How to Train Multi-Step Agents Without Writing Reward Functions Using ART

Train Multi-Step Agents for Real-World Tasks with ART An end-to-end guide for developers who hate writing reward functions Reader profile: You already know Python, have played with an LLM API, and now want the model to do something useful across many steps—play 2048, solve Temporal Clue, retrieve the right e-mail—without spending nights hand-crafting a reward function. This article explains exactly how the open-source Agent Reinforcement Trainer (ART) does that for you. 1. What problem does ART solve? Pain point How ART fixes it Writing a reward function is tedious and error-prone RULER auto-scores trajectories with another LLM GRPO training code …