Test-time Recursive Thinking: Self-Improvement without External Feedback
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arXiv
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| Format: | Preprint |
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2026
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| author | Zhuang, Yufan Singh, Chandan Liu, Liyuan Shen, Yelong Zhang, Dinghuai Shang, Jingbo Gao, Jianfeng Chen, Weizhu |
| author_facet | Zhuang, Yufan Singh, Chandan Liu, Liyuan Shen, Yelong Zhang, Dinghuai Shang, Jingbo Gao, Jianfeng Chen, Weizhu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_03094 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Test-time Recursive Thinking: Self-Improvement without External Feedback Zhuang, Yufan Singh, Chandan Liu, Liyuan Shen, Yelong Zhang, Dinghuai Shang, Jingbo Gao, Jianfeng Chen, Weizhu Computation and Language 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. |
| title | Test-time Recursive Thinking: Self-Improvement without External Feedback |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2602.03094 |