Datasets:

awni00
/

multi-strategy-algorithmic-tasks

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, 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

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:

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:

{
    "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.

Citation

@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.