Table of Contents

• Table of Contents
• NeMo-Gym
• Setup
  • Helpful development commands
• How To: Run a simple agent
  • TL;DR
  • Introduction
  • Configs
    • Special policy model placeholders
  • Running servers
  • OpenAI Responses vs Chat Completions API
  • Run tests for simple agent
• How To: Add a resource server
  • TLDR final expected artifacts
• How To: Upload and download a dataset from Gitlab
• How To: Offline rollout collection or synthetic data generation
• How To: Prepare and validate data for PR submission or RL training
• How To: ng_dump_config - Dump a YAML config as exactly as NeMo Gym sees it
• How To: Use NeMo Gym with a non-Responses compatible API endpoint like vLLM
• How To: Multi-verifier usage
• How To: Profile your resources server
• FAQ: DCO and commit signing VSCode and Git setup
• FAQ: SFT and RL
• FAQ: Error: Found files with missing copyright
• FAQ: build-docs / Build docs CI failures
• FAQ: NeMo Gym, training frameworks, and token IDs
• FAQ: NeMo Gym what CI/CD do I need to pass?
• FAQ: Why aiohttp backend and not httpx/httpcore for async http?

NeMo-Gym

Setup

Clone NeMo-Gym. It's recommended to clone via SSH if you are a developer.

git clone git@github.com:NVIDIA-NeMo/Gym.git
cd Gym

Install UV

curl -LsSf https://astral.sh/uv/install.sh | sh
source $HOME/.local/bin/env

Initialize environment

uv venv --python 3.12
source .venv/bin/activate

Install NeMo Gym

uv sync --extra dev --group docs

If you are a developer, install pre-commit hooks

pre-commit install

Helpful development commands

Run Nemo Gym tests

ng_dev_test

View test coverage

coverage html

Run tests for a single server e.g. responses_api_agents/simple_agent

ng_test +entrypoint=responses_api_agents/simple_agent

Run all server tests

ng_test_all

How To: Run a simple agent

Reading time: 10 mins Date: Mon Aug 04, 2025

TL;DR

After setup above:

echo "policy_base_url: https://api.openai.com/v1
policy_api_key: {your OpenAI API key}
policy_model_name: gpt-4.1-2025-04-14" > env.yaml

config_paths="resources_servers/simple_weather/configs/simple_weather.yaml,\
responses_api_models/openai_model/configs/openai_model.yaml"
ng_run "+config_paths=[${config_paths}]"

python responses_api_agents/simple_agent/client.py

Introduction

In this example, we will run a simple agent that uses the GPT 4.1 model and has access to a very simple dummy get_weather tool. NeMo Gym has three core abstractions: models, resources, and agents.

1. Models - found under responses_api_models, NeMo Gym's model abstraction contains OpenAI Chat Completions and Responses compatible interfaces. Models are intended to abstract out any model quirks, e.g. pointing to an OpenAI endpoint or a local VLLM instance, using a reasoning model or a non-reasoning model, using a model with different chat templating, etc, so that Agents can freely point to any model instance.
   1. Think “gpt 4.1”, “o3”, “claude sonnet”, “nano v2”, etc.
2. Resources - found under resources_servers, NeMo Gym's resource abstraction contains the environment including tool implementations or "step" functions like in OpenAI Gym, as well as any verification or reward logic. Resource servers are intended to abstract out any heavy processing that needs to be done, so that Agents can efficiently async and await on model and resource server calls.
   1. Think "FastAPI server" or "verifier".
3. Agents - found under responses_api_agents, NeMo Gym's agent abstraction contains an OpenAI Responses compatible interface. Agents are intended to abstract out any major system designs that sit on top of model and resource servers.
   1. Think “deep research agent”, “search agent”, “customer service agent”, “Claude code”, “math agent”, etc.

Configs

NeMo Gym operates using YAML configuration files and command line arguments via Hydra and OmegaConf. The rough skeleton of a config is annotated and shown below, using the simple agent config as an example responses_api_agents/simple_agent/configs/simple_agent.yaml.

# `simple_agent` here is the name or ID of this server and will be used to identify it in subsequent requests.
# If you spin up multiple servers, you must ensure that each name/ID is unique.
simple_agent:
  # This is the server type. There are 3 server types: responses_api_models, resources_servers, and responses_api_agents.
  # These server types are all held in the three folders in the top-level directory of NeMo-Gym, parallel to the nemo_gym folder.
  responses_api_agents:
    # This is the model/resource/agent type. This is custom and written by you.
    # This must be the name of the folder inside the server type folder.
    simple_agent:
      # This is the server entrypoint path, relative to the agent type folder. When your server is run, it will be run through here.
      entrypoint: app.py
      # Everything below here is a server-specific variable. In this case (as we will see in a second), there are two top-level variables `resources_server` and `model_server`.
      resources_server:
        type: resources_servers
        # This `???` is Hydra syntax for a required but missing field
        name: ???
      model_server:
        type: responses_api_models
        name: policy_model

This is how this YAML config translates to the simple agent config as defined in Python in responses_api_agents/simple_agent/app.py.

from nemo_gym.server_utils import ResourcesServerRef, ModelServerRef

class SimpleAgentConfig(BaseResponsesAPIAgentConfig):
    resources_server: ResourcesServerRef
    model_server: ModelServerRef

You can define your server configs to require or accept any arbitrary structures or values. In this case, we require two variables resources_server and model_server that are server reference objects. These server reference objects are how you can refer to one server from another server, in a server-instance agnostic way. For example, this SimpleAgentConfig doesn't need any model_server in particular, just a model_server.

