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Getting Started with Distributed Training at Scale

Figure 1: AI computing application scenarios in Weixin

The architecture of a Reinforcement Learning (RL) library is split into two primary components: Generation and Training. During the generation phase, an LLM Engine performs multi-turn rollouts within an environment to produce data and reward signals. This output is then fed into the training phase to update the model's parameters. This process forms a feedback loop, where the progressively improved model generates the next iteration of data for continuous refinement.

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AI Application Platform Buyers Guide

eBook AI Platform Buyer Guide

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AI Application Platform Buyer’s Guide

Coactive AI now delivers real time multimodal insights across millions of images and videos, with faster model deployment, lower processing costs, and a cleaner, more scalable platform powered by Anyscale.

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day from dev to prod for multimodal AI models

Any accelerator, any cloud, any framework. Anyscale is made for how you work today and how you want to scale tomorrow.

Coactive AI Runs its Multimodal Platform on Anyscale

“As multimodal AI models evolve, Anyscale lets us adopt the latest quickly. Setup, evaluation, and deployment that once took a week or more can now be done by a single developer in a day.”

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Modernizing workloads with Gen AI

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Samsara Case Study

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Powered by Ray, Anyscale empowers AI builders to run and scale all ML and AI workloads on any cloud and on-prem.

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