Datasets:
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9379442 28a5abc 9379442 28a5abc 9379442 28a5abc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 | # KodCode
## Curation
We prepare a 36,074-problem pool from KodCode-V1-SFT-4o (26,475 problems) and
KodCode-Light-RL-10K (9,599 problems). The recorded preparation process includes
deduplication by original question, interface repair, static checks, and
reference-solution execution checks. All released problems retain a historical
full-execution-pass flag; this is verification against the supplied tests, not
a guarantee that the tests cover every valid input.
SFT and RL share 35,974 training and 100 validation problems. The validation
problems all come from Light-RL-10K; they are not a source-stratified sample of
the combined pool. The earlier 37,881-answer diagnostic pool is not a separate
configuration in this release.
## LLM-assisted annotation
We add algorithmic category annotations through an LLM API and retain the
classification reasons and review history alongside the data. The records
identify 7,564 reviewed annotations and 1,677 annotations changed during review.
These counts cover train and validation once, without double-counting the RL
representation of the same problems.
| Final recorded classifier | Problems |
|---|---:|
| `gpt-5.4-mini` | 28,492 |
| `gpt-5.4` | 7,472 |
| `gpt-4.1` | 110 |
These are the model identifiers stored in `classifier_model`, not a count of
all API calls. Review refers to the recorded classification stage; it should
not be interpreted as independent human verification.
The reviewed v1 primary categories and subcategories are mapped deterministically
into the ten-category v2 taxonomy. This mapping makes no additional API calls.
Definitions and mapping rules are in
[taxonomy.json](../metadata/kodcode/taxonomy.json).
| Fields | Meaning |
|---|---|
| `primary_category`, `taxonomy_version` | One final v2 category and its taxonomy version |
| `v1_primary_category`, `v1_subcategory` | Reviewed labels before the v2 mapping |
| `v1_alternative_category` | Optional alternative in the older taxonomy, not a second v2 label |
| `techniques` | Auxiliary free-text techniques, not additional primary labels |
| `rationale` | The reason for the classification, not the solution's reasoning trace |
| `classifier_model`, `classification_stage` | Recorded classifier and annotation stage |
| `was_reviewed`, `review_changed`, `v1_first_pass_category` | Review status and earlier classification |
| `confidence`, `v1_confidence` | Recorded annotation confidence, not a measured accuracy |
| `mapping_method` | How the final category was derived |
The original API prompts, request settings, and review-selection logic are not
included in this release. The annotations can be reused, but the API annotation
process cannot be rerun end to end from this package alone.
## Reusing the experimental subsets
Historical main ablations remove the primary category only, then select a
recorded equal-size sample. They do not remove every problem mentioning that
category in its auxiliary techniques or alternative label.
```bash
python scripts/build_kodcode.py --arm no_dynamic_programming --output arm.parquet
```
Run the command from the repository root. Use `--kind rl` for the corresponding
RL subset. The script exports the recorded membership rather than resampling.
`--source DIR` repackages a frozen, already labeled export; it does not rerun the
upstream cleaning or API annotation pipeline. Source revisions, file hashes,
and sample memberships are in [metadata/kodcode/](../metadata/kodcode/).
|