In this tutorial, we explore how an agent can internalize planning, memory, and tool use within a single neural model rather than relying on external orchestration. We design a compact, model-native agent that learns to perform arithmetic reasoning tasks through reinforcement learning. By combining a stage-aware actor-critic network with a curriculum of increasingly complex environments,
The post How to Build a Model-Native Agent That Learns Internal Planning, Memory, and Multi-Tool Reasoning Through End-to-End Reinforcement Learning appeared first on MarkTechPost. Read More
