Finding RELIEF: Shaping Reasoning Behavior without Reasoning Supervision via Belief Engineering

• URL: http://arxiv.org/abs/2601.13752v1
• Date: Tue, 20 Jan 2026 09:07:01 GMT
• Title: Finding RELIEF: Shaping Reasoning Behavior without Reasoning Supervision via Belief Engineering
• Authors: Chak Tou Leong, Dingwei Chen, Heming Xia, Qingyu Yin, Sunbowen Lee, Jian Wang, Wenjie Li,
• Abstract summary: Large reasoning models (LRMs) have achieved remarkable success in complex problem-solving, yet they often suffer from computational redundancy or reasoning unfaithfulness.<n>We propose Reasoning Belief Engineering (RELIEF), a framework that shapes LRM behavior by aligning the model's self-concept with a target belief blueprint.<n>RELIEF internalizes desired traits by fine-tuning on synthesized, self-reflective question-answering pairs that affirm the target belief.
• Score: 25.183793455770978
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large reasoning models (LRMs) have achieved remarkable success in complex problem-solving, yet they often suffer from computational redundancy or reasoning unfaithfulness. Current 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. Building upon this insight, we propose Reasoning Belief Engineering (RELIEF), a simple yet effective framework that shapes LRM behavior by aligning the model's self-concept with a target belief blueprint. Crucially, RELIEF completely bypasses the need for reasoning-trace supervision. It internalizes desired traits by fine-tuning on synthesized, self-reflective question-answering pairs that affirm the target belief. Extensive experiments on efficiency and faithfulness tasks demonstrate that RELIEF matches or outperforms behavior-supervised and preference-based baselines while requiring lower training costs. Further analysis validates that shifting a model's reasoning belief effectively shapes its actual behavior.

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