For AI agents: a documentation index is available at the root level at /llms.txt. Append /llms.txt to any URL for a page-level index, or .md for the markdown version of any page.
LogoLogoNeMo Gym
DocumentationAPI Reference
DocumentationAPI Reference
  • About
    • Concepts
      • Environments
      • Evaluation
      • Training
      • Key Terminology
    • Architecture
    • Ecosystem
    • Release Notes
  • Get Started
    • Prerequisites
    • Installation
    • Quickstart
  • Prepare Data
    • Prepare and Validate
    • Download from Hugging Face
    • Prompt Config
  • Configure Agents
    • Integrate Existing Agents
    • Drive a Remote Agent
    • Agent Skills
  • Configure Models
    • OpenAI
    • Azure OpenAI
    • Inference Providers
    • Model-call capture
    • vLLM
    • Local vLLM
    • Local vLLM Proxy
    • Anthropic Messages
  • Build Verifiers
    • Verification Patterns
    • Multi-Reward Verification
  • Evaluate
    • Benchmarks
    • Browse Environments
    • Aggregate Metrics
    • Diagnose Results
  • Tutorials
    • Training Tutorials
    • Evaluation Tutorials
  • Build Environments
    • Resources Server APIs
    • Single-Step Environment
    • Multi-Step Environment
    • Stateful Environment
    • MCP Resources Server
    • Real-World Environment
    • Integrate external libraries
  • Model Recipes
    • Nemotron 3 Nano
    • Nemotron 3 Super
  • Infrastructure
    • Deployment Topology
    • Sandbox API
    • Engineering Notes
  • Reference
    • Configuration
    • RL Framework Compatibility
    • CLI Commands
    • FAQ
    • Trajectory capability matrix
  • Troubleshooting
    • Configuration Errors
  • Contribute
    • Development Setup
    • Environments
    • Integrate RL Frameworks
    • Agent Skills
  • About
  • Concepts
  • Environments
  • Evaluation
  • Training
  • Key Terminology
  • Architecture
  • Ecosystem
  • Release Notes
  • Prerequisites
  • Installation
  • Quickstart
  • Prepare Data
  • Prepare and Validate
  • Download from Hugging Face
  • Prompt Config
  • Configure Agents
  • Integrate Existing Agents
  • Drive a Remote Agent
  • Agent Skills
  • Configure Models
  • OpenAI
  • Azure OpenAI
  • Inference Providers
  • Model-call capture
  • vLLM
  • Local vLLM
  • Local vLLM Proxy
  • Anthropic Messages
  • Build Verifiers
  • Verification Patterns
  • Equivalence Match
  • Execution and State Match
  • LLM-as-Judge
  • Multi-Reward Verification
  • Evaluate
  • Benchmarks
  • Browse Environments
  • Aggregate Metrics
  • Diagnose Results
  • Training Tutorials
  • NeMo RL
  • About Workplace Assistant
  • Gym Configuration
  • Multi-Node Training
  • NeMo RL Configuration
  • Setup
  • Single Node Training
  • Unsloth
  • Multi-Environment Training
  • Training with VeRL
  • Offline Training (SFT/DPO)
  • Evaluation Tutorials
  • Evaluate EvalPlus
  • BLADE Analysis Skill
  • Reverify Rollouts
  • Build Environments
  • Resources Server APIs
  • Single-Step Environment
  • Multi-Step Environment
  • Stateful Environment
  • MCP Resources Server
  • Real-World Environment
  • Generating Training Data
  • Resources Server Implementation
  • Integrate external libraries
  • Model Recipes
  • Nemotron 3 Nano
  • Nemotron 3 Super
  • Infrastructure
  • Deployment Topology
  • Sandbox API
  • OpenSandbox Provider
  • Apptainer Provider
  • Docker Provider
  • ECS Fargate
  • Adding a Sandbox Provider
  • Engineering Notes
  • aiohttp vs httpx
  • Responses API
  • SWE RL Case Study
  • System Design
  • Reference
  • Configuration
  • RL Framework Compatibility
  • CLI Commands
  • FAQ
  • Trajectory capability matrix
  • Troubleshooting
  • Configuration Errors
  • Contribute
  • Development Setup
  • Environments
  • Add a benchmark
  • New Environment
  • Integrate RL Frameworks
  • Generation Backend
  • Integration Footprint
  • On-Policy Corrections
  • Success Criteria
  • Agent Skills
On this page
  • Concept Highlights
About

Concepts

||View as Markdown|

Understand what environments are, how they power evaluation, agent optimization, and training, and how the same components serve all three.


Concept Highlights

Each explainer below covers one foundational idea and links to deeper material.

Environments

Where the concept comes from, what components make up an environment, and how environments unify the evaluate-improve loop.

Evaluation

How environments are used to measure model and agent performance.

Training

Post-training techniques and how environments are used for training.

Key Terminology

Essential vocabulary for agent evaluation, policy-model training, RL workflows, and NeMo Gym.


Previous
NeMo Gym
Next
Environments
NVIDIANVIDIA
Developer-friendly docs for your API
Privacy Policy | Your Privacy Choices | Terms of Service | Accessibility | Corporate Policies | Product Security | Contact

Copyright © 2026, NVIDIA Corporation.