ReGraph is a large-scale recipe graph dataset that represents ingredients, cooking actions, and tools as entities, uses entity attributes to describe ingredient state changes, and employs typed relations to encode manipulation targets, destinations, and procedural ordering. ReGraph further incorporates explicit Recipe Reasoning Chain-of-Thought (RR-CoT) traces, providing auxiliary supervision for procedural decomposition and structured graph generation. Building on ReGraph, we propose Recipe Graph Learning (RGL), a two-stage framework that enables Large Multimodal Models (LMMs) to generate a plausible fine-grained cooking workflow from a food image in the form of a structured recipe graph. Our experiments reveal a substantial gap between text-generation quality and recoverable procedural structure: recipes produced by existing approaches achieve competitive text-generation scores yet yield limited reference-aligned entity and relation structure under the ReGraph schema. In contrast, across two representative LMM backbones, RGL consistently improves the generation of cooking entities and procedural relations, while our analysis further shows that fine-grained ingredient-state capture remains the most challenging dimension. Blogger's Review: ReGraph provides a new, structured perspective on recipe generation, enabling models to generate more detailed, ordered cooking workflows, which is a very interesting research direction that may have applications in smart kitchens, automated cooking, and other areas in the future.