Generalizable LLM Learning of Graph Synthetic Data with Reinforcement Learning

• URL: http://arxiv.org/abs/2506.00845v2
• Date: Fri, 06 Jun 2025 02:55:21 GMT
• Title: Generalizable LLM Learning of Graph Synthetic Data with Reinforcement Learning
• Authors: Yizhuo Zhang, Heng Wang, Shangbin Feng, Zhaoxuan Tan, Xinyun Liu, Yulia Tsvetkov,
• Abstract summary: We propose to unlock generalizable learning of graph synthetic data with reinforcement learning.<n>We first design solution-based and process-based rewards for synthetic graph problems.<n>Experiments demonstrate that our RL recipe leads to statistically significant improvement on 5 datasets.
• Score: 38.485929062532925
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Previous research has sought to enhance the graph reasoning capabilities of LLMs by supervised fine-tuning on synthetic graph data. While these led to specialized LLMs better at solving graph algorithm problems, we don't need LLMs for shortest path: we need generalization from synthetic graph data to real-world tasks with implicit graph structures. In this work, we propose to unlock generalizable learning of graph synthetic data with reinforcement learning. We first design solution-based and process-based rewards for synthetic graph problems: instead of rigid memorizing response patterns in direct fine-tuning, we posit that RL would help LLMs grasp the essentials underlying graph reasoning and alleviate overfitting. We employ RL algorithms such as GRPO and DPO, aligning both off-the-shelf LLMs and LLMs fine-tuned on synthetic graph data. We then compare them against existing settings on both in-domain synthetic tasks and out-of-domain real-world tasks with implicit graph structures such as multi-hop QA, structured planning, and more. Extensive experiments demonstrate that our RL recipe leads to statistically significant improvement on 5 datasets, with an average gain of 12.9\% over baseline settings. Further analysis reveals that process-based rewards consistently outperform solution-based rewards, mixing synthetic and real-world task data yields potential gains, while compositionality and explainable intermediate steps remains a critical challenge even after RL.

Related papers

• GraphRAG-R1: Graph Retrieval-Augmented Generation with Process-Constrained Reinforcement Learning [33.57411612551111]
  We propose GraphRAG-R1, an adaptive GraphRAG framework by training LLMs with process-constrained outcome-based reinforcement learning (RL)<n>Our method can decompose complex problems, autonomously invoke retrieval tools, and perform effective reasoning.<n>Our framework can be flexibly integrated with various existing retrieval methods, consistently delivering performance improvements.
  arXiv  Detail & Related papers  (2025-07-31T14:11:16Z)
• G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning [58.73279333365234]
  Reinforcement Learning (RL) on synthetic graph-theoretic tasks can significantly scale graph reasoning abilities.<n>With RL on Erdos, G1 obtains substantial improvements in graph reasoning, where our finetuned 3B model even outperforms Qwen2.5-72B-Instruct (24x size)<n>Our findings offer an efficient, scalable path for building strong graph reasoners by finetuning LLMs with RL on graph-theoretic tasks.
  arXiv  Detail & Related papers  (2025-05-24T04:33:41Z)
• What Do LLMs Need to Understand Graphs: A Survey of Parametric Representation of Graphs [69.48708136448694]
  Large language models (LLMs) are reorganizing in the AI community for their expected reasoning and inference abilities.<n>We believe this kind of parametric representation of graphs, graph laws, can be a solution for making LLMs understand graph data as the input.
  arXiv  Detail & Related papers  (2024-10-16T00:01:31Z)
• How Do Large Language Models Understand Graph Patterns? A Benchmark for Graph Pattern Comprehension [53.6373473053431]
  This work introduces a benchmark to assess large language models' capabilities in graph pattern tasks.<n>We have developed a benchmark that evaluates whether LLMs can understand graph patterns based on either terminological or topological descriptions.<n>Our benchmark encompasses both synthetic and real datasets, and a variety of models, with a total of 11 tasks and 7 models.
  arXiv  Detail & Related papers  (2024-10-04T04:48:33Z)
• Can LLM Graph Reasoning Generalize beyond Pattern Memorization? [46.93972334344908]
  We evaluate whether large language models (LLMs) can go beyond semantic, numeric, structural, reasoning patterns in the synthetic training data and improve utility on real-world graph-based tasks. We find that while post-training alignment is most promising for real-world tasks, empowering LLM graph reasoning to go beyond pattern remains an open research question.
  arXiv  Detail & Related papers  (2024-06-23T02:59:15Z)
• Integrating Graphs with Large Language Models: Methods and Prospects [68.37584693537555]
  Large language models (LLMs) have emerged as frontrunners, showcasing unparalleled prowess in diverse applications. Merging the capabilities of LLMs with graph-structured data has been a topic of keen interest. This paper bifurcates such integrations into two predominant categories.
  arXiv  Detail & Related papers  (2023-10-09T07:59:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.