If your config contains a server reference that doesn't exist, NeMo Gym will let you know e.g.:

AssertionError: Could not find type='responses_api_models' name='simple_model_server' in the list of available servers: [AgentServerRef(type='responses_api_agents', name='simple_agent'), ModelServerRef(type='responses_api_models', name='policy_model'), ResourcesServerRef(type='resources_servers', name='simple_weather')]

If your config is missing an argument or argument value, NeMo Gym will let you know e.g.:

omegaconf.errors.MissingMandatoryValue: Missing mandatory value: policy_model.responses_api_models.openai_model.openai_api_key
    full_key: policy_model.responses_api_models.openai_model.openai_api_key
    object_type=dict

Special policy model placeholders

There is one set of special NeMo Gym variables relating to the agent policy model. These are the policy_base_url, policy_api_key, policy_model_name variables. When you go to train a model, these are the information that will be used to query the model server endpoint you are trying to train. By default, every agent will refer to this shared policy_model model server.

policy_model:
  responses_api_models:
    openai_model:
      entrypoint: app.py
      openai_base_url: ${policy_base_url}
      openai_api_key: ${policy_api_key}
      openai_model_name: ${policy_model_name}

Running servers

In NeMo Gym, you run servers using the ng_run or nemo_gym_run bash commands. You can pass in configurations in three ways: as YAML config paths, as part of a local env.yaml file, or as part of command line args. For example, a run command might look like:

config_paths="responses_api_models/openai_model/configs/openai_model.yaml,\
resources_servers/simple_weather/configs/simple_weather.yaml"
ng_run "+config_paths=[$config_paths]" \
    +simple_weather_simple_agent.responses_api_agents.simple_agent.resources_server.name=simple_weather

We provide our Yaml config files using the config_paths command line argument. We specify 3 configs, one for our simple agent, which relies on our simple model server and simple weather servers. By default, the simple agent doesn't point to any specific resources server (see the resources_server... name: ??? above), so we provide this pointer via command line using Hydra syntax simple_agent.responses_api_agents.simple_agent.resources_server.name=simple_weather.

Our example relies on an OpenAI model server. We need to provide our OpenAI API key and other model information in order to properly run this example. At runtime, NeMo Gym will read from a local and git-ignored file at env.yaml. This env.yaml file is intended to hold sensitive information that should not be checked in, like API keys or other secrets. Create your env.yaml file in this directory, copy in the following information, and add your OpenAI API key.

policy_base_url: https://api.openai.com/v1
policy_api_key: {your OpenAI API key}
policy_model_name: gpt-4.1-2025-04-14

Please never commit any secrets in your config files! We explicitly provide a way to avoid this using the env.yaml. You should run touch env.yaml and your NeMo Gym folder should look like this i.e. if you run ls . you should see something like:

...
cache
data
nemo_gym
...
env.yaml
...

You can also use env.yaml to store config values for convenience e.g. in env.yaml:

simple_weather_config_paths:
- responses_api_agents/simple_agent/configs/simple_agent.yaml
- responses_api_models/openai_model/configs/openai_model.yaml
- resources_servers/simple_weather/configs/simple_weather.yaml

Then you can run NeMo Gym like:

ng_run '+config_paths=${simple_weather_config_paths}' \
    +simple_agent.responses_api_agents.simple_agent.resources_server.name=simple_weather

Config values will be resolved in the following order: Earlier config paths < later config paths < env.yaml < command line args.

After filling in your OpenAI API key, run the ng_run command below.

config_paths="resources_servers/simple_weather/configs/simple_weather.yaml,\
responses_api_models/openai_model/configs/openai_model.yaml"
ng_run "+config_paths=[${config_paths}]"

You should see an output that looks like this:

INFO:     Started server process [49744]
INFO:     Waiting for application startup.
INFO:     Application startup complete.
INFO:     Uvicorn running on http://127.0.0.1:11000 (Press CTRL+C to quit)
Audited 1 package in 6ms
Activate with: source .venv/bin/activate
Audited 1 package in 8ms
Audited 1 package in 248ms
INFO:     Started server process [49762]
INFO:     Uvicorn running on http://127.0.0.1:62922 (Press CTRL+C to quit)
INFO:     Started server process [49761]
INFO:     Uvicorn running on http://127.0.0.1:62920 (Press CTRL+C to quit)
INFO:     Started server process [49768]
INFO:     Uvicorn running on http://127.0.0.1:62921 (Press CTRL+C to quit)

Now we can query our agent.

python responses_api_agents/simple_agent/client.py

Inside the client.py file, we import the ServerClient class and instantiate a server_client. The server client is immediately usable to query our Responses API-compatible agent. This is also how you query servers from inside other servers at runtime.

from nemo_gym.openai_utils import NeMoGymResponseCreateParamsNonStreaming
from nemo_gym.server_utils import ServerClient

server_client = ServerClient.load_from_global_config()
server_client.post(
    server_name="simple_weather_agent",  # This is your server name or ID
    url_path="/v1/responses",
    json=NeMoGymResponseCreateParamsNonStreaming(...),
)
...

