen3-4B-Thinking")
tokenizer = AutoTokenizer.from_pretrained("apple/SSD-Qwen3-4B-Thinking")
License
This model is released under the Apple Machine Learning Research Model License.