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awni00/multi-strategy-algorithmic-tasks

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, which studies the problem of recovering mixtures of strategies implicitly represented in language models. The benchmark provides a… See the full description on the dataset page: https://huggingface.co/datasets/awni00/multi-strategy-algorithmic-tasks.

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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:

SplitRows
train1,000,000
validation10,000
test10,000

Release version: v1.0.0.

To select one task family:

python
sorting = dataset.filter(
    lambda example: example["task_name"] == "sorting_algorithms"
)

Fields

FieldDescription
task_nameAlgorithmic task family
strategy_idNamespaced strategy sampled to generate the trace
input_textRendered problem instance
reasoning_traceComplete 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.

TaskProblemStrategies
list_summationSum four integers from 0 to 9left-to-right, right-to-left, pairwise
sorting_algorithmsSort five integers from 0 to 9bubble-sort, selection-sort, insertion-sort, merge-sort, heap-sort
grid_pathfindingMonotone shortest paths on a 6×6 gridright-first, down-first, alternating
linear_equation_solvingSolve integer equations of the form ax+b=csubtract-then-divide, divide-then-subtract, inverse-ops
base_conversionConvert integers from 1 to 255 to base 2, 4, 8, or 16repeated-division, via-binary, decomposition
multidigit_additionAdd two three-digit nonnegative integersright-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.

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.