README.md

---
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+ |

## Prompt

Each row is a two-turn chat asking for reasoning inside an explicit
`<think></think>` region, then a fenced Python block:

```
system: Please reason step by step inside a <think> </think> block, then put
        your final solution in a single ```python code block.
user:   <task>
        Your implementation should pass tests like the following ...
        First reason step by step inside <think> and </think>. Then write your
        complete solution in a single markdown fenced code block ...
```

The delimiter is deliberate. Without it, reasoning is unmarked prose and the
only signal about it is total response length, which conflates "stopped
reasoning" with "wrote shorter code". Measured on Qwen2.5-3B during RL on this
data, mean response length fell from ~446 tokens at step 9 to 54 by step 95
while training reward held at 0.83: MBPP is easy enough that reasoning earns no
extra reward, so GRPO strips it. The tag does not prevent that collapse, but it
makes the think region separately measurable.

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

## 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). 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),
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]`.

## 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)