Build a Self‑Contained Agent with End‑to‑End RL Framework

Traditional AI agents often rely on a separate planner, memory module, and tool‑use orchestrator that sits outside the core neural network. This separation makes deployment difficult and hampers end‑to‑end learning. The tutorial demonstrates how to collapse these components into a single, compact neural model—what the authors call a “model‑native agent.” By embedding planning, short‑term memory, and tool‑use logic directly into the network’s architecture, the agent can learn everything—from arithmetic reasoning to tool invocation—through pure reinforcement learning.

The core of the design is a stage‑aware actor‑critic network that tracks the agent’s progress through a sequence of sub‑tasks. Each stage corresponds to a different reasoning phase, such as drafting a plan, retrieving a memory slot, or calling an external calculator. The network’s hidden state carries a lightweight memory buffer that is updated only when the agent reaches a memory‑related stage. To teach the model to handle multiple tools, the authors introduce a curriculum of arithmetic environments that grow in depth and complexity, ranging from single‑step addition to multi‑step algebraic manipulation. As the agent masters simpler tasks, new, harder environments are added, encouraging continual learning and preventing catastrophic forgetting.

Training proceeds with a standard PPO algorithm, but with a few practical tweaks that make the learning stable. The reward signal is sparse—only when the final answer is correct—but the stage‑aware architecture provides intermediate shaping signals that encourage the agent to complete each sub‑task. The tutorial supplies a fully reproducible code base in PyTorch, along with step‑by‑step instructions for setting up the curriculum, defining the stage transitions, and logging progress. In experiments, the model‑native agent achieved over 90 % accuracy on the hardest arithmetic tasks after just 200 k steps, outperforming a baseline that used an external planner. The authors conclude that a single neural network can internalize complex reasoning patterns, paving the way for more lightweight, deployable agents.