mlsanityData to 14 Oct 2026

A Multi-Stage Workflow for the Review of Marketing Content with Reasoning Large Language Models

arXiv 2601.06054 ↗First posted 19 Dec 2025cs.CL cs.AIcited by 0

On the map: LLM reasoning → Reasoning benchmarks and evaluation

Abstract (arXiv)

Reasoning Large Language Models (LLMs) have shown promising results when tasked with solving complex problems. In this paper, we propose and evaluate a multi-stage workflow that leverages the capabilities of fine-tuned reasoning LLMs to assist in the review process of marketing content, making sure they comply with a given list of requirements. The contributions of this paper are the following: (i) we present a novel approach -- that does not rely on any external knowledge representation -- for the automatic identification of compliance issues in textual content; (ii) compare the effectiveness of different fine-tuning strategies like Supervised Fine-Tuning (SFT) and Group Relative Policy Optimization (GRPO) in training models to solve this problem; (iii) we evaluate the effectiveness of training small LLMs to generate reasoning tokens before providing their final response; (iv) we evaluate how the choice and combinations of different reward functions affects the performance of a model trained with GRPO.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 6 of its 11 references that are in the map. First posted on arXiv 19 Dec 2025.

Builds on

6 of 11 references.

References · 6

  1. 2024The Llama 3 Herd of Modelscited by 14,715
  2. 2024DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Modelscited by 9,379
  3. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  4. 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,330
  5. 2025MedVLM-R1: Incentivizing Medical Reasoning Capability of Vision-Language Models (VLMs) via Reinforcement Learningcited by 173
  6. 2024Safeguarding Decentralized Social Media: LLM Agents for Automating Community Rule Compliancecited by 5

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2025LLMs for Explainable Business Decision-Making: A Reinforcement Learning Fine-Tuning Approach
  2. 2025Fine-Tuning LLMs to Analyze Multiple Dimensions of Code Review: A Maximum Entropy Regulated Long Chain-of-Thought Approach
  3. 2023Scaling Relationship on Learning Mathematical Reasoning with Large Language Models
  4. 2023Adapting Large Language Models for Content Moderation: Pitfalls in Data Engineering and Supervised Fine-tuning
  5. 2025Advancing Reasoning in Large Language Models: Promising Methods and Approaches
  6. 2024General Purpose Verification for Chain of Thought Prompting
  7. 2024The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunities
  8. 2025GPO: Learning from Critical Steps to Improve LLM Reasoning