> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://docs.nvidia.com/nemo/gym/v0.7.0/data/llms.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/nemo/gym/_mcp/server.

# Prepare Data

NeMo Gym datasets use JSONL format for reinforcement learning (RL) training. Each dataset connects to an **agent server** (orchestrates agent-environment interactions) which routes requests to a **resources server** (provides tools and computes rewards).

## Prerequisites

* **NeMo Gym installed**: See [Installation](/get-started/installation)
* **Repository cloned** (for built-in datasets):
  ```bash
  git clone https://github.com/NVIDIA-NeMo/Gym.git
  cd Gym
  ```

> **Note**
>
> NeMo Gym uses OpenAI-compatible schemas for model server compatibility. **No OpenAI account required**—local servers like vLLM use the same format.

## Data Format

Each JSONL line requires a `responses_create_params` field following the [OpenAI Responses API schema](https://platform.openai.com/docs/api-reference/responses/create):

```json
{"responses_create_params": {"input": [{"role": "user", "content": "What is 2+2?"}]}}
```

Additional fields like `expected_answer` vary by resources server—the component that provides tools and reward signals.

### Required Fields

| Field                     | Added By              | Description                                                                                               |
| ------------------------- | --------------------- | --------------------------------------------------------------------------------------------------------- |
| `responses_create_params` | User                  | Input to the model during training. Contains `input` (messages) and optional `tools`, `temperature`, etc. |
| `agent_ref`               | `gym dataset collate` | Routes each row to its agent server. Auto-generated during data preparation.                              |

### Optional Fields

| Field             | Description                                    |
| ----------------- | ---------------------------------------------- |
| `expected_answer` | Ground truth for verification (task-specific). |
| `question`        | Original question text (for reference).        |
| `id`              | Tracking identifier.                           |

> **Tip**
>
> Check `resources_servers/<name>/README.md` for fields required by each resources server's `verify()` method.

### The `agent_ref` Field

The `agent_ref` field maps each row to a specific agent server, which in turn knows its resources server from the YAML config. A training dataset can blend multiple agent servers in a single file—`agent_ref` tells NeMo Gym which server handles each row.

```json
{
  "responses_create_params": {"input": [{"role": "user", "content": "..."}]},
  "agent_ref": {"type": "responses_api_agents", "name": "math_with_judge_simple_agent"}
}
```

**You don't create `agent_ref` manually.** The `gym dataset collate` tool adds it automatically based on your config file. The tool matches the agent type (`responses_api_agents`) with the agent name from the config.

### Example Data

```json
{"responses_create_params": {"input": [{"role": "user", "content": "What is 2+2?"}]}, "expected_answer": "4"}
{"responses_create_params": {"input": [{"role": "user", "content": "What is 3*5?"}]}, "expected_answer": "15"}
{"responses_create_params": {"input": [{"role": "user", "content": "What is 10/2?"}]}, "expected_answer": "5"}
```

## Quick Start

Run this command from the repository root:

```bash
gym dataset collate \
    --config responses_api_models/vllm_model/configs/vllm_model_for_training.yaml \
    --resources-server example_multi_step \
    --output-dir data/test \
    --mode example_validation
```

**Success**: `Finished!` message and `data/test/example_metrics.json` created.

## Dataset Types

| Type         | Purpose                    | License      |
| ------------ | -------------------------- | ------------ |
| `example`    | Testing and development    | Not required |
| `train`      | RL training data           | Required     |
| `validation` | Evaluation during training | Required     |

## Configuration

Define datasets in your agent server's YAML config:

```yaml
datasets:
  - name: train
    type: train
    jsonl_fpath: resources_servers/workplace_assistant/data/train.jsonl
    # Unified dataset source. `type` selects the backend; the other fields are backend-specific.
    source:
      type: huggingface
      repo_id: nvidia/Nemotron-RL-agent-workplace_assistant
      artifact_fpath: train.jsonl
    license: Apache 2.0
```

| Field         | Required         | Description                                                        |
| ------------- | ---------------- | ------------------------------------------------------------------ |
| `name`        | Yes              | Dataset identifier                                                 |
| `type`        | Yes              | `example`, `train`, or `validation`                                |
| `jsonl_fpath` | Yes              | Path to data file                                                  |
| `license`     | Train/validation | See valid values below                                             |
| `source`      | No               | Where to fetch the data from when it's missing locally (see below) |
| `num_repeats` | No               | Repeat count (default: `1`)                                        |

