Datasets:
RL verification
Call the appropriate module's compute_score(data_source, solution_str, ground_truth), passing reward_model.ground_truth from an RL row.
| Family | Tests | Reward |
|---|---|---|
| KodCode | Python test module and named test functions | 1 only if every test passes; otherwise 0 |
| TACO/LeetCode | Standard-input or function-call cases | 1 for full pass; otherwise 0.8 × pass fraction |
The bundled code preserves the historical scorer. KodCode defaults to 1 second per test and 180 seconds per completion; TACO defaults to 2 and 6 seconds. Both default to 2 GiB memory and 16 MiB file-output limits. Training runs may override these arguments; record any changes when comparing results.
Execution requires Linux. Install verification/requirements.txt for the
third-party modules recorded by the KodCode tests; solutions may need other imports.
Process resource limits are not a security isolation boundary; run untrusted
generated code in an isolated container without credentials or network access.
Dataset loading and structural validation do not execute problem code.
python verification/smoke_test.py