You should see an output like this:

[2025-08-04 20:35:19,983][httpx][INFO] - HTTP Request: POST http://127.0.0.1:62920/v1/responses "HTTP/1.1 200 OK"
[
    {
        "arguments": "{\"city\":\"San Francisco\"}",
        "call_id": "call_OnWAk719Jr3tte4OmCJtJOB4",
        "name": "get_weather",
        "type": "function_call",
        "id": "fc_68a3739f2f0081a1aae4b93d5df07c100cb216b5cc4adbc4",
        "status": "completed"
    },
    {
        "call_id": "call_OnWAk719Jr3tte4OmCJtJOB4",
        "output": "{\"city\": \"San Francisco\", \"weather_description\": \"The weather in San Francisco is cold.\"}",
        "type": "function_call_output",
        "id": null,
        "status": null
    },
    {
        "id": "msg_68a373a1099081a1bb265ecf3b26c0dc0cb216b5cc4adbc4",
        "content": [
            {
                "annotations": [],
                "text": "The weather in San Francisco tonight is cold. You might want to wear layers or bring a jacket to stay comfortable while you're out. Let me know if you want outfit advice or tips on where to go!",
                "type": "output_text",
                "logprobs": []
            }
        ],
        "role": "assistant",
        "status": "completed",
        "type": "message"
    }
]

When you run NeMo Gym, a head server will spin up that contains the single source of truth configuration for all of its servers. This header server is what the ServerClient.load_from_global_config() reads from in order to get information about how to query each individual server. This way, all hostnames and ports are abstracted away from any consumers of NeMo Gym. However, a host and port can still be specified for any server if the orchestrator wishes so. If no port is specified, a random one will be chosen by the operating system.

OpenAI Responses vs Chat Completions API

Agents and verifiers work with responses in a standardized format based on the OpenAI Responses API schema. The verifier receives an object where the output field conforms to the Response object output documented here.

The output list may contain multiple item types, such as:

• ResponseOutputMessage - The main user-facing message content returned by the model.
• ResponseOutputItemReasoning - Internal reasoning or "thinking" traces that explain the model’s thought process.
• ResponseFunctionToolCall - A request from the model to invoke an external function or tool.

Example If a chat completion contains both thinking traces and user-facing text:

ChatCompletion(
    Choices=[
        Choice(
            message=ChatCompletionMessage(
                content="<think>I'm thinking</think>Hi there!",
                tool_calls=[{...}, {...}],
                ...
            )
        )
    ],
    ...
)

In the Responses schema, this would be represented as:

Response(
    output=[
        ResponseOutputItemReasoning(
            type="reasoning",
            summary=[
                Summary(
                    type="summary_text",
                    text="I'm thinking",
                )
            ]
        ),
        ResponseOutputMessage(
            role="assistant",
            type="message",
            content=[
                ResponseOutputText(
                    type="output_text",
                    text="Hi there!",
                )
            ]
        ),
        ResponseFunctionToolCall(
            type="function_call",
            ...

        ),
        ResponseFunctionToolCall(
            type="function_call",
            ...

        ),
        ...
    ]
)

Reasoning traces (Reasoning items) are parsed before the verifier processes the output. The parsing is model-specific, and the verifier does not need to worry about the extracting or interpreting reasoning traces. The verifier receives these items already separated and clearly typed.

Run tests for simple agent

Run the Simple Chat Agent tests. ng_test or nemo_gym_test stands for Nemo Gym Test.

ng_test +entrypoint=responses_api_agents/simple_agent

Tests are strongly encouraged and you must have at least one test for every server you make. Test coverage is not explicitly required which means that YOU ARE RESPONSIBLE FOR YOUR OWN SERVER CORRECTNESS AND FUNCTION.

How To: Add a resource server

Reading time: 5 mins Date: Tue Aug 05, 2025

Resource servers are used to abstract out any business logic of tool implementations and verifiers. Each resource server must implement a verify function.

Resource servers live in the resources_servers folder. Initialize a resource server now. For this example, we will be writing a dummy test weather server.

ng_init_resources_server +entrypoint=resources_servers/test_weather

For the purposes of this example, we don't have any external dependencies, but if you want to add server-specific requirements, you would do so in the requirements.txt file. You can add requirements for external PyPI packages or Github repos.

-e nemo-gym[dev] @ ../../
{additional dependencies here}

Implement a tool for your agent to use in app.py. Start by adding your request and response schemas

...
class TestWeatherResourcesServerConfig(BaseResourcesServerConfig):
    pass


class GetWeatherRequest(BaseModel):
    city: str


class GetWeatherResponse(BaseModel):
    city: str
    weather_description: str


class TestWeatherResourcesServer(SimpleResourcesServer):
    config: TestWeatherResourcesServerConfig

...

Implement a get_weather function under the TestWeatherResourcesServer class. For now we will just always say it is cold.

...
        # app.post("/get_weather")(self.get_weather)

        return app

    async def get_weather(self, body: GetWeatherRequest) -> GetWeatherResponse:
        return GetWeatherResponse(
            city=body.city, weather_description=f"The weather in {body.city} is cold."
        )

    async def verify(self, body: BaseVerifyRequest) -> BaseVerifyResponse:
        return BaseVerifyResponse(**body.model_dump(), reward=1.0)
...

Register your new get_weather function as a FastAPI route.

