docs/kodcode.md

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.

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.

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/.