Rnow CLI

ReinforceNow CLI Reference

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

Installation

pip install rnow

Command Overview

rnow login

Authenticate using OAuth device flow.

rnow login [OPTIONS]

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: 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.

# 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]

Available Templates

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

rnow run

Submit project for training.

rnow run [OPTIONS]

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]

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]

Example:

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

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