Generating deep research reports requires large-scale information acquisition and the synthesis of insight-driven analyses, posing a major challenge for current language models. Existing methods largely follow a plan-then-write paradigm, whose performance strongly depends on the quality of the initial outline. We propose a Writing As Reasoning Policy (WARP) framework, which enables models to dynamically revise outlines during report writing. In this policy, the agent alternates between Evidence-Based Drafting and Reasoning-Driven Deepening phases, jointly supporting information acquisition, knowledge refinement, and outline updating. We further introduce a Multi-Stage Agentic Training—including cold start, atomic skill RL, and holistic pipeline RL—that enables small models to operate the WARP effectively. Experiments on DeepResearch Bench, DeepConsult, and DeepResearch Gym show that our approach allows small models to surpass leading closed-source systems, particularly with substantial improvements in Insight metric.