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language:
- en
pretty_name: MicroReason Math
size_categories:
- 100K<n<1M
task_categories:
- text-generation
- question-answering
tags:
- math
- arithmetic
- algebra
- synthetic
- reasoning
- chain-of-thought
- compositional-generalization
configs:
- config_name: benchmark
data_files:
- split: val
path: data/benchmark/val.jsonl
- split: val_heldout_shape
path: data/benchmark/val_heldout_shape.jsonl
- split: test_id
path: data/benchmark/test_id.jsonl
- split: test_heldout_shape
path: data/benchmark/test_heldout_shape.jsonl
- split: test_extrapolation
path: data/benchmark/test_extrapolation.jsonl
- split: test_extrapolation_fresh
path: data/benchmark/test_extrapolation_fresh.jsonl
- config_name: sft
data_files:
- split: train
path: data/sft/train.jsonl
---
# MicroReason Math
Synthetic arithmetic and linear-algebra problems with exact answers and canonical step-by-step
solutions. They were generated to train and evaluate
[MicroReason-43M](https://huggingface.co/arpjw/microreason-43m), a small language model trained
from scratch.
**Generation and checking:**
- **Source:** every problem comes from a seeded grammar over random expression trees and linear
equations.
- **Answers:** each answer is verified by an independent exact solver.
- **Steps:** every solution step is checked to be correct.
The benchmark splits test generalization to **unseen problem structures** and to **larger
numbers** than those seen in training.
```python
from datasets import load_dataset
bench = load_dataset("arpjw/microreason-math", "benchmark") # 6 evaluation splits, 40,000 problems
sft = load_dataset("arpjw/microreason-math", "sft", split="train") # 100,000 training problems
```
## Examples
```
Compute: 93 - (-90) * (-73)
1. Multiply: -90 * (-73) = 6570
2. Subtract: 93 - 6570 = -6477
answer: -6477
Find x: 23x + 7462 = 15(x - 2721)
1. Expand: 23x + 7462 = 15x - 40815
2. Subtract 15x from both sides: 8x + 7462 = -40815
3. Subtract 7462 from both sides: 8x = -48277
4. Divide both sides by 8: x = -48277/8
answer: -48277/8
```
## Splits
| Config / split | Problems | Shapes | What it measures |
| --- | ---: | ---: | --- |
| `benchmark` / `val` | 7,000 | 1,555 | In-distribution validation |
| `benchmark` / `val_heldout_shape` | 6,000 | 1,039 | Validation on problem structures never seen in training |
| `benchmark` / `test_id` | 7,000 | 1,574 | In-distribution test |
| `benchmark` / `test_heldout_shape` | 6,000 | 1,053 | Test on unseen structures (3–5 operators and equations) |
| `benchmark` / `test_extrapolation` | 7,000 | 1,214 | Test with 4–5 digit numbers (training uses 0–999) |
| `benchmark` / `test_extrapolation_fresh` | 7,000 | 1,209 | An independent second extrapolation suite (own seed, frozen separately) |
| `sft` / `train` | 100,000 | 9,407 | Supervised fine-tuning problems |
**Family mix:**
- **Expressions:** 1–5 operators over `+ − × ÷`. In-distribution splits are about 11% 1-op,
20% 2-op, 16% 3-op, 10% 4-op and 8% 5-op.
- **Equations:** linear equations make up about 35% of in-distribution splits.
- **Held-out-shape splits:** only 3+ operator structures and equations.
**How shapes are held out:** a *shape* is the operator structure of an expression tree, such as
`(- n (* n n))`, or an equation template.
- **Assignment:** each shape is hashed into exactly one of train-visible (80%), held-out-val
(10%) or held-out-test (10%).
- **Coverage guarantee:** every operator and pairing in a held-out shape also appears in a
training shape. Held-out problems are new *combinations*, not new primitives.
**Contamination checks:**
- No problem appears in more than one split.
- Every evaluation problem was checked against the 435 k-document general-text corpus
(Cosmopedia) used in pretraining.
## Fields
| Field | Description |
| --- | --- |
| `id` | Stable record id |
| `prompt` | The problem as posed to the model, e.g. `Compute: …` or `Find x: …` |
| `solution_steps` | Canonical worked solution: post-order reductions or fixed equation moves |
| `final_answer` | Exact answer: an integer, or a reduced fraction like `-48277/8` |
| `answer_type` | `integer` or `rational` |
| `kind` | `expression` or `linear_equation` |
| `math_text` | The bare expression or equation |
| `variable` | `x` for equations, else null |
| `family` | `expression_1op` … `expression_5op`, `linear_equation` |
| `shape` | Structural template (used for held-out splits and cluster bootstrap) |
| `difficulty` | Integer difficulty level |
| `number_range` | `in_distribution` (0–999) or `extrapolation` (4–5 digits) |
| `params` | JSON string of the sampled numbers |
| `seed` | Generator seed (string; a 64-bit unsigned integer) |
| `semantic_fingerprint` | Hash of the canonical problem, used for de-duplication |
| `generator_version` | `3.0` |
## Exact original files
`data/` holds loader-friendly copies, with `seed` and `params` stored as strings. `raw/` holds the
byte-exact files produced by the generator. Their sha256 hashes match the frozen manifests in the
MicroReason repository:
| File | sha256 |
| --- | --- |
| `raw/benchmark_val.jsonl` | `4d9b10b32896…` |
| `raw/benchmark_val_heldout_shape.jsonl` | `fabbcecef5dd…` |
| `raw/benchmark_test_id.jsonl` | `2085da0f3c2e…` |
| `raw/benchmark_test_heldout_shape.jsonl` | `a370443a077d…` |
| `raw/benchmark_test_extrapolation.jsonl` | `28db77f8794d…` |
| `raw/benchmark_test_extrapolation_fresh.jsonl` | `35d481707bd9…` |
| `raw/sft_train.jsonl` | `0020a0e550aa…` |
## How MicroReason-43M scores
Greedy decoding, show-work prompt, exact final answer:
| Model | `test_id` | `test_heldout_shape` | `test_extrapolation` | `test_extrapolation_fresh` |
| --- | ---: | ---: | ---: | ---: |
| `sft-long` | 87.1% | 81.4% | 10.2% | 9.5% |
| `grpo` | 87.8% | 82.0% | 10.1% | 9.3% |
| `extra-sft` | 88.8% | 83.1% | 7.8% | 7.4% |
## Not included
The larger training pools used for pretraining, mid-training, long SFT and RL are not included:
6 M + 5 M + 4 M records. They come from the same generator and are rebuilt deterministically from
their seeds.
## Notes
- **Prompt conventions:** in MicroReason the show-work prompt appends ` Show your work.` to
`prompt`. Solutions are rendered as numbered lines inside `[steps] … [/steps]`, followed by
`[answer]…[/answer]`.
- **Purpose:** these are synthetic problems for studying small-model reasoning and generalization.
They are not representative of natural-language math word problems.
|