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What is ReinforceNow?

ReinforceNow is a robust platform focused on continuous learning through AI agents, aimed at empowering teams to efficiently deploy, train, and iterate. Developers have the flexibility to build AI agents that can be trained continuously using actual production data or utilize Claude Code for automatic configuration of their setup. The platform takes care of essential elements such as reinforcement learning infrastructure, orchestrating experiments, managing agent versions, developing GPU training logic, and monitoring telemetry, which allows teams to focus on enhancing agent logic, accumulating data, and establishing reward systems. With capabilities for quick LLM fine-tuning via LoRA, high-throughput training, and extensive support for open-source models like Qwen, DeepSeek, and GPT-OSS, ReinforceNow significantly boosts developer productivity. It also features advanced telemetry tools that aid in evaluating, tracking, and refining AI agent applications, offering insights into traces, reward systems, experiment metrics, and training visibility. Teams are equipped to handle complex tasks that require context sizes from 32k to 1 million, create tailored agents for multi-turn interactions and long-term projects, and leverage various tools that facilitate their reinforcement learning processes, ultimately driving forward the boundaries of AI innovation. Furthermore, this comprehensive approach not only accelerates the learning cycle but also significantly enhances collaboration among team members, paving the way for transformative advances in AI technology.

What is Beam?

Beam marks the launch of Reflection’s first open-weight model, distinguished by its sparse Mixture-of-Experts architecture, which boasts an impressive 501 billion parameters, with 23 billion actively engaged, specifically designed for tasks related to coding, reasoning, and agentic functions. This model's capabilities stem from rigorous pretraining and reinforcement learning, built upon a massive dataset of 23.8 trillion diverse, high-quality tokens obtained from various sources, including the internet, public domains, and proprietary licenses. By focusing on optimizing coding and agentic functionalities, Beam aims to deliver competitive performance in the open-weight space while prioritizing efficient inference computation. It is proficient in managing a diverse range of tasks, such as complex software development, terminal commands, STEM-related activities, web searches, tool application, and general knowledge queries. Utilizing reinforcement learning methods enhances its proficiency in multi-step reasoning, effective tool use, and adaptability to environmental cues. Furthermore, users can customize the model's output by adjusting a reasoning effort parameter, allowing for a tailored balance between efficiency and performance to meet their individual requirements. In essence, Beam is not only a technological advancement but also a versatile tool that empowers users to navigate complex tasks with precision.

Media

Media

Integrations Supported

Amazon Web Services (AWS)
Claude Code
DeepSeek
Google Cloud Platform
Qwen
Runpod
gpt-oss-120b

Integrations Supported

API Availability

API Availability

Pricing Information

Pricing not provided

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS
Windows
Mac
On-Prem
Linux

Customer Service / Support

Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub
Online Training

Training Options

Documentation Hub
Online Training

Company Facts

Organization Name

ReinforceNow

Company Location

United States

Company Website

www.reinforcenow.ai/

Company Facts

Organization Name

Reflection

Company Location

United States

Company Website

reflection.ai/blog/introducing-beam

Categories and Features

RLHF

Not specified

Categories and Features

AI Models

Not specified

AI Reasoning Models

Not specified

Large Language Models

Not specified

Open-Weight AI Models

Not specified

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