datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
gepa-rlm-exp-algorithmic-20260219-191545
gepa-rlm-exp-algorithmic-20260219-191545
GEPA prompt optimization experiment on AIME math problems.
Task LM: openai/gpt-4.1-mini | Reflection LM: openai/gpt-5 | Reflection Mode: algorithmic | Last updated: 2026-02-19 21:34 UTC
Results
Run
Method
k
Mode
Val Score
Test Acc
Tokens
Cost
Time
fixed_rlm_k20
rlm
20
algorithmic
48.89%
26.67%
932,391
$0.0000
5209s
Learning Curves
Experiment Config
{
"script_name": "run_experiment.py"… See the full description on the dataset page: https://huggingface.co/datasets/reasoning-degeneration-dev/gepa-rlm-exp-algorithmic-20260219-191545.gepa-exp-algorithmic-vanilla-20260220-083526
gepa-exp-algorithmic-vanilla-20260220-083526
GEPA prompt optimization experiment on AIME math problems.
Task LM: openai/gpt-4.1-mini | Reflection LM: openai/gpt-5 | Reflection Mode: algorithmic | Last updated: 2026-02-20 21:20 UTC
Results
Run
Method
k
Mode
Val Score
Test Acc
Tokens
Cost
Time
fixed_vanilla_k3
vanilla
3
algorithmic
35.56%
46.67%
77,464
$0.2182
3126s
Learning Curves
Experiment Config
{
"script_name":… See the full description on the dataset page: https://huggingface.co/datasets/reasoning-degeneration-dev/gepa-exp-algorithmic-vanilla-20260220-083526.algorithmic-sft-training-data-v1
algorithmic-sft-training-data-v1
Algorithmic SFT training data: deterministic step-by-step traces for 5 domains (countdown, formal_logic, long_arithmetic, conlang_morphology, cellular_automata) across multiple algorithm variants. Programmatically generated — no LLM involved.
Dataset Info
Rows: 63000
Columns: 8
Columns
Column
Type
Description
question
Value('string')
The problem statement presented to the model
answer
Value('string')
The correct… See the full description on the dataset page: https://huggingface.co/datasets/reasoning-degeneration-dev/algorithmic-sft-training-data-v1.gepa-exp-algorithmic-rlm-20260220-083526
gepa-exp-algorithmic-rlm-20260220-083526
GEPA prompt optimization experiment on AIME math problems.
Task LM: openai/gpt-4.1-mini | Reflection LM: openai/gpt-5 | Reflection Mode: algorithmic | Last updated: 2026-02-20 21:25 UTC
Results
Run
Method
k
Mode
Val Score
Test Acc
Tokens
Cost
Time
fixed_rlm_k3
rlm
3
algorithmic
37.78%
44.00%
400,201
$0.0000
3483s
Learning Curves
Experiment Config
{
"script_name": "run_experiment.py"… See the full description on the dataset page: https://huggingface.co/datasets/reasoning-degeneration-dev/gepa-exp-algorithmic-rlm-20260220-083526.algorithmic-reasoning-seed
Dataset Card for Algorithmic Reasoning (seed)
Note: This dataset is WIP and most question's answer section is empty or incomplete! See also "Other Known Limitations" section
Warning: If you somehow do use this dataset, remember to NOT do any eval after training on the questions in this dataset!
Dataset Summary
Dataset to help LLM learn how to reason about code, especially on algorithmic tasks, by seeing human demostration.
Supported Tasks and Leaderboards
[More… See the full description on the dataset page: https://huggingface.co/datasets/lemonteaa/algorithmic-reasoning-seed.algorithmic-sft-training-configs-v1
algorithmic-sft-training-configs-v1
LlamaFactory training configs. All cutoff_len=32768. Countdown configs use new equation-answer format.
Dataset Info
Rows: 17
Columns: 7
Columns
Column
Type
Description
config_name
Value('string')
YAML filename
domain
Value('string')
No description provided
is_distillation
Value('bool')
No description provided
yaml_content
Value('string')
Full YAML config
model_name
Value('string')
No description provided… See the full description on the dataset page: https://huggingface.co/datasets/reasoning-degeneration-dev/algorithmic-sft-training-configs-v1.algorithmic-sft-sharegpt-training-v1
algorithmic-sft-sharegpt-training-v1
Exact LlamaFactory training data. Countdown uses equation-answer format with Step 1:.... Other domains use Answer: X. All wrapped in tags.
Dataset Info
Rows: 82903
Columns: 4
Columns
Column
Type
Description
conversations
List({'from': Value('string'), 'value': Value('string')})
ShareGPT — literal LlamaFactory input
source_file
Value('string')
JSON filename → dataset_info.json
model_type
Value('string')… See the full description on the dataset page: https://huggingface.co/datasets/reasoning-degeneration-dev/algorithmic-sft-sharegpt-training-v1.algorithmic-reasoning-seed
Dataset Card for Algorithmic Reasoning (seed)
Note: This dataset is WIP and most question's answer section is empty or incomplete! See also "Other Known Limitations" section
Warning: If you somehow do use this dataset, remember to NOT do any eval after training on the questions in this dataset!
Dataset Summary
Dataset to help LLM learn how to reason about code, especially on algorithmic tasks, by seeing human demostration.
Supported Tasks and Leaderboards
[More… See the full description on the dataset page: https://huggingface.co/datasets/pharaouk/algorithmic-reasoning-seed.algorithmic-sft-eval-sets-v1
algorithmic-sft-eval-sets-v1
Evaluation sets for algorithmic SFT experiment: test (1000/domain), val (200/domain), harder variant (500/domain), structural OOD (500/domain). 5 domains total.
Dataset Info
Rows: 11000
Columns: 9
Columns
Column
Type
Description
question
Value('string')
The problem statement
answer
Value('string')
The correct answer
sft_trace
Value('null')
Reference algorithmic trace (golden solution)
difficulty
Value('int64')… See the full description on the dataset page: https://huggingface.co/datasets/reasoning-degeneration-dev/algorithmic-sft-eval-sets-v1.reasoning-gym-dataset-algorithmic-tasksalgorithmic-sft-distillation-training-data-v1
algorithmic-sft-distillation-training-data-v1
QwQ-32B distillation training data for 5 algorithmic domains. Correct responses filtered by collect_distill_results.py with truncation rejection. KNOWN ISSUE: countdown domain has 74.2% QwQ repetition loops (model repeats \boxed{} answer until hitting token limit). Countdown is being regenerated with v3 pipeline (32k tokens). Other 4 domains are clean (>99% quality).
Dataset Info
Rows: 24133
Columns: 6
Columns… See the full description on the dataset page: https://huggingface.co/datasets/reasoning-degeneration-dev/algorithmic-sft-distillation-training-data-v1.reasoninggym-algorithmicalgorithmic-sft-full-eval-v3
algorithmic-sft-full-eval-v3
Aggregate eval results: 16 models × 3 splits (test/harder/OOD). v3 re-evaluation at MAX_TOKENS=32768. Bootstrap 95% CI.
Dataset Info
Rows: 50
Columns: 14
Columns
Column
Type
Description
eval_name
Value('string')
Filename-derived eval identifier
domain
Value('string')
Task domain (countdown, formal_logic, long_arithmetic, cellular_automata, conlang_morphology)
variant
Value('string')
Model variant (algorithm name or… See the full description on the dataset page: https://huggingface.co/datasets/reasoning-degeneration-dev/algorithmic-sft-full-eval-v3.AlgorithmicReasoning