Learning to Reason Faithfully through Step-Level Faithfulness Maximization

• URL: http://arxiv.org/abs/2602.03507v1
• Date: Tue, 03 Feb 2026 13:28:17 GMT
• Title: Learning to Reason Faithfully through Step-Level Faithfulness Maximization
• Authors: Runquan Gui, Yafu Li, Xiaoye Qu, Ziyan Liu, Yeqiu Cheng, Yu Cheng,
• Abstract summary: Reinforcement Learning with Verifiable Rewards (RLVR) has markedly improved the performance of Large Language Models (LLMs)<n>Most RLVR pipelines rely on sparse outcome-based rewards, providing little supervision over intermediate steps.<n>We propose FaithRL, a general reinforcement learning framework that directly optimize reasoning faithfulness.
• Score: 35.23601691819328
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has markedly improved the performance of Large Language Models (LLMs) on tasks requiring multi-step reasoning. However, most RLVR pipelines rely on sparse outcome-based rewards, providing little supervision over intermediate steps and thus encouraging over-confidence and spurious reasoning, which in turn increases hallucinations. To address this, we propose FaithRL, a general reinforcement learning framework that directly optimizes reasoning faithfulness. We formalize a faithfulness-maximization objective and theoretically show that optimizing it mitigates over-confidence. To instantiate this objective, we introduce a geometric reward design and a faithfulness-aware advantage modulation mechanism that assigns step-level credit by penalizing unsupported steps while preserving valid partial derivations. Across diverse backbones and benchmarks, FaithRL consistently reduces hallucination rates while maintaining (and often improving) answer correctness. Further analysis confirms that FaithRL increases step-wise reasoning faithfulness and generalizes robustly. Our code is available at https://github.com/aintdoin/FaithRL.

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