Recursive Think-Answer Process for LLMs and VLMs
This addresses the issue of error vulnerability in reasoning models for AI researchers, though it is incremental as it builds on existing Think-Answer approaches.
The paper tackles the problem of output errors in single-pass inference for Think-Answer reasoners like DeepSeek-R1 by proposing a Recursive Think-Answer Process (R-TAP) that enables iterative reasoning cycles, resulting in models consistently outperforming conventional methods for LLMs and VLMs with significantly fewer self-reflective patterns and faster inference.
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 Confidence Increase Reward and Final Answer Confidence Reward-we show that R-TAP-enhanced models consistently outperform conventional single-pass methods for both large language models (LLMs) and vision-language models (VLMs). Moreover, by analyzing the frequency of "Oops"-like expressions in model responses, we find that R-TAP-applied models exhibit significantly fewer self-reflective patterns, resulting in more stable and faster inference-time reasoning. We hope R-TAP pave the way evolving into efficient and elaborated methods to refine the reasoning processes of future AI.