Reasoning or Retrieval? A Study of Answer Attribution on Large Reasoning Models

• URL: http://arxiv.org/abs/2509.24156v1
• Date: Mon, 29 Sep 2025 01:13:33 GMT
• Title: Reasoning or Retrieval? A Study of Answer Attribution on Large Reasoning Models
• Authors: Yuhui Wang, Changjiang Li, Guangke Chen, Jiacheng Liang, Ting Wang,
• Abstract summary: Large reasoning models (LRMs) exhibit unprecedented capabilities in solving complex problems through Chain-of-Thought (CoT) reasoning.<n>Recent studies reveal that their final answers often contradict their own reasoning traces.<n>We hypothesize that this inconsistency stems from two competing mechanisms for generating answers: CoT reasoning and memory retrieval.<n>We introduce FARL, a novel fine-tuning framework that integrates memory unlearning with reinforcement learning.
• Score: 15.797612515648412
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large reasoning models (LRMs) exhibit unprecedented capabilities in solving complex problems through Chain-of-Thought (CoT) reasoning. However, recent studies reveal that their final answers often contradict their own reasoning traces. We hypothesize that this inconsistency stems from two competing mechanisms for generating answers: CoT reasoning and memory retrieval. To test this hypothesis, we conduct controlled experiments that challenge LRMs with misleading cues during reasoning and/or corrupted answers during retrieval. Our results across models and datasets confirm that both mechanisms operate simultaneously, with their relative dominance influenced by multiple factors: problem domains, model scales, and fine-tuning approaches (e.g., reinforcement learning vs. distillation). The findings reveal a critical limitation in current reasoning fine-tuning paradigms: models can exploit the retrieval mechanism as a shortcut, effectively "hacking" the reward signal and undermining genuine reasoning development. To address this challenge, we introduce FARL, a novel fine-tuning framework that integrates memory unlearning with reinforcement learning. By carefully suppressing retrieval shortcuts during the fine-tuning process, FARL promotes reasoning-dominant behavior and enhances generalizable reasoning capabilities.

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