Algorithmic SFT vs Distillation
Collection
10 LoRA adapters + 6 datasets. Algo template SFT vs QwQ distillation on Qwen2.5-1.5B-Instruct across 4 reasoning domains. • 16 items • Updated
Conlang Morphology — QwQ Distillation
LoRA adapter for Qwen/Qwen2.5-1.5B-Instruct fine-tuned on conlang morphology via QwQ-32B Distillation.
Part of the Algorithmic SFT vs Distillation experiment studying whether deterministic algorithmic templates teach procedural reasoning more effectively than distillation from large reasoning models.
Training
| Parameter | Value |
|---|---|
| Base model | Qwen/Qwen2.5-1.5B-Instruct |
| Method | QwQ-32B Distillation |
| Framework | LLaMA-Factory (SFT stage) |
| LoRA rank | 64 |
| LoRA target | all linear layers |
| Learning rate | 1e-4 |
| Epochs | 3 |
| Batch size | 1 (grad accum 16) |
| Cutoff length | 32,768 tokens |
| Training data | 5,000 QwQ-32B reasoning traces (d7+d5, 3 samples/question, filtered). Teacher solve rate: 15.8-44.3% |
Evaluation (v3, MAX_TOKENS=32768)
| Split | Accuracy |
|---|---|
| Test (in-distribution) | 40.4% |
| Harder variant | 11.0% |
| Structural OOD | 38.4% |
Notes
Large gap vs algo SFT (40.4% vs 98.6%). Distillation traces don't transfer the rule composition procedure.
Usage
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-1.5B-Instruct")
model = PeftModel.from_pretrained(base, "reasoning-degeneration-dev/algo-sft-conlang-morphology-distill-qwq")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-1.5B-Instruct")
Related Datasets
- Training data (63K algo traces)
- Distillation data (24K QwQ traces)
- Eval results (aggregate scores)
- Eval questions (11K test/val/harder/OOD)
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