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pretty_name: Multi-Strategy Algorithmic Tasks
license: cc-by-4.0
language:
- en
task_categories:
- text-generation
tags:
- synthetic
- reasoning
- algorithmic-reasoning
- strategy
- datasets
size_categories:
- 1M<n<10M
---
# Multi-Strategy Algorithmic Tasks
A synthetic benchmark of parseable algorithmic problems with multiple valid
solution strategies for each task. Each example contains a problem,a strategy-specific
solution trace, and the strategy used to generate that trace.
The benchmark accompanies
*[Uncovering Latent Reasoning Strategies in Language Models](https://arxiv.org/abs/2607.17674)*,
which studies the problem of recovering mixtures of strategies implicitly represented in language models.
The benchmark provides a controlled setting for studying strategy recovery, representation, routing,
and controllable generation.
## Load the dataset
```python
from datasets import load_dataset
dataset = load_dataset("awni00/multi-strategy-algorithmic-tasks")
example = dataset["train"][0]
```
The release contains:
| Split | Rows |
| --- | ---: |
| `train` | 1,000,000 |
| `validation` | 10,000 |
| `test` | 10,000 |
Release version: `v1.0.0`.
To select one task family:
```python
sorting = dataset.filter(
lambda example: example["task_name"] == "sorting_algorithms"
)
```
## Fields
| Field | Description |
| --- | --- |
| `task_name` | Algorithmic task family |
| `strategy_id` | Namespaced strategy sampled to generate the trace |
| `input_text` | Rendered problem instance |
| `reasoning_trace` | Complete strategy-specific solution trace, including the final answer |
Example:
```python
{
"task_name": "multidigit_addition",
"strategy_id": "multidigit_addition:left-to-right-partials",
"input_text": "560+342",
"reasoning_trace": (
"p100:500+300=800 ; p10:60+40=100 ; "
"p1:0+2=2 ; sum=800+100+2=902"
),
}
```
## Tasks and strategies
The generator first samples one of the six task families uniformly. It then
samples a strategy uniformly within that task family.
| Task | Problem | Strategies |
| --- | --- | --- |
| `list_summation` | Sum four integers from 0 to 9 | `left-to-right`, `right-to-left`, `pairwise` |
| `sorting_algorithms` | Sort five integers from 0 to 9 | `bubble-sort`, `selection-sort`, `insertion-sort`, `merge-sort`, `heap-sort` |
| `grid_pathfinding` | Monotone shortest paths on a 6×6 grid | `right-first`, `down-first`, `alternating` |
| `linear_equation_solving` | Solve integer equations of the form `ax+b=c` | `subtract-then-divide`, `divide-then-subtract`, `inverse-ops` |
| `base_conversion` | Convert integers from 1 to 255 to base 2, 4, 8, or 16 | `repeated-division`, `via-binary`, `decomposition` |
| `multidigit_addition` | Add two three-digit nonnegative integers | `right-to-left-carry`, `left-to-right-partials`, `rounding-decomposition` |
The public `strategy_id` includes the task namespace, for example
`grid_pathfinding:alternating`. There are 20 strategies in total across the six tasks.
`strategy_id` is the strategy selected by the generator. On some inputs,
multiple strategies produce the same observable trace.
This is most common when a problem requires only a few steps or different
algorithms happen to traverse identical intermediate states.
## License
The dataset is released under the
[Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/).
## Citation
```bibtex
@misc{altabaa2026uncovering,
title = {Uncovering Latent Reasoning Strategies in Language Models},
author = {Awni Altabaa and John Lafferty},
year = {2026},
eprint = {2607.17674},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/2607.17674}
}
```
Code associated with the paper is available at
[Awni00/latent-strategies-in-lms](https://github.com/Awni00/latent-strategies-in-lms).
|