---
language:
- en
task_categories:
- text-generation
tags:
- reasoning
- reinforcement-learning
- verifiable-rewards
- modebench
pretty_name: ModeBench
configs:
- config_name: level1_countdown
data_files:
- split: train
path: data/level1_countdown/train.parquet
- split: eval
path: data/level1_countdown/eval.parquet
- config_name: level1_graph_coloring
data_files:
- split: train
path: data/level1_graph_coloring/train.parquet
- split: eval
path: data/level1_graph_coloring/eval.parquet
- config_name: level1_graph_coloring_unique_answer
data_files:
- split: eval
path: data/level1_graph_coloring_unique_answer/eval.parquet
- config_name: level1_mathir
data_files:
- split: train
path: data/level1_mathir/train.parquet
- split: eval
path: data/level1_mathir/eval.parquet
- config_name: level1_pantry_plan
data_files:
- split: train
path: data/level1_pantry_plan/train.parquet
- split: dev
path: data/level1_pantry_plan/dev.parquet
- split: eval
path: data/level1_pantry_plan/eval.parquet
- config_name: level1_python_factors
data_files:
- split: train
path: data/level1_python_factors/train.parquet
- split: eval
path: data/level1_python_factors/eval.parquet
- config_name: level2_countdown
data_files:
- split: train
path: data/level2_countdown/train.parquet
- split: dev
path: data/level2_countdown/dev.parquet
- split: eval
path: data/level2_countdown/eval.parquet
- config_name: level2_graph_coloring
data_files:
- split: train
path: data/level2_graph_coloring/train.parquet
- split: dev
path: data/level2_graph_coloring/dev.parquet
- split: eval
path: data/level2_graph_coloring/eval.parquet
- config_name: level2_mathir
data_files:
- split: train
path: data/level2_mathir/train.parquet
- split: dev
path: data/level2_mathir/dev.parquet
- split: eval
path: data/level2_mathir/eval.parquet
- config_name: level2_pantry_plan
data_files:
- split: train
path: data/level2_pantry_plan/train.parquet
- split: dev
path: data/level2_pantry_plan/dev.parquet
- split: eval
path: data/level2_pantry_plan/eval.parquet
- config_name: level2_python_factors
data_files:
- split: train
path: data/level2_python_factors/train.parquet
- split: dev
path: data/level2_python_factors/dev.parquet
- split: eval
path: data/level2_python_factors/eval.parquet
- config_name: level3_countdown
data_files:
- split: train
path: data/level3_countdown/train.parquet
- split: dev
path: data/level3_countdown/dev.parquet
- split: eval
path: data/level3_countdown/eval.parquet
- config_name: level3_graph_coloring
data_files:
- split: train
path: data/level3_graph_coloring/train.parquet
- split: dev
path: data/level3_graph_coloring/dev.parquet
- split: eval
path: data/level3_graph_coloring/eval.parquet
- config_name: level3_mathir
data_files:
- split: train
path: data/level3_mathir/train.parquet
- split: dev
path: data/level3_mathir/dev.parquet
- split: eval
path: data/level3_mathir/eval.parquet
- config_name: level3_pantry_plan
data_files:
- split: train
path: data/level3_pantry_plan/train.parquet
- split: dev
path: data/level3_pantry_plan/dev.parquet
- split: eval
path: data/level3_pantry_plan/eval.parquet
- config_name: level3_python_factors
data_files:
- split: train
path: data/level3_python_factors/train.parquet
- split: dev
path: data/level3_python_factors/dev.parquet
- split: eval
path: data/level3_python_factors/eval.parquet
- config_name: level4_countdown
data_files:
- split: dev
path: data/level4_countdown/dev.parquet
- split: eval
path: data/level4_countdown/eval.parquet
- split: train
path: data/level4_countdown/train.parquet
- config_name: level4_graph_coloring
data_files:
- split: dev
path: data/level4_graph_coloring/dev.parquet
- split: eval
path: data/level4_graph_coloring/eval.parquet
- split: train
path: data/level4_graph_coloring/train.parquet
- config_name: level4_mathir
data_files:
- split: dev
path: data/level4_mathir/dev.parquet
- split: eval
path: data/level4_mathir/eval.parquet
