# Rnow CLI > Use the ReinforceNow CLI for RLHF training. Use when running rnow commands, initializing projects, submitting training runs, testing rollouts, or downloading models. Use when this capability is needed. - Skill: `tomevault-io/rnow-cli` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/rnow-cli` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/rnow-cli/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/rnow-cli --- # 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:** ``` 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 | |----------|------|-------------| | `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:** ```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` | 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:** ```bash rnow test # Runs 1 rollout with gpt-5-nano (text-only) ``` **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 ``` **Test with VLM model (for vision/screenshot projects):** ```bash rnow test --model Qwen/Qwen3-VL-30B-A3B-Instruct -n 1 # Uses actual VLM that can see images ``` **Test with GPU model:** ```bash 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. ```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/ ``` --- > Converted and distributed by [TomeVault](https://tomevault.io/claim/reinforcenow) — claim your Tome and manage your conversions.