TRN-R1-Zero: Text-rich Network Reasoning via LLMs with Reinforcement Learning Only

TL;DR

TRN-R1-Zero introduces a reinforcement learning framework for zero-shot reasoning on text-rich networks, effectively integrating textual semantics with relational structure without task-specific supervision.

Contribution

It presents a novel RL-based training method for LLMs that enhances relational reasoning in TRNs without supervised fine-tuning or chain-of-thought data.

Findings

Outperforms prior methods on multiple TRN benchmarks.

Achieves zero-shot inference on edge- and graph-level tasks.

Demonstrates robustness and generalization across domains.

Abstract

Zero-shot reasoning on text-rich networks (TRNs) remains a challenging frontier, as models must integrate textual semantics with relational structure without task-specific supervision. While graph neural networks rely on fixed label spaces and supervised objectives, recent large language model (LLM)-based approaches often overlook graph context or depend on distillation from larger models, limiting generalisation. We propose TRN-R1-Zero, a post-training framework for TRN reasoning trained solely via reinforcement learning. TRN-R1-Zero directly optimises base LLMs using a Neighbour-aware Group Relative Policy Optimisation objective that dynamically adjusts rewards based on a novel margin gain metric for the informativeness of neighbouring signals, effectively guiding the model toward relational reasoning. Unlike prior methods, TRN-R1-Zero requires no supervised fine-tuning or chain-of-thought data generated from large reasoning models. Extensive experiments across citation, hyperlink, social and co-purchase TRN benchmarks demonstrate the superiority and robustness of TRN-R1-Zero. Moreover, relying strictly on node-level training, TRN-R1-Zero achieves zero-shot inference on edge- and graph-level tasks, extending beyond cross-domain transfer. The codebase is publicly available at https://github.com/superallen13/TRN-R1-Zero.