MATH0-MID

Math Done

math0_mid

d125m (134.1M parameters) on OpenWebMath + DeepMind maths (70/30), trained on RTX 3060 12GB (Home GPU). Started 7 Oct 2026, ended 7 Oct 2026.

Hypothesis

250M more pre-training tokens for MATH0, 70% its maths web pages and 30% DeepMind arithmetic, algebra and number drills, teach it the arithmetic fine-tuning could not.

What we learned

Running: continued pre-training from MATH0; scores follow.

Scores

Lower is better on the headline; change against the parent run, MATH0.

OpenWebMath held-out, clean
1.07346
bits per byte · headline
+0.7%
Validation, in training
1.0379
bits per byte
+0.7%
Every published score of MATH0-MID, against its parent MATH0
Test setMATH0-MIDMATH0 (parent)Change
OpenWebMath held-out, clean headline
Mathematical web pages never used in training, minus every page with copied passages in the training text.
1.07346 1.06595+0.7%
GSM8K test
Grade-school maths word problems with worked solutions, scored as text.
1.08254 1.07016+1.2%
MATH test, clean
Competition problems with solutions, mostly in LaTeX, minus any with copied text in the training pages.
0.86696 0.86027+0.8%
OpenWebMath held-out, all
Every held-out OpenWebMath page, including the ones with passages copied into training pages.
0.99899 0.99258+0.6%
MATH test, all
All 5,000 MATH test problems, including the ones with copied text in the training pages.
0.85298 0.84625+0.8%

Bits per byte: how many bits the model needs, on average, to predict each byte of text it never saw in training. Lower is better, and it compares models with different tokenizers fairly, because every model is charged for the same bytes.

Curves

Training loss

Cross-entropy per token, by step. The first few percent of the run, far higher, run off the top; hover or the table has every value.

Held-out bits per byte

The validation split, evaluated during training, by step. Lower is better.

Throughput

Tokens per second, by step.

Model

Preset
d125m
Parameters
134,105,856
Outside embeddings
84,953,856
Layers · heads · width
12 · 12 · 768
Tokenizer
Shared unigram 32k

Muon + AdamW at a third of the peak rate, flat then linear decay, from MATH0

Data

Slice
OpenWebMath + DeepMind maths (70/30)
Words
—
Training tokens
1,555,523,031
Passes
0.16 passes
Tokens seen
250,183,680

Compute

GPU
RTX 3060 12GB
Where
Home GPU
Steps
5,090 / 5,090
GPU hours
2.83
Cost
—
Spot restarts
—

Lineage

homed125m