### Dataset `source`

`source` is the unified way to declare where a dataset is fetched from. `type` selects the backend; the remaining fields are backend-specific:

```yaml
# Hugging Face Hub
source:
  type: huggingface
  repo_id: nvidia/Nemotron-RL-agent-workplace_assistant
  artifact_fpath: train.jsonl

# GitLab dataset registry
source:
  type: gitlab
  dataset_name: example_multi_step
  version: 0.0.1
  artifact_fpath: train.jsonl
```

> **Note**
>
> The legacy `huggingface_identifier:` / `gitlab_identifier:` blocks still work (a deprecation warning is emitted), so existing configs keep running — but new configs should use `source:`.

### Valid Licenses

`Apache 2.0` · `MIT` · `GNU General Public License v3.0` · `Creative Commons Attribution 4.0 International` · `Creative Commons Attribution-ShareAlike 4.0 International` · `TBD` · `NVIDIA Internal Use Only, Do Not Distribute`

## Workflow

```mermaid
flowchart LR
    A[Create JSONL] --> B[Add to config]
    B --> C[Run gym dataset collate]
    C -->|Pass| D[Train with NeMo RL]
    C -->|Fail| E[Fix and retry]
```

## Validation Modes

| Mode                 | Scope                  | Use Case                         |
| -------------------- | ---------------------- | -------------------------------- |
| `example_validation` | `example` datasets     | Format check before contributing |
| `train_preparation`  | `train` + `validation` | Full prep for RL training        |

To prepare training data with auto-download:

```bash
gym dataset collate \
    --config responses_api_models/vllm_model/configs/vllm_model_for_training.yaml \
    --resources-server workplace_assistant \
    --output-dir data/workplace_assistant \
    --mode train_preparation \
    --download
```

> **Tip**
>
> HuggingFace downloads require authentication. Set `hf_token` in `env.yaml` or export `HF_TOKEN`.

## Common Errors

| Error                                      | Cause           | Fix                                      |
| ------------------------------------------ | --------------- | ---------------------------------------- |
| `JSON parse error at line N`               | Invalid JSON    | Check quotes, commas, brackets at line N |
| `ValidationError: responses_create_params` | Missing field   | Add `responses_create_params.input`      |
| `A license is required`                    | Missing license | Add `license` to dataset config          |
| `Missing local datasets`                   | File not found  | Check path or add `--download`           |

## Guides

#### [Prepare and Validate](/data/prepare-validate)

Full data preparation workflow.

data-prep

#### [Download from Hugging Face](/data/download-huggingface)

Fetch datasets from HuggingFace Hub.

huggingface

#### [Prompt Config](/data/prompt-config)

YAML-based prompt templates applied at rollout time.

prompts

## CLI Commands

| Command                | Description                   |
| ---------------------- | ----------------------------- |
| `gym dataset collate`  | Validate and generate metrics |
| `gym dataset download` | Download from HuggingFace     |

See [CLI Commands](/reference/cli-commands) for details.

## Large Datasets

* Validation streams line-by-line (memory-efficient)
* Single-threaded; >100K samples may take minutes
* Use `num_repeats` instead of duplicating JSONL lines

## Docs

- [Prepare and Validate](https://docs.nvidia.com/nemo/gym/v0.7.0/data/prepare-validate.md): 
- [Download from Hugging Face](https://docs.nvidia.com/nemo/gym/v0.7.0/data/download-huggingface.md): 
- [Prompt Config](https://docs.nvidia.com/nemo/gym/v0.7.0/data/prompt-config.md):