...
    def setup_webserver(self) -> FastAPI:
        app = super().setup_webserver()

        # Additional server routes go here! e.g.:
        app.post("/get_weather")(self.get_weather)

        return app
...

You can see a complete example of app.py in resources_servers/simple_weather/app.py!

Run an agent with your new server!

config_paths="responses_api_agents/simple_agent/configs/simple_agent.yaml,\
responses_api_models/openai_model/configs/openai_model.yaml,\
resources_servers/simple_weather/configs/simple_weather.yaml"
ng_run "+config_paths=[$config_paths]" \
    +simple_agent.responses_api_agents.simple_agent.resources_server.name=test_weather

Run a query with your new resources server! Your agent should say that it's cold in SF :)

python responses_api_agents/simple_agent/client.py

After you implement your server, please make sure to update the README.md with appropriate licensing information! Your PR will not be merged unless licensing information is present and accurate.

Run the tests for your server

ng_test +entrypoint=resources_servers/simple_weather

You can also run detailed tests after running tests the first time

cd resources_servers/simple_weather
source .venv/bin/activate
pytest

At some point, you will want to actually add data that can be used to query your server. Please follow the instructions for How To: Prepare and validate data for PR submission or RL training.

If you need some dataset preprocessing or formatting scripts, please place them your resources server directory e.g. resources_servers/simple_weather/my_preprocess_script.py.

You are required to have the following 3 files in your resources server data folder:

1. example.jsonl - contains 5 example inputs to an agent server that uses your resources server. These examples need to be created on your own using whatever data processing script you want. It's highly suggested to store the data processing scripts in each folder if possible.
2. example_metrics.json - the metrics for the examples above, as output by ng_prepare_data in the data validation flow above.
3. example_rollouts.jsonl - rollouts through your resources server for the 5 example inputs in example.jsonl.

TLDR final expected artifacts

1. All the artifacts produced by ng_init_resources_server +entrypoint=resources_servers/test_weather. Your agent and resources server must be runnable.

multineedle_config_paths="responses_api_models/openai_model/configs/openai_model.yaml,\
resources_servers/multineedle/configs/multineedle.yaml"
ng_run "+config_paths=[${multineedle_config_paths}]"

2. At least 1 test at resources_servers/test_weather/tests/test_app.py.
3. 5 examples found at resources_servers/test_weather/data/examples.jsonl
4. Example metrics as output by ng_prepare_data found at resources_servers/test_weather/data/example_metrics.json

multineedle_config_paths="responses_api_models/openai_model/configs/openai_model.yaml,\
resources_servers/multineedle/configs/multineedle.yaml"
ng_prepare_data "+config_paths=[${multineedle_config_paths}]" \
    +output_dirpath=data/multineedle \
    +mode=example_validation

5. Example rollouts as output by ng_collect_rollouts found at resources_servers/test_weather/data/example_rollouts.jsonl

ng_collect_rollouts +agent_name=multineedle_simple_agent \
    +input_jsonl_fpath=resources_servers/multineedle/data/example.jsonl \
    +output_jsonl_fpath=resources_servers/multineedle/data/example_rollouts.jsonl \
    +limit=null \
    +num_repeats=null \
    +num_samples_in_parallel=null

How To: Upload and download a dataset from Gitlab

We want to track and version golden versions of our datasets so that we always know what data is being trained on and that the data we are training on is high quality. Major versions of all training datasets should be tracked in NeMo Gym. For example, the HelpSteer dataset https://huggingface.co/datasets/nvidia/HelpSteer3 has 3 major versions 1, 2, and 3. Each of these major versions would be uploaded and tracked in NeMo Gym.

Right now, NeMo Gym is hosted in Nvidia Gitlab and we use Gitlab's model artifact registry to store datasets. https://gitlab-master.nvidia.com/bxyu/nemo-gym/-/ml/models?first=30&orderBy=created_at&sort=desc#/

Gitlab uses MLFlow to interface with its model artifact registry. You will need:

1. The NeMo Gym repository Gitlab URI.
   1. Go to the Model Registry page, click the "..." next to "Create model", then click "Using the MLFlow client".
   2. The URI will look something like https://gitlab-master.nvidia.com/api/v4/projects/191584/ml/mlflow/
2. Your Gitlab token. Your Gitlab token must have the api and read_api scopes.

Provide your MLFlow credentials in env.yaml.

mlflow_tracking_uri: {your NeMo Gym Gitlab URI}
mlflow_tracking_token: {your Gitlab PAT}

Upload a dataset to Gitlab model artifact registry. Dataset name will be your model artifact name. Version must be a str in the format x.x.x.

ng_upload_dataset_to_gitlab \
    +dataset_name=multineedle \
    +version=0.0.1 \
    +input_jsonl_fpath=data/multineedle_benchmark.jsonl

Download a dataset from Gitlab model artifact registry.

ng_download_dataset_from_gitlab \
    +dataset_name=multineedle \
    +version=0.0.1 \
    +artifact_fpath=multineedle_benchmark.jsonl \
    +output_fpath=data/multineedle_benchmark.jsonl

How To: Offline rollout collection or synthetic data generation

Reading time: 5 mins Date: Tue Aug 05, 2025

NeMo Gym can be used for rollout collection e.g. for DPO or for synthetic data generation e.g. for SFT!

