...explained step-by-step with code (100% local).

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Daily Dose of Data Science | Avi Chawla | Substack

ART (Agent Reinforcement Trainer) is an open-source framework that applies GRPO-based reinforcement fine-tuning to multi-step LLM agents. Unlike SFT, which teaches imitation, RFT lets agents improve through trial and error with reward signals. ART supports tool calls, multi-turn conversations, and integrates with LangGraph, CrewAI, and ADK. Its RULER component uses an LLM-as-a-judge to automatically generate relative reward scores without labeled data, eliminating the need to hand-craft reward functions. A hands-on walkthrough shows building a text-to-SQL agent connected to an MCP server, where a Qwen2.5-3B model learns to explore schemas, write JOINs, and handle errors through RL training—entirely locally. Benchmarks show a Qwen2.5-14B model trained with ART achieving 96% accuracy on email search, outperforming o3 while being 5x faster and 64x cheaper.

ART: Train Agents That Can Learn From Experience