Skip to main content

ReinforceNow CLI - Reinforcement Learning platform command-line interface

Project description

ReinforceNow CLI

PyPI version Docs Follow on X MIT License

Documentation

See the documentation for a technical overview of the platform and train your first agent

Quick Start

1. Install uv (Python package manager)

# macOS/Linux:
$ curl -LsSf https://astral.sh/uv/install.sh | sh

# Windows:
PS> powershell -c "irm https://astral.sh/uv/install.ps1 | iex"

2. Install ReinforceNow

uv init && uv venv --python 3.11
source .venv/bin/activate  # Windows: .\.venv\Scripts\Activate.ps1
uv pip install rnow

3. Authenticate

rnow login

4. Create & Run Your First Project

rnow init --template sft
rnow run

That's it! Your training run will start on ReinforceNow's infrastructure. Monitor progress in the dashboard.

ReinforceNow Graph

Core Concepts

Go from raw data to a reliable AI agent in production. ReinforceNow gives you the flexibility to define:

1. Reward Functions

Define how your model should be evaluated using the @reward decorator:

from rnow.core import reward, RewardArgs

@reward
async def accuracy(args: RewardArgs, messages: list) -> float:
    """Check if the model's answer matches ground truth."""
    response = messages[-1]["content"]
    expected = args.metadata["answer"]
    return 1.0 if expected in response else 0.0

Write your first reward function

2. Tools (for Agents)

Give your model the ability to call functions during training:

from rnow.core import tool

@tool
def search(query: str, max_results: int = 5) -> dict:
    """Search the web for information."""
    # Your implementation here
    return {"results": [...]}

Train an agent with custom tools

3. Training Data

Create a train.jsonl file with your prompts and reward assignments:

{"messages": [{"role": "user", "content": "Balance the equation: Fe + O2 → Fe2O3"}], "rewards": ["accuracy"], "metadata": {"answer": "4Fe + 3O2 → 2Fe2O3"}}
{"messages": [{"role": "user", "content": "Balance the equation: H2 + O2 → H2O"}], "rewards": ["accuracy"], "metadata": {"answer": "2H2 + O2 → 2H2O"}}
{"messages": [{"role": "user", "content": "Balance the equation: N2 + H2 → NH3"}], "rewards": ["accuracy"], "metadata": {"answer": "N2 + 3H2 → 2NH3"}}

Learn about training data format

Contributing

We welcome contributions! ❤️ Please open an issue to discuss your ideas before submitting a PR


ReinforceNow

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

rnow-0.3.7.tar.gz (1.1 MB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

rnow-0.3.7-py3-none-any.whl (1.1 MB view details)

Uploaded Python 3

File details

Details for the file rnow-0.3.7.tar.gz.

File metadata

  • Download URL: rnow-0.3.7.tar.gz
  • Upload date:
  • Size: 1.1 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for rnow-0.3.7.tar.gz
Algorithm Hash digest
SHA256 0ef8c349bd67cd7a6bca7288d8d10cad1cd67432712275b66676245274caf991
MD5 2821ee523037f1f60dca463c6ce6b97e
BLAKE2b-256 f98ac80ea262bdf059e894a2f22d1f309ade9e128e607acc195799f6cbcdd1a2

See more details on using hashes here.

File details

Details for the file rnow-0.3.7-py3-none-any.whl.

File metadata

  • Download URL: rnow-0.3.7-py3-none-any.whl
  • Upload date:
  • Size: 1.1 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for rnow-0.3.7-py3-none-any.whl
Algorithm Hash digest
SHA256 b5fbc9f23b4e6c92cd31e0723ff0504bfbc872551fa09beec2ce41875b73ad9e
MD5 876e8f78b8a965492c16389870ef4349
BLAKE2b-256 cc25368fc01cadac38e8ed89f44e6f383b20de3615c835aa06033a3e5f1882c7

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page