- split: train
path: data/level4_mathir/train.parquet
- config_name: level4_pantry_plan
data_files:
- split: dev
path: data/level4_pantry_plan/dev.parquet
- split: eval
path: data/level4_pantry_plan/eval.parquet
- split: train
path: data/level4_pantry_plan/train.parquet
- config_name: level4_python_factors
data_files:
- split: dev
path: data/level4_python_factors/dev.parquet
- split: eval
path: data/level4_python_factors/eval.parquet
- split: train
path: data/level4_python_factors/train.parquet
- config_name: level5_countdown
data_files:
- split: dev
path: data/level5_countdown/dev.parquet
- split: eval
path: data/level5_countdown/eval.parquet
- split: train
path: data/level5_countdown/train.parquet
- config_name: level5_graph_coloring
data_files:
- split: dev
path: data/level5_graph_coloring/dev.parquet
- split: eval
path: data/level5_graph_coloring/eval.parquet
- split: train
path: data/level5_graph_coloring/train.parquet
- config_name: level5_mathir
data_files:
- split: dev
path: data/level5_mathir/dev.parquet
- split: eval
path: data/level5_mathir/eval.parquet
- split: train
path: data/level5_mathir/train.parquet
- config_name: level5_pantry_plan
data_files:
- split: dev
path: data/level5_pantry_plan/dev.parquet
- split: eval
path: data/level5_pantry_plan/eval.parquet
- split: train
path: data/level5_pantry_plan/train.parquet
- config_name: level5_python_factors
data_files:
- split: dev
path: data/level5_python_factors/dev.parquet
- split: eval
path: data/level5_python_factors/eval.parquet
- split: train
path: data/level5_python_factors/train.parquet
---
# ModeBench
ModeBench measures both correctness and the variety of correct answers produced by language models. Each problem has an executable verifier and a canonical definition of answer identity, so distinct wording alone does not count as a new solution.
This release contains three frozen benchmark levels across **Graph Coloring, Countdown, Python Factors, MathIR, and PantryPlan**. It preserves every original feature, row, and row order. The `answer` field is a serialized executable specification, not a target answer to include in the model prompt.
[Level 1](#level-1) · [Level 2](#level-2) · [Level 3](#level-3) · [Loading](#loading) · [Evaluation](#evaluation) · [Manifest](MANIFEST.json) · [Validation](VALIDATION.json)
The accompanying [model and research-artifact archive](https://huggingface.co/od2961/maxent-grpo-models) contains experiment-specific model indexes and mappings to the paper results. Dataset publication does not imply completed Level-3 training results.
## Level 1
The original benchmark uses 384 training problems and 128 primary evaluation problems per domain. PantryPlan additionally has its original 64-row development split. Graph Coloring also includes a separate 128-row `unique_answer` evaluation subset; it is published as the auxiliary `level1_graph_coloring_unique_answer` configuration and is not the primary multi-answer paper evaluation.
## Level 2
Level 2 is the frozen `modebench_harder_v2_matched_r5` release. It changes problem structure while retaining the verifier and canonicalizer contracts. Each domain has 384 training, 128 development, and 128 evaluation rows. Support histograms follow the recorded matched reference construction; refer to the original identity manifest for the precise reference splits and scaling factors.
## Level 3
Level 3 is the E122-frozen `modebench_level3_matched_v3` release, with 384 training, 128 development, and 128 evaluation rows per domain. It preserves Level-2 support histograms. Admission used adaptive second-round matching to fixed historical Level-1 measurements: Graph and Python have fresh v3 confirmation, while Countdown, MathIR, and Pantry retain prior split bytes and confirmation evidence. This is not a claim of statistical equivalence, or of five fresh same-round confirmations.