Spin up your agent. For this example, we will use the multineedle resources server!

config_paths="responses_api_models/openai_model/configs/openai_model.yaml,\
resources_servers/multineedle/configs/multineedle.yaml"
ng_run "+config_paths=[$config_paths]" \
    +simple_agent.responses_api_agents.simple_agent.resources_server.name=multineedle

Download the MultiNeedle data

ng_download_dataset_from_gitlab \
    +dataset_name=multineedle \
    +version=0.0.1 \
    +artifact_fpath=multineedle_benchmark.jsonl \
    +output_fpath=data/multineedle_benchmark.jsonl

Run rollout collection.

ng_collect_rollouts +agent_name=multineedle_simple_agent \
    +input_jsonl_fpath=data/multineedle_benchmark.jsonl \
    +output_jsonl_fpath=results/multineedle_rollout_collection.jsonl \
    +limit=null \
    +num_repeats=null \
    +num_samples_in_parallel=null \
    +responses_create_params.max_output_tokens=32_768

The supported parameters include:

• limit: Limits how many examples from the input JSONL file to process
• num_repeats: Repeats each input example multiple times to collect multiple rollouts per example
• num_samples_in_parallel: Controls how many rollout collection requests run concurrently
• responses_create_params: A dictionary of sampling parameter overrides.

View the rollouts just collected!

ng_viewer +jsonl_fpath=results/multineedle_rollout_collection.jsonl

How To: Prepare and validate data for PR submission or RL training

When you use ng_init_resources_server +entrypoint=resources_servers/multineedle to initialize a resources server, you will get a config.yaml that looks like the below code block. The dataset information for training, validation, and example will be inside the scope of your agent config (e.g. under simple_agent) and is a list of dataset objects.

multineedle_resources_server:
  resources_servers:
    multineedle:
      entrypoint: app.py
multineedle_simple_agent:
  responses_api_agents:
    simple_agent:
      entrypoint: app.py
      resources_server:
        type: resources_servers
        name: multineedle_resources_server
      model_server:
        type: responses_api_models
        name: policy_model
      datasets:
      - name: train
        type: train
        license: Apache 2.0
        jsonl_fpath: resources_servers/multineedle/data/train.jsonl
        num_repeats: 1
        gitlab_identifier:
          dataset_name: multineedle
          version: 0.0.1
          artifact_fpath: multineedle/train.jsonl
        license: Apache 2.0
      - name: validation
        type: validation
        license: Apache 2.0
        jsonl_fpath: resources_servers/multineedle/data/validation.jsonl
        num_repeats: 1
        gitlab_identifier:
          dataset_name: multineedle
          version: 0.0.1
          artifact_fpath: multineedle/validation.jsonl
        license: Apache 2.0
      - name: example
        type: example
        jsonl_fpath: resources_servers/multineedle/data/example.jsonl
        num_repeats: 1

A dataset object consists of:

• Name: An identifier for you
• Type: train, validation, or example. Train and validation are as used in NeMo RL or other train frameworks. More information about the example type is in the next section.
• Jsonl fpath: the local file path to your jsonl file for this dataset.
• Num repeats: optionally repeat each row when preparing or collating data. Defaults to 1 if unspecified.
• Gitlab identifier: The remote path to the dataset as held in the Gitlab dataset registry. This field is required for train and validation datasets. (Not required for example datasets since those are required to be committed to Git).
• License: The license of that dataset. Required for train and validation datasets and not required for example datasets, similar in principle to the Gitlab identifier.
• Start idx, end idx: used for slicing your dataset.

- name: train
  type: train
  jsonl_fpath: resources_servers/multineedle/data/train.jsonl
  gitlab_identifier:
    dataset_name: multineedle
    version: 0.0.1
    artifact_fpath: multineedle/validation.jsonl
  license: Apache 2.0

Each config.yaml in the resources server requires at least one agent with one example dataset. This example dataset is the first 5 rows of your train dataset that is used for sanity checks on the format for your dataset and the format of each individual example and for others to quickly understand your data.

For every PR that contributes data, we require common dataset statistics and sanity checks on the data itself. This process is also helpful to catch any simple issues before you ever train with NeMo RL. NeMo Gym provides a helper command ng_prepare_data to do so.

config_paths="resources_servers/multineedle/configs/multineedle.yaml,\
responses_api_models/openai_model/configs/openai_model.yaml"
ng_prepare_data "+config_paths=[$config_paths]" \
    +output_dirpath=data/multineedle \
    +mode=example_validation

# Run NeMo Gym servers the exact same way with the same configs!
ng_run "+config_paths=[$config_paths]"

The ng_prepare_data command will:

1. Attempt to load all the datasets you specified from disk. Missing datasets will be reported before any processing is done.
2. For each dataset, read example by example. Check the format and report the filepaths and indices/ranges of offending examples if any.
   1. We only require that the dataset has one key responses_create_params which is valid Responses API schema.
3. Compute aggregate statistics, print them to terminal, and save them next to the jsonl fpaths.
   1. Number of examples
   2. Avg/max/min number of tools
   3. Input length in terms of OpenAI tokens
   4. Avg/max/min number of turns
   5. Number of unique create params
   6. Avg/max/min temperature and other sampling params
   7. Number of unique user messages
4. Check that the aggregate statistics of individual datasets match those of existing aggregate statistics.
5. Collate all the examples into one final train and validation dataset jsonl files at the output dirpath specified for downstream NeMo RL or other train framework consumption.
6. The final aggregate statistics are reported and saved next to the train and validation datasets.
7. [NeMo RL train] Use the exact same config paths to ng_prepare_data and the train/validation dataset paths output in step 5. There is no special pre or post processing done in the NeMo Gym/RL integration other than shuffling and distributed data loading. What you see is what you get.

