Internalizing Academic Writing Workflows for Introduction Generation via Struct-Aware Policy Learning

Abstract:Generating a rigorous paper introduction with large language models (LLMs) remains challenging, since it requires coordinating background, gap identification, method and contribution within a coherent narrative. Existing solutions externalize this process as multi-stage prompts or agent workflows which are expensive and vulnerable to cross-stage drift. We propose StructPO, a struct-aware policy learning framework that internalizes the entire multi-stage writing workflow into a single-pass policy controlled by explicit stage tokens. StructPO introduces struct-aware credit assignment to decouple local stage quality from global coherence and refinement-guided optimization to internalize revision behavior into the first-pass policy. Experiments show that StructPO improves semantic alignment, structural rationality and inference efficiency over workflow-based baselines, generalizes to out-of-domain settings, and remains competitive with GPT-5.1 in human evaluation when scaled to Qwen3-32B. These results show that internalizing academic writing workflows through fine-grained policy optimization offers a viable alternative to costly external orchestration.

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

arXiv:2608.03138 [cs.CL]

(or arXiv:2608.03138v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2608.03138 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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