rnow-cli

name	rnow-cli
description	Use the ReinforceNow CLI for RLHF training. Use when running rnow commands, initializing projects, submitting training runs, testing rollouts, or downloading models.

ReinforceNow CLI Reference

The rnow CLI manages RLHF training projects on the ReinforceNow platform.

Installation

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.

rnow login [OPTIONS]

Option	Description
--force	Force new login even if already authenticated
--api-url URL	Custom API base URL

Example:

rnow login
# Opens browser for authentication
# Stores credentials in ~/.reinforcenow/credentials.json

---

rnow logout

Remove stored credentials.

rnow logout

---

rnow status

Check authentication status and running jobs.

rnow status

Output:

Logged in as: [e-mail]
Organization: My Team (org_abc123)
Active runs: 2
  - run_xyz789 (running) - Math Training
  - run_def456 (queued) - Code Agent

---

rnow orgs

List or select organizations.

# List all organizations
rnow orgs

# Select an organization
rnow orgs ORG_ID

Example:

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.

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
first-rl	RL	Starter template for RL
rl-single	RL	Single-turn RL with rewards
rl-tools	RL	Multi-turn RL with tool calling
sft	SFT	Supervised finetuning
tutorial-reward	RL	Learn reward functions
tutorial-tool	RL	Learn tool functions
mcp-tavily	RL	MCP integration (web search)
kernel	RL	VLM browser agent with Kernel
rl-browser	RL	Browser agent with Playwright
off-distill-agent	SFT	Off-policy distillation
on-distill-agent	Distill	On-policy KL distillation
posttrain	Midtrain	Continued pretraining

Examples:

# 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.

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:

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.

rnow stop RUN_ID

Example:

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.

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	gpt-5-nano	Override model for testing

Available Models

OpenAI API models (default, fast):

• gpt-5-nano - Fastest, recommended for quick testing
• gpt-5-mini - Faster
• gpt-5.2 - Balanced
• gpt-5-pro - Highest quality

GPU models (slower, uses actual training infrastructure):

Text models:

• Qwen/Qwen3-8B
• Qwen/Qwen3-32B
• Qwen/Qwen3-30B-A3B
• Qwen/Qwen3-235B-A22B-Instruct-2507
• meta-llama/Llama-3.1-8B-Instruct
• meta-llama/Llama-3.3-70B-Instruct
• deepseek-ai/DeepSeek-V3.1

VLM models (for vision tasks with screenshots):

• Qwen/Qwen3-VL-30B-A3B-Instruct - Vision-language model
• Qwen/Qwen3-VL-235B-A22B-Instruct - Larger VLM

Important: Default gpt-5-nano is text-only and cannot process images. For VLM projects that return screenshots (e.g., browser agents), use --model Qwen/Qwen3-VL-30B-A3B-Instruct to test with actual vision capabilities.

Examples

Basic test:

rnow test
# Runs 1 rollout with gpt-5-nano (text-only)

Multiple rollouts:

rnow test -n 5

Test specific entries:

rnow test --entry 0,3,7
# Tests entries at indices 0, 3, and 7 from train.jsonl

Test with VLM model (for vision/screenshot projects):

rnow test --model Qwen/Qwen3-VL-30B-A3B-Instruct -n 1
# Uses actual VLM that can see images

Test with GPU model:

rnow test --model Qwen/Qwen3-8B -n 3
# Uses GPU infrastructure instead of OpenAI API

Test Output

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.

rnow download RUN_ID [OPTIONS]

Option	Default	Description
-o, --output DIR	./model	Output directory

Example:

rnow download run_abc123xyz -o ./my-model
# Downloading checkpoint...
# Progress: 100%
# Saved to: ./my-model/