The ng_prepare_data command has 2 modes, one for actual train and validation set preparation, and one for example validation intended to sanity check your data format. You would typically run +mode=example_validation when first contributing a resources server, and then run with +mode=train_preparation when you actually go to train.

config_paths="resources_servers/multineedle/configs/multineedle.yaml,\
responses_api_models/openai_model/configs/openai_model.yaml"
ng_prepare_data "+config_paths=[$config_paths]" \
    +output_dirpath=data/multineedle \
    +mode=example_validation

How To: ng_dump_config - Dump a YAML config as exactly as NeMo Gym sees it

# Example ng_run command
config_paths="resources_servers/multineedle/configs/multineedle.yaml,\
responses_api_models/openai_model/configs/openai_model.yaml"
ng_run "+config_paths=[$config_paths]"


# Dump the exact yaml config that NeMo gym sees, just by swapping ng_run -> ng_dump_config
ng_dump_config "+config_paths=[$config_paths]"

How To: Use NeMo Gym with a non-Responses compatible API endpoint like vLLM

As of Sep 05, 2025, not many models have been trained with middlewares or chat templates that are easily parseable to OpenAI Responses API schema, with the notable exception of OpenAI's own open source model GPT-OSS. Since Gym is first-party Responses API, this makes Gym very difficult to use with basically any model.

As a result, we provide a Responses API to Chat Completions mapping middleware layer in the form of responses_api_models/vllm_model. VLLMModel assumes that you are pointing to a vLLM instance (since it relies on vLLM-specific endpoints like /tokenize and vLLM-specific arguments like return_tokens_as_token_ids).

To use VLLMModel, just change the responses_api_models/openai_model/configs/openai_model.yaml in your config paths to responses_api_models/vllm_model/configs/vllm_model.yaml!

config_paths="resources_servers/multineedle/configs/multineedle.yaml,\
responses_api_models/vllm_model/configs/vllm_model.yaml"
ng_run "+config_paths=[$config_paths]"

Here is an e2e example of how to spin up a NeMo Gym compatible vLLM Chat Completions OpenAI server.

• If you want to use tools, please find the appropriate vLLM arguments regarding the tool call parser to use. In this example, we use Qwen3-30B-A3B, which is suggested to use the hermes tool call parser.
• Important note: Please do NOT use a reasoning parser argument to vLLM here. The Responses to Chat Completions middleware logic needs to parse to and from Responses Reasoning items and Chat Completion Message content. Do NOT use things like --reasoning-parser qwen3.

uv venv --python 3.12 --seed 
source .venv/bin/activate
# hf_transfer for faster model download. datasets for downloading data from HF
uv pip install hf_transfer datasets vllm --torch-backend=auto

# Qwen/Qwen3-30B-A3B, usable in Nemo RL!
HF_HOME=.cache/ \
HF_HUB_ENABLE_HF_TRANSFER=1 \
    hf download Qwen/Qwen3-30B-A3B

HF_HOME=.cache/ \
HOME=. \
vllm serve \
    Qwen/Qwen3-30B-A3B \
    --dtype auto \
    --tensor-parallel-size 4 \
    --gpu-memory-utilization 0.9 \
    --enable-auto-tool-choice --tool-call-parser hermes \
    --host 0.0.0.0 \
    --port 10240

How To: Multi-verifier usage

Gym is explicitly designed to support multi-verifier training.

Let's say you want to use both math and search verifiers. Normally how you spin up the servers individually is: For math:

config_paths="responses_api_models/openai_model/configs/openai_model.yaml,\
resources_servers/library_judge_math/configs/bytedtsinghua_dapo17k.yaml"
ng_run "+config_paths=[${config_paths}]"

For search:

config_paths="responses_api_models/openai_model/configs/openai_model.yaml,\
resources_servers/google_search/configs/google_search.yaml"
ng_run "+config_paths=[$config_paths]"

If you want to use them both you would just add the yamls together like:

config_paths="responses_api_models/openai_model/configs/openai_model.yaml,\
resources_servers/library_judge_math/configs/bytedtsinghua_dapo17k.yaml,\
resources_servers/google_search/configs/google_search.yaml"
ng_run "+config_paths=[$config_paths]"

The same process goes for data preparation and downstream training framework Gym configuration, you would just add additional server configs.

How To: Profile your resources server

For large scale verifier training, it's critical that your resources server is as efficient as possible. It may be slammed with 16k concurrent requests or more. Gym provides easy tools to profile and understand the efficiency of your servers.

In one terminal, start your agent, model, and resources servers, with profiling enabled.

