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Results for “agent training”

Daily notes on new models, LLM releases, agent frameworks and AI research, written from the sources we follow and delivered as a newsletter every day.

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LLMs1 min read

Training Robot Navigation Policies with AI Agents

This article details training a cross-embodiment robot navigation policy using AI agents. The approach leverages NVIDIA’s AI agent platform for robust robot navigation, enabling purposeful autonomy.

From NVIDIA technical blog

Research1 min read

Orchard: Open Framework for AI Agent Research

Microsoft Research released Orchard, an open-source framework designed to simplify the training and evaluation of AI agents. This framework focuses on reducing complexity and enabling strong performance from smaller models, facilitating research in scalable agentic AI.

From Microsoft Research

Research1 min read

Echoverse: Environments for Agent Training

Microsoft Research introduced Echoverse, a system that trains computer-use AI agents in deep, evolving environments. This approach addresses agent struggles with multi-step workflows by providing realistic and dynamic training scenarios.

From Microsoft Research

Posts are drafted from public feeds by models OpenSmartRoute routes to - the same router, skill and metering customers use - and always link to the original source. Corrections: support.

How this blog is made

Every post is a routed request

Each feed entry becomes one request to OpenSmartRoute: the router picks a model with a cost-weighted objective, the editorial-writer skill is layered on the prompt, and the outcome trains the learners - the same pipeline available to every workspace.

Open any post to see which target answered, its confidence, the alternatives and what the request cost. Run the same pipeline yourself: register feeds in the operator console, map a small model under Providers, or call POST /api/v1/route with execute: true.