@inproceedings{leong-etal-2026-finding,
    title = "Finding {RELIEF}: Shaping Reasoning Behavior without Reasoning Supervision via Belief Engineering",
    author = "Leong, Chak Tou  and
      Chen, Dingwei  and
      Xia, Heming  and
      Yin, Qingyu  and
      Lee, Sunbowen  and
      Wang, Jian  and
      Li, Wenjie",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.218/",
    pages = "4444--4467",
    ISBN = "979-8-89176-395-1",
    abstract = "Large reasoning models (LRMs) have achieved remarkable success through step-by-step chains of thought, yet they often suffer from excessive redundancy or unfaithful reasoning. Existing methods for shaping LRM behavior typically rely on reinforcement learning or fine-tuning with gold-standard reasoning traces, a paradigm that is both computationally expensive and difficult to scale. In this paper, we reveal that LRMs possess latent \textit{reasoning beliefs} that internally track their own reasoning traits, which can be captured through simple logit probing without specialized training. Building on this insight, we propose Reasoning Belief Engineering (), a simple yet effective framework that shapes LRM behavior by aligning the model{'}s self-concept with a target belief blueprint. Crucially, completely bypasses the need for reasoning-trace supervision. It internalizes desired traits by fine-tuning on synthesized, self-reflective QA pairs that affirm the target belief. Extensive experiments on efficiency and faithfulness tasks demonstrate that matches or outperforms behavior-supervised and preference-based baselines while requiring significantly lower training costs. Our analysis further validates that shifting a model{'}s reasoning belief effectively shapes its actual behavior."
}