Recursive Think-Answer Process for LLMs and VLMs
Think-Answer reasoners such as DeepSeek-R1 have made notable progress by leveraging interpretable internal reasoning. However, despite the frequent presence of self-reflective cues like "Oops!", they remain vulnerable to output errors during single-pass inference. To address this limitation, we propose an efficient Recursive Think-Answer Process (R-TAP) that enables models to engage in iterative reasoning cycles and generate more accurate answers, going beyond conventional single-pass approaches. Central to this approach is a confidence generator that evaluates the certainty of model responses and guides subsequent improvements. By incorporating two complementary rewards-Recursively Confi...
| Authors | Byung-Kwan Lee, Youngchae Chee, Yong Man Ro |
|---|---|
| Primary category | cs.CL |
| Published | 2 March 2026 |
| Last updated at source | 3 March 2026 |
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