Test-time Recursive Thinking: Self-Improvement without External Feedback

• URL: http://arxiv.org/abs/2602.03094v1
• Date: Tue, 03 Feb 2026 04:37:37 GMT
• Title: Test-time Recursive Thinking: Self-Improvement without External Feedback
• Authors: Yufan Zhuang, Chandan Singh, Liyuan Liu, Yelong Shen, Dinghuai Zhang, Jingbo Shang, Jianfeng Gao, Weizhu Chen,
• Abstract summary: Test-time Recursive Thinking (TRT) is an iterative self-improvement framework.<n>Open-source models reach 100% accuracy on AIME-25/24, and on LiveCodeBench's most difficult problems, closed-source models improve by 10.4-14.8 percentage points without external feedback.
• Score: 120.80790108733942
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modern Large Language Models (LLMs) have shown rapid improvements in reasoning capabilities, driven largely by reinforcement learning (RL) with verifiable rewards. Here, we ask whether these LLMs can self-improve without the need for additional training. We identify two core challenges for such systems: (i) efficiently generating diverse, high-quality candidate solutions, and (ii) reliably selecting correct answers in the absence of ground-truth supervision. To address these challenges, we propose Test-time Recursive Thinking (TRT), an iterative self-improvement framework that conditions generation on rollout-specific strategies, accumulated knowledge, and self-generated verification signals. Using TRT, open-source models reach 100% accuracy on AIME-25/24, and on LiveCodeBench's most difficult problems, closed-source models improve by 10.4-14.8 percentage points without external feedback.

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