ReFIne: A Framework for Trustworthy Large Reasoning Models with
  Reliability, Faithfulness, and Interpretability
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
68eda21ede1fee572713a67d
Chung-En Sun
claimed_verified
2025-10-14T07:31:19.643Z
64b6076821c601b486d217a3
/avatars/bfd680028e10683b6c0544eb24006246.svg
Chung-En, Sun
user
cesun
68eda21ede1fee572713a67e
Ge Yan
68eda21ede1fee572713a67f
Akshay Kulkarni
68eda21ede1fee572713a680
Tsui-Wei Weng
ReFIne: A Framework for Trustworthy Large Reasoning Models with
  Reliability, Faithfulness, and Interpretability
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
ReFIne, a new training framework, enhances the trustworthiness of reasoning models by improving interpretability, faithfulness, and reliability through structured traces, decisive information disclosure, and confidence estimates.
long chain-of-thought reasoning CoT reasoning ReFIne supervised fine-tuning GRPO interpretability faithfulness reliability structured traces high-level planning cross-section references self-assessments derivation soundness confidence estimates Qwen3 models mathematical benchmarks