ReFIne: A Framework for Trustworthy Large Reasoning Models with
  Reliability, Faithfulness, and Interpretability
Authors: name: Chung-En Sun, name: Ge Yan, name: Akshay Kulkarni, name: Tsui-Wei Weng

Recent advances in long chain-of-thought (CoT) reasoning have largely
prioritized answer accuracy and token efficiency, while overlooking aspects
critical to trustworthiness. We argue that usable reasoning systems must be
trustworthy, characterized by three properties: interpretability, faithfulness,
and reliability. To this end, we propose ReFIne, a new training framework that
integrates supervised fine-tuning with GRPO to encourage models to: (i) improve
interpretability by producing structured, tag-based traces with high-level
planning that are easier for humans to follow; (ii) enhance faithfulness by
explicitly disclosing the decisive information guiding each solution, with
consistent cross-section references; and (iii) promote reliability by providing
self-assessments of both the derivation's soundness and the confidence of the
final answer. We apply ReFIne to the Qwen3 models at multiple scales
(1.7B/4B/8B) and evaluate across mathematical benchmarks of varying difficulty.
Our experimental results show that ReFIne models generate clearer and
better-structured reasoning traces (interpretability +44.0%), more faithfully
expose their underlying decision process (faithfulness +18.8%), and offer
informative confidence estimates (reliability +42.4%). These findings highlight
an overlooked but important direction: reasoning models should be optimized not
only for accuracy, but also for broader dimensions of trustworthiness. Our code
is available at:
https://github.com/Trustworthy-ML-Lab/Training_Trustworthy_LRM_with_Refine