README.md

metadata

license: apache-2.0
language:
  - en
metrics:
  - macro-f1
library_name: transformers
base_model:
  - Qwen/Qwen2.5-7B-Instruct
tags:
  - fact-checking
  - misinformation
  - reinforcement-learning
  - grpo
  - retrieval-augmented-generation
  - iterative-retrieval
  - multi-hop-reasoning
  - llm

Model Card

R³Check is a large language model specifically trained for complex multi-hop fact-checking through iterative evidence retrieval and structured reasoning.

It is built upon Qwen2.5-7B-Instruct and trained using a rule-guided reinforcement learning framework based on Group Relative Policy Optimization (GRPO). Unlike supervised fine-tuning approaches that require annotated reasoning trajectories, R³Check relies only on final claim-veracity labels and rule-based format rewards.

The model follows a structured retrieval–reasoning protocol:

1. Structured reasoning: The model analyzes the claim and decomposes it into verifiable components within <think> tags.
2. Iterative retrieval: When additional evidence is required, the model generates search queries within <search> tags and receives retrieved passages through <information> tags.
3. Evidence-grounded verification: The model reasons over the accumulated evidence and may issue additional searches when information is incomplete.
4. Final prediction: The model produces a binary veracity judgment, True or False, within <answer> tags.

R³Check is trained using a two-stage difficulty-incremental curriculum:

• Stage 1 — Reasoning Reinforcement: The model learns structured, evidence-grounded reasoning in a closed fact-checking setting using gold evidence.
• Stage 2 — Reasoning–Retrieval Coordination: The model jointly learns iterative retrieval and reasoning by interacting with an external retriever in real time.

An importance-based bucket sampling strategy is additionally used to emphasize informative training examples and improve the effectiveness of GRPO optimization.

Model Sources

• Paper: https://dl.acm.org/doi/10.1145/3805712.3809601
• Project: https://pengqi.website/R3Check/

Citation

@inproceedings{qi2026r3check,
  author    = {Qi, Peng and Zhao, Yuyang and Hsu, Wynne and Lee, Mong Li},
  title     = {R3Check: Reinforcement Learning for Iterative Retrieval and Structured Reasoning in Complex Fact Checking},
  booktitle = {Proceedings of the 49th International ACM SIGIR Conference on Research and Development in Information Retrieval},
  year      = {2026},
  pages     = {1473--1484},
  publisher = {Association for Computing Machinery},
  doi       = {10.1145/3805712.3809601}
}