| --- |
| 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](https://github.com/volcengine/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`](https://huggingface.co/datasets/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: |
| |
| 1. **Everything in MBPP+** (378 problems). MBPP+ is derived from MBPP-sanitized |
| and consumes 378 of that config's 427 problems. |
| 2. **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**: |
|
|
| ```json |
| {"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`](https://huggingface.co/datasets/google-research-datasets/mbpp) (configs `full` and `sanitized`) |
| - [`evalplus/mbppplus`](https://huggingface.co/datasets/evalplus/mbppplus) |
| |
