Paper
Tool Verification for Test-Time Reinforcement Learning
Authors
Ruotong Liao, Nikolai Röhrich, Xiaohan Wang, Yuhui Zhang, Yasaman Samadzadeh, Volker Tresp, Serena Yeung-Levy
Abstract
Test-time reinforcement learning (TTRL) has emerged as a promising paradigm for self-evolving large reasoning models (LRMs), enabling online adaptation on unlabeled test inputs via self-induced rewards through majority voting. However, a spurious yet high-frequency unverified consensus can become a biased and reinforced reward signal, leading to incorrect mode collapse. We address this failure mode with T^3RL (Tool-Verification for Test-Time Reinforcement Learning), which introduces test-time tool verification into reward estimation. Concretely, a verifier uses an external tool as evidence (e.g., from code execution) to upweight verified rollouts in a verification-aware voting, producing more reliable pseudo-labels for training. Across various math difficulties (MATH-500, AMC, and AIME 2024) and diverse backbone types, T^3RL significantly improves over TTRL, with larger gains on harder problems. More broadly, T^3