Configuration Reference#

Complete syntax and field specifications for NeMo Gym configuration files.

File Locations#

File	Location	Version Control
Server configs	`<server_type>/<implementation>/configs/*.yaml`	✅ Committed
env.yaml	Repository root (`./env.yaml`)	❌ Gitignored (user creates)

Server Configuration#

All servers share this structure:

server_id:                    # Your unique name for this server
  server_type:                # responses_api_models | resources_servers | responses_api_agents
    implementation:           # Directory name inside the server type directory
      entrypoint: app.py      # Python file to run
      # ... additional fields vary by server type

Model Server Fields#

policy_model:                                 # Server ID (use "policy_model" — agent configs expect this name)
  responses_api_models:                       # Server type (must be "responses_api_models" for model servers)
    openai_model:                             # Implementation (use "openai_model", "vllm_model", or "azure_openai_model")
      entrypoint: app.py                      # Python file to run
      openai_base_url: ${policy_base_url}     # API endpoint URL
      openai_api_key: ${policy_api_key}       # Authentication key
      openai_model: ${policy_model_name}      # Model identifier

Tip

Keep the server ID as policy_model — agent configs reference this name by default. The ${policy_base_url}, ${policy_api_key}, and ${policy_model_name} placeholders should be defined in env.yaml at the repository root, allowing you to change model settings in one place.

Resources Server Fields#

my_resource:                                  # Server ID (your choice — agents reference this name)
  resources_servers:                          # Server type (must be "resources_servers" for resources servers)
    example_single_tool_call:                   # Implementation (must match a directory in resources_servers/)
      entrypoint: app.py                      # Python file to run
      domain: agent                           # Server category (see values below)
      verified: false                         # Passed reward profiling and training checks (default: false)
      description: "Short description"        # Server description
      value: "What this improves"             # Training value provided

Domain values: math, coding, agent, knowledge, instruction_following, long_context, safety, games, e2e, other (see Domain)

Agent Server Fields#

Agent servers must include both a resources_server and model_server block to specify which servers to use.

my_agent:                                     # Server ID (your choice — used in API requests)
  responses_api_agents:                       # Server type (must be "responses_api_agents" for agent servers)
    simple_agent:                             # Implementation (must match a directory in responses_api_agents/)
      entrypoint: app.py                      # Python file to run
      resources_server:                       # Specifies which resources server to use
        type: resources_servers               # Always "resources_servers"
        name: my_resource                     # Server ID of the resources server
      model_server:                           # Specifies which model server to use
        type: responses_api_models            # Always "responses_api_models"
        name: policy_model                    # Server ID of the model server
      datasets:                               # Optional: define for training workflows
        - name: train                         # Dataset identifier
          type: train                         # example | train | validation
          jsonl_fpath: path/to/data.jsonl     # Path to data file
          license: Apache 2.0                 # Required for train/validation

Dataset Configuration#

Define datasets associated with agent servers for training and evaluation.

datasets:
  - name: my_dataset
    type: train
    jsonl_fpath: path/to/data.jsonl
    license: Apache 2.0
    num_repeats: 1

Field	Required	Description
`name`	Yes	Dataset identifier
`type`	Yes	`example`, `train`, or `validation`
`jsonl_fpath`	Yes	Path to data file
`license`	For train/validation	License identifier (see values below)
`num_repeats`	No	Repeat dataset n times (default: `1`)

Dataset types:

example — For testing and development
train — Training data (requires license)
validation — Evaluation data (requires license)

License values: Apache 2.0, MIT, Creative Commons Attribution 4.0 International, Creative Commons Attribution-ShareAlike 4.0 International, TBD (see license)

Local Configuration (env.yaml)#

Store secrets and local settings at the repository root. This file is gitignored.

# Policy model (required for most setups)
# Reference these variables in server configs using `${variable_name}` syntax (e.g., `${policy_base_url}`)
policy_base_url: https://api.openai.com/v1
policy_api_key: sk-your-api-key
policy_model_name: gpt-4o-2024-11-20

# Optional: store config paths for reuse
my_config_paths:
  - responses_api_models/openai_model/configs/openai_model.yaml
  - resources_servers/example_single_tool_call/configs/example_single_tool_call.yaml

# Optional: validation behavior
error_on_almost_servers: true   # Exit on invalid configs (default: true)

Command Line Usage#

Run servers using ng_run, NeMo Gym uses Hydra for configuration management.”

Loading Configs#

# Load one or more config files
ng_run "+config_paths=[config1.yaml,config2.yaml]"

# Use paths stored in env.yaml
ng_run "+config_paths=${my_config_paths}"

Overriding Values#

# Override nested values (use dot notation after server ID)
ng_run "+config_paths=[config.yaml]" \
    +my_server.resources_servers.my_impl.domain=coding

# Override policy model
ng_run "+config_paths=[config.yaml]" \
    +policy_model_name=gpt-4o-mini

# Disable strict validation
ng_run "+config_paths=[config.yaml]" +error_on_almost_servers=false