The original [identity manifest](provenance/source_data/modebench_level3_matched_v3/identity.json) still records `pending_fresh_candidate_confirmation`. That historical record is preserved verbatim. The later [confirmation report](provenance/confirmation_report.json) records `matched_fixed_reference` and binds that exact dataset identity. Neither record has been rewritten for publication.
## Loading
```python
from datasets import load_dataset
ds = load_dataset("od2961/ModeBench", "level2_countdown")
train = ds["train"]
development = ds["dev"]
evaluation = ds["eval"]
# Raw problem text goes to the policy. Keep the executable answer spec private
# from the policy and use it only in the verifier.
problem = evaluation[0]["problem"]
reference_spec = evaluation[0]["answer"]
```
Use `level1_`, `level2_`, or `level3_` followed by `graph_coloring`, `countdown`, `python_factors`, `mathir`, or `pantry_plan`. Only existing splits are exposed; Level 1 generally has no `dev` split. No `test` split is invented. The Parquet files load through the standard datasets loader without remote dataset code. Pin a dataset revision for reproducible experiments.
## Evaluation
Use the original held-out `eval` rows and the frozen prompt, decoding, and verifier settings for a stated comparison. Use `dev` only where the original release provides it. Do not fit prompts, choose checkpoints, or tune methods on evaluation outcomes; training, development, and evaluation roles remain separate after public release.
The same executable validation produces acceptance and canonical identity. Report correctness and distinct correct identities separately: with K sampled answers, pass@K is whether any answer verifies, and distinct@K counts unique verified canonical keys. The reported main benchmark uses K=8; the papers specify the seeds, draws, and training checkpoints. [Evaluation and prompt guide](EVALUATION.md) documents the supplied verifier interface and its versioned source.
The support-count metadata and reference specifications are for construction, checking, and analysis. Do not supply gold support catalogues or hidden evaluation references to the policy. Original prompt strings are retained; [prompt formatting](PROMPTS.md) distinguishes raw problems from the model-specific chat and decoding interfaces.
## Files and provenance
`MANIFEST.json` records every source identity, feature schema, ordered-row digest, split mapping, and Parquet digest. `VALIDATION.json` records full row and feature round trips, unchanged source files, canonical problem-identity checks, and support matching. `code/oat_drgrpo/` preserves verifier and template source files byte for byte; `provenance/` preserves original identities and construction/admission records.
## License provenance
The original repository's Apache-2.0 source-code license is preserved in [licenses/source_repository_APACHE_2.0.txt](licenses/source_repository_APACHE_2.0.txt), and copied source copyright notices remain intact. The frozen dataset roots contain no separate dataset-specific license declaration; this export does not invent new dataset license terms. PantryPlan ingredient provenance records USDA FoodData Central Foundation Foods under CC0/public-domain source terms in [pantry_ingredient_source.json](provenance/pantry_ingredient_source.json).
## Levels 4 and 5: admission status
**Level 5 is admitted.** All five domains cleared held-out confirmation against 128 fresh rows each, replayed through the original graders. Level 5 carries the same warrant as Levels 1-3.
**Level 4 cleared four of five domains and is not admitted.** Difficulty-matched: `level4_countdown`, `level4_graph_coloring`, `level4_pantry_plan`, `level4_python_factors`. Not difficulty-matched: `level4_mathir`, which missed its pass@1 gate at 1.07 of tolerance.
A result on Level 5, or on one of the four confirmed Level 4 domains, rests on the same warrant as one on Levels 1-3. A Level 4 MathIR result does not, and should not be reported as difficulty-matched.
The Level 1 MathIR target is not reachable at 7B by any construction measured: the target pairs pass@1 0.0437 with pass@8 0.2402, a heterogeneity gap of 0.060, while every 7B MathIR construction runs 0.169 to 0.398, so the two gates cannot be met together. The dataset itself is sound and verified.
Every config records its own status; see `provenance/levels45_manifest.json`.