• profiling_enabled (bool): whether profiling is enabled or not. By default this is disabled since it incurs some slight overhead we don't want at runtime.
• profiling_results_dirpath (str): The directory to save all server profiling results in. Previous logs for the same will be overwritten in the same directory.

config_paths="responses_api_models/openai_model/configs/openai_model.yaml,\
resources_servers/library_judge_math/configs/bytedtsinghua_dapo17k.yaml"
ng_run "+config_paths=[${config_paths}]" \
    +profiling_enabled=true \
    +profiling_results_dirpath=results/profiling/library_judge_math

In another terminal, run some large number of rollouts against your servers. Use the limit and num_repeats flags to adjust the number of samples you want to run.

ng_collect_rollouts +agent_name=library_judge_math_simple_agent \
    +input_jsonl_fpath=resources_servers/library_judge_math/data/dapo17k_bytedtsinghua_train.jsonl \
    +output_jsonl_fpath=temp/library_judge_math_rollouts.jsonl \
    +limit=1024 \
    +num_repeats=1

After ng_collect_rollouts finishes, ctrl+c to quit your servers. You should see some output in the terminal like this:

The log file content for a server will look something like the following:

name                                                                                                                      ncall       tsub      ttot      tavg      
.../nemo-gym/resources_servers/library_judge_math/app.py:118 LibraryJudgeMathResourcesServer.verify                       1024        0.009755  17.98387  0.017562
.../nemo-gym/resources_servers/library_judge_math/app.py:145 LibraryJudgeMathResourcesServer._verify_answer               1024        0.002933  17.87998  0.017461
.../nemo-gym/resources_servers/library_judge_math/app.py:173 LibraryJudgeMathResourcesServer._verify_answer_with_library  1024        0.007851  17.87704  0.017458
.../nemo-gym/resources_servers/library_judge_math/app.py:191 <genexpr>                                                    2339        0.001695  0.029082  0.000012
.../nemo-gym/resources_servers/library_judge_math/app.py:163 _mute_output                                                 2048        0.007473  0.016538  0.000008

• ncall: number of calls (how many times the function/subroutine was invoked).
  • The LibraryJudgeMathResourcesServer.verify function was invoked 1024 times.
• tsub: time spent inside the subroutine itself, excluding calls to other functions (sometimes called "self time").
  • The LibraryJudgeMathResourcesServer.verify function itself accounted for only 0.009755s of time.
• ttot: total time spent in the subroutine, including all the functions it called.
  • The LibraryJudgeMathResourcesServer.verify function and all functions it called including _verify_answer, etc accounted for a total of 17.98387s.
• tavg: average time per call (often ttot / ncall).
  • The LibraryJudgeMathResourcesServer.verify function took 0.017562s per call on average.

FAQ: DCO and commit signing VSCode and Git setup

Here are some suggestions for easier development using the VSCode code editor.

VSCode workspace settings at .vscode/settings.json

{
    "git.enableCommitSigning": true,
    "git.alwaysSignOff": true
}

Set up your Github signing keys! https://docs.github.com/en/authentication/managing-commit-signature-verification/about-commit-signature-verification#ssh-commit-signature-verification

Specifically, if you visit https://github.com/settings/keys while logged into your account, you should see the following:

1. Under the "SSH keys" major section, there are 2 subsections
   1. Authentication keys
   2. Signing key

More often than node, the SHA256 displayed by Github (SHA256:xxxx) should be the same for the two keys above since you probably want to just use the same SSH key for both purposes. If you do not see the following, please following the signing keys link above!

For developers that sign commits via SSH keys, this is configuration so that VSCode source control is able to sign commits properly!

git config gpg.format ssh
git config user.signingkey ~/.ssh/id_ed25519.pub

FAQ: SFT and RL

Reading time: 5 mins Date: Fri Aug 15, 2025

SFT (supervised fine tuning) and RL (reinforcement learning) are two different ways of optimizing your model for different tasks and each have their own use cases.

Let's say you wanted to train your model to be really good at math.

• For SFT, you would take some input math questions and either ask human annotators to provide a gold response, or run it through a stronger teacher model and get your SFT target. And then you would SFT on these input + gold response pairs.
• For RL, you would take some input math questions and implement a way to score model answers. During RL training, you would ask the model you are trying to train these math questions, score the model responses using your scorer, and use the scores as a signal on how to optimize your model. Model responses with higher scores would be encouraged.

One way I like to think about these things is:

• You can do RL on SFT data, where your input is your SFT input, and the model answer scorer is just an exact match on the SFT gold label.
• You can also do SFT on RL data via synthetic data generation, where you run your inputs into some strong teacher model, score the responses, and use the scores to pick your SFT gold label.

Tying back to NeMo Gym, NeMo gym can be used to create synthetic data for SFT training by running strong teacher models on the different environments. Critically, it will also be used as the source of data during RL training.

FAQ: Error: Found files with missing copyright

If you get an error like this on your PR:

Error: Found files with missing copyright:
path= ./resources_servers/comp_coding/scripts/validate_dataset.py
path= ./resources_servers/comp_coding/scripts/build_examples.py
path= ./resources_servers/comp_coding/app.py

Please add the following copyright snippet to the top of the files listed:

# Copyright (c) 2025, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

FAQ: build-docs / Build docs CI failures

If you see some docs building related errors that are kind of cryptic regarding .rst files like

updating environment: [config changed ('toc_object_entries_show_parents')] 16 added, 0 changed, 0 removed
reading sources... [100%] index
/Users/bxyu/Documents/nemo-gym/nemo_gym/server_utils.py.rst:3: WARNING: Document headings start at H2, not H1 [myst.header]
/Users/bxyu/Documents/nemo-gym/nemo_gym/server_utils.py.rst:3: WARNING: Document headings start at H2, not H1 [myst.header]
/Users/bxyu/Documents/nemo-gym/README.md:: WARNING: image file not readable: resources/rl_verifiers_system_design.png [image.not_readable]
looking for now-outdated files... none found
pickling environment... done
checking consistency... done

You may need to reformat some of your docstrings to Napoleon format docstrings https://sphinxcontrib-napoleon.readthedocs.io/en/latest/

FAQ: NeMo Gym, training frameworks, and token IDs

One of the goals of NeMo Gym is to act as a rollout tool for LLM post-training, either as synthetic data generation for SFT or as training environments for RL.

RL training frameworks don't typically operate in OpenAI schema; they operate in tokens IDs. It is especially critical to always have the correct token IDs during training so that we stay on-policy and to make sure that what we think the model sees is what the model actually sees. However, when providing this OpenAI schema compatible interface to training environment developers, we lose track of the token IDs in Gym.

For example, say we are training a Qwen 3 family model. During rollouts, the model may sample from the entire token distribution. The token IDs are then decoded into text and subsequently converted to OpenAI schema and returned to the training environment developer. At some point for multi-step and multi-turn scenarios, the training environment developer will call the model again with the previously output OpenAI schema. This re-tokenization causes problems since a single string may map to multiple possible sequences of token IDs. So if the model generations token ID sequence 1 and the re-tokenization outputs token ID sequence 2, suddenly things may become off policy when the Gym result is consumed by the RL training framework.

So, the OpenAI compatible model server in a training framework needs to be able to handle this discrepancy. In order to do that, Gym needs a handle on the ground truth token IDs and it needs to provide that information back to the training frameworks' OpenAI compatible server.

TODO @bxyu-nvidia: expand on this later.

FAQ: NeMo Gym what CI/CD do I need to pass?

NeMo Gym has an E2E suite of CI/CD in the form of Github actions workflows. Some of these are critical to PR merge and some of the mare not.

For the majority of PRs, there are 5 checks that need to pass:

1. DCO
2. Code linting / Lint check (pull_request)
3. Copyright check / copyright-check / main (pull_request)
4. Secrets detector / secrets-detector / secrets-detector (pull_request)
5. Unit tests / Test (pull_request)

Examples of PR checks that most PRs do not need to wait for to pass:

1. CICD NeMo / cicd-container-build / build / main (push)
2. CICD NeMo / Nemo_CICD_Test (push) ...

FAQ: Why aiohttp backend and not httpx/httpcore for async http?

TL;DR: httpx is O(n^2) runtime where n is the number of queued requests (i.e. for each request, we check all other queued requests). This is terribly inefficient and results in major slowdowns.

On Wed Sep 17, 2025, inspired by the Deepseek R1 Nature paper, we tried launching a larger rollout batch run with up to 16 off policy steps in NeMo RL. Our setting resulted in Gym being slammed with 16k concurrent requests. At the time, we were using a single Gym instance with multiple data-parallel vLLM workers, and that setup hung for 40 minutes before the first request was processed. Something was wrong.

Before that time, we had also gotten reports that the rollout collection in Gym couldn't be used with high concurrency i.e. in some cases people had to set the concurrency to 32 requests in parallel. Putting these two data points together, we figured something was wrong with the concurrency setup in Gym.

For some context, Gym is a set of servers that end up calling a model endpoint server at some point. And it's really important that we never artificially restrict the concurrency in the Gym side since technically we are always clients of that model endpoint server, since the model endpoint server could handle many more requests than we're restricting the concurrency to. So we always want Gym to be as efficient as possible and not have e.g. max parallel requests or smth parameter in Gym.

Eventually, we isolated the issue to our async http backend -- httpx and httpcore. We originally decided to use httpx for the async http backend in Gym because the OpenAI client uses it by default so we can share the same backend http client. Unfortunately, the httpcore connection pool subroutine for pooling connections over requests is O(n^2) where n is the number of queued requests.

Networking mental model:

1. A request is sent by Gym to the model endpoint server.
2. This request requires a connection from our client side to the server side.
   1. This connection is a socket (identified by a port) and a socket is an open file (managed by the operating system).
   2. If we are sending 100 requests, in the worst case we could open 100 connections == 100 open files. This quickly becomes very expensive.
   3. So, async http backends will pool requests across connections to a single endpoint, where multiple requests can leverage the same file if they are going to the same endpoint origin.
   4. This is called connection pooling. And it's possible that all 100 requests share a single connection.
3. But this connection pooling now needs some management logic. When the client sends a new request, it needs to determine if that request can reuse an existing connection.
   1. And this is where the httpcore connection pool logic is very inefficient.

Here are the key calls in the stack trace:

1. OpenAI client at some point calls httpx client
2. httpx client calls into the transport here
3. Transport calls into httpcore connection pool here
4. For each request, the httpcore connection pool calls this _assign_requests_to_connections subroutine here
   1. This subroutine loops through connections here
   2. and loops through queued requests here
   3. Which results in a total of O(n^2) runtime if the number of queued requests is large. Which is always the case if we slam with some larger number of requests.

In the end, we decided to swap our http backend from httpx to aiohttp since we had good prior experience working with aiohttp in production infra.

Here are some Github issues related to this problem. They didn't help too much, but they did validate our solution (kind of) to use aiohttp as as async http backend instead.

• openai/openai-python#1596
• encode/httpx#3215 (comment)

If you are using AsyncOpenAI client with a parallelism > 32, you may also want to check if this kind of inefficiency also affects your setup.