Datasets:

RL-Forgetting-Experiments-3
/

mbpp-code-rl

license: cc-by-4.0
task_categories:
  - text-generation
language:
  - en
tags:
  - code
  - rlvr
  - reinforcement-learning
  - mbpp
  - verl
size_categories:
  - n<1K
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train.parquet
      - split: test
        path: data/test.parquet
      - split: heldout_mbpp_test
        path: data/heldout_mbpp_test.parquet

MBPP for code RL (deduplicated against MBPP+)

MBPP prepared for RLVR training in verl, with two independent hold-outs so both MBPP+ and MBPP's own canonical test split stay reportable after training on this data.

split	rows	contents
`train`	320	MBPP canonical train + validation + prompt, minus everything in MBPP+
`test`	378	exactly the problems in `evalplus/mbppplus`
`heldout_mbpp_test`	276	MBPP's canonical test split (task_id 11-510) that is not in MBPP+

Why 320 and not 974

MBPP full is 974 problems across four canonical splits (prompt 10, test 500, validation 90, train 374). Two things are removed from the training pool:

Everything in MBPP+ (378 problems). MBPP+ is derived from MBPP-sanitized and consumes 378 of that config's 427 problems.
MBPP's canonical test split. Deduplicating against MBPP+ alone would leave 596 training problems, but 276 of those are canonical MBPP test — fine if MBPP+ is your only benchmark, fatal if you ever want standard MBPP numbers.

Deduplicating MBPP-sanitized against MBPP+ leaves only 49 problems, which is why the sanitized config alone is not viable for training. Each problem here uses its sanitized text and corrected tests where one exists (427 of 974 do) while keeping the other 547 — sanitized quality at usable size.

Leakage is asserted at build time: no train task_id appears in MBPP+, and no train row comes from a withheld split.

Format

verl RLHF layout: data_source, prompt, ability, reward_model, extra_info.

reward_model.ground_truth is a JSON string:

{"assert_case": ["assert floor_Min(10,20,30) == 15", "..."]}

The verifier dispatches on the keys of that dict, so one reward path handles MBPP asserts and competitive-programming stdin/stdout with no dataset-specific routing. Setup imports (test_setup_code / test_imports) are folded into each assert string, because every test runs as a standalone script: solution + "\n" + assert_case[i].

The prompt is a two-turn chat whose system message matches the math/kk runs of the same study, so prompt format is not a confound across tasks:

system: Please reason step by step and put your final solution in a single ```python code block.
user:   <task> + half the tests as an interface hint + fenced-block instruction

Half the tests (floor(N/2)) are shown so the model can infer the function name and signature; all tests are used for reward.

Validation

All 974 MBPP reference solutions score 1.0 through the verifier.

Sources

google-research-datasets/mbpp (configs full and sanitized)
evalplus/mbppplus