microsoft/agent-lightning

Why this is useful

A training framework for improving AI agents using reinforcement learning, aimed at letting an existing agent's code stay mostly unchanged while its underlying decision-making gets trained against a reward signal.

It suits practitioners who already have a working agent and want to systematically improve its behaviour through training, rather than only through prompt iteration.

Reinforcement learning is a genuinely hard, resource-intensive discipline: defining a good reward signal and running training loops is a significant undertaking, not a quick tuning step, and this is a research-grade tool rather than a polished product.

Topics

• agent
• agentic-ai
• llm
• mlops
• reinforcement-learning

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