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reasoning-degeneration-dev/algo-sft-cellular-automata-step-simulation-d5

sourceHugging Facemitupdated 6mo agoView on Hugging Face

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Model Card

Cellular Automata — Step Simulation

LoRA adapter for Qwen/Qwen2.5-1.5B-Instruct fine-tuned on cellular automata via Algorithmic Template SFT.

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	Algorithmic Template SFT
Framework	LLaMA-Factory (SFT stage)
LoRA rank	64
LoRA target	all linear layers
Learning rate	1e-4
Epochs	3
Batch size	4 (grad accum 4)
Cutoff length	32,768 tokens
Training data	5,000 deterministic step-by-step simulation traces (d5: all 256 rules, 16-20 cells, 3-5 steps)

Evaluation (v3, MAX_TOKENS=32768)

Split	Accuracy
Test (in-distribution)	94.6%
Harder variant	3.4%
Structural OOD	72.0% (Rule 110, never seen)

Notes

Learned to read and apply any rule from lookup table. Generalizes to novel rules (72% OOD) but struggles with multi-step on larger grids (3.4% harder).

Usage

python

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-cellular-automata-step-simulation-d5")
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)