// TAG: REINFORCEMENT-LEARNING

An introduction to Agentic Lightening, an open-source framework for optimizing AI agents with minimal code changes.

An overview of advanced optimization algorithms used in AI agent training, focusing on Reinforcement Learning and Automatic Prompt Optimization.

Explains the principles and components of Reinforcement Learning from Human Feedback (RLHF) in LLM development.

Learn how to implement basic RLHF workflows with Tunix for creating helpful and aligned Language Models.

Learn how to enhance a LangChain agent with Reinforcement Learning using Agentic Lightening for better decision-making and tool usage in multi-step …

A comprehensive guide to further learning and resources for mastering Agentic Lightening, LLM agents, and related technologies.

RAGEN-2 identifies and measures 'reasoning collapse' in multi-turn LLM agents, where internal thought processes degrade despite initial task success, …

Learn how to use Agentic Lightening, Microsoft's open-source framework for training and optimizing AI agents.