ReinforceNow CLI Reference
The rnow CLI manages RLHF training projects on the ReinforceNow platform.
Installation
bash
pip install rnow
Command Overview
| Command | Description |
|---|---|
rnow login | Authenticate with the platform |
rnow logout | Remove credentials |
rnow status | Check auth and running jobs |
rnow orgs | Manage organizations |
rnow init | Create new project from template |
rnow run | Submit training run |
rnow stop | Cancel active run |
rnow test | Test rollouts locally |
rnow download | Download trained model |
rnow login
Authenticate using OAuth device flow.
bash
rnow login [OPTIONS]
| Option | Description |
|---|---|
--force | Force new login even if already authenticated |
--api-url URL | Custom API base URL |
Example:
bash
rnow login # Opens browser for authentication # Stores credentials in ~/.reinforcenow/credentials.json
rnow logout
Remove stored credentials.
bash
rnow logout
rnow status
Check authentication status and running jobs.
bash
rnow status
Output:
code
Logged in as: user@example.com Organization: My Team (org_abc123) Active runs: 2 - run_xyz789 (running) - Math Training - run_def456 (queued) - Code Agent
rnow orgs
List or select organizations.
bash
# List all organizations rnow orgs # Select an organization rnow orgs ORG_ID
Example:
bash
rnow orgs # Output: # * org_abc123 - My Team (owner) # org_def456 - Other Team (member) rnow orgs org_def456 # Switched to: Other Team
rnow init
Initialize a new project from a template.
bash
rnow init [OPTIONS]
| Option | Description |
|---|---|
--template NAME | Template to use (see below) |
--name NAME | Project name (prompts if not provided) |
Available Templates
| Template | Type | Description |
|---|---|---|
start | RL | Default single-turn RL (alias for rl-single) |
rl-single | RL | Single-turn with math reasoning |
rl-tools | RL | Multi-turn with tool calling |
sft | SFT | Supervised finetuning |
tutorial-reward | RL | Learn reward functions |
tutorial-tool | RL | Learn tool functions |
mcp-tavily | RL | External MCP server (web search) |
deepseek-aha | RL | DeepSeek aha-moment training |
finqa | RL | Financial QA |
convfinqa | RL | Conversational financial QA |
quantqa | RL | Quantitative finance |
new | RL | Minimal template |
blank | - | Empty (config only) |
Examples:
bash
# Create SFT project rnow init --template sft --name "my-sft-project" # Create RL project with tools rnow init --template rl-tools # Create from tutorial rnow init --template tutorial-reward
Generated Files
| Template | Files |
|---|---|
sft | config.yml, train.jsonl |
rl-single | config.yml, train.jsonl, rewards.py, requirements.txt |
rl-tools | config.yml, train.jsonl, rewards.py, tools.py, requirements.txt |
blank | config.yml |
rnow run
Submit project for training.
bash
rnow run [OPTIONS]
| Option | Description |
|---|---|
--dir PATH | Project directory (default: current) |
--name NAME | Custom run name |
Required files:
- •
config.yml- Configuration - •
train.jsonl- Training data - •
rewards.py- Reward functions (RL only)
Optional files:
- •
tools.py- Tool definitions - •
requirements.txt- Python dependencies
Example:
bash
cd my-project rnow run # Output: # Validating project... # Uploading files... # Starting run: run_abc123xyz # View at: https://www.reinforcenow.ai/runs/run_abc123xyz
rnow stop
Cancel an active training run.
bash
rnow stop RUN_ID
Example:
bash
rnow stop run_abc123xyz # Are you sure you want to stop run_abc123xyz? [y/N]: y # Run stopped. # Duration: 2h 15m # Cost: $12.50
rnow test
Test RL rollouts locally before submitting.
bash
rnow test [OPTIONS]
| Option | Default | Description |
|---|---|---|
-d, --dir PATH | . | Project directory |
-n, --num-rollouts N | 1 | Number of rollouts |
--entry INDICES | random | Test specific entries (e.g., "0,2,5") |
--model MODEL | config | Override model for testing |
Examples
Basic test:
bash
rnow test # Runs 1 rollout, shows reward breakdown
Multiple rollouts:
bash
rnow test -n 5
Test specific entries:
bash
rnow test --entry 0,3,7 # Tests entries at indices 0, 3, and 7 from train.jsonl
Override model:
bash
rnow test --model gpt-5-nano -n 3 # Uses gpt-5-nano instead of config.model.path
Test Output
code
Rollout 1/3 Entry: 0 Prompt: What is 2+2? Turn 1: Assistant: The answer is 4. Rewards: accuracy: 1.0 format_check: 1.0 Total: 1.0 --- Rollout 2/3 ...
rnow download
Download a trained model checkpoint.
bash
rnow download RUN_ID [OPTIONS]
| Option | Default | Description |
|---|---|---|
-o, --output DIR | ./model | Output directory |
Example:
bash
rnow download run_abc123xyz -o ./my-model # Downloading checkpoint... # Progress: 100% # Saved to: ./my-model/