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-0.3.2.tar.gz (625.1 kB view details)

Uploaded Source

Built Distribution

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

rnow-0.3.2-py3-none-any.whl (637.5 kB view details)

Uploaded Python 3

File details

Details for the file rnow-0.3.2.tar.gz.

File metadata

  • Download URL: rnow-0.3.2.tar.gz
  • Upload date:
  • Size: 625.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for rnow-0.3.2.tar.gz
Algorithm Hash digest
SHA256 befb6e5f0d89d42cd50552fa6d5d63b77192bbcd6032559aaaa8e60f82acc636
MD5 c4c95e746015dfca3ead8dfb76601e0e
BLAKE2b-256 08a4186d4806b4ac68dbb1cc7809c3dda4138cac0c0b3f646f6232a78092515e

See more details on using hashes here.

File details

Details for the file rnow-0.3.2-py3-none-any.whl.

File metadata

  • Download URL: rnow-0.3.2-py3-none-any.whl
  • Upload date:
  • Size: 637.5 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for rnow-0.3.2-py3-none-any.whl
Algorithm Hash digest
SHA256 bb97aa39abe6ebfa113f0b6426edab2cb6433987a2fc9c4dc568bcbd79e1f24a
MD5 2823d5714043bee34446992ed7f396fc
BLAKE2b-256 2c9d29deda7e98a501e31abf6d6f75722bb2f3e3a41214d8e3db3a9ce6c296cc

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