---
dataset_info:
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configs:
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  data_files:
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    path: data/train-*
  - split: test
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---

`JetBrains-Research/django_method_gen` is a code generation benchmark built from the Django codebase.

This dataset is used in the example of [IDEGym](https://github.com/JetBrains-Research/idegym/) usage for VERL-based RL training (see [`this repo`](https://github.com/JetBrains-Research/idegym/tree/main/examples/verl)).

Each example is a task to regenerate a single Python method that has been cut from its class. The dataset provides the surrounding class code, file imports, and docstrings as context. Reward is rule-based: the agent's submission is evaluated by running the original unit tests.

The dataset follows the VERL multi-turn format: each row contains a `prompt` (chat-style system + user messages), an `agent_name` field (`"idegym_django"`), and an `extra_info` blob carrying the raw task data passed to the IDEGym server — including the method body to recover, file context, and test metadata. There are 1,364 training examples and 100 test examples, spanning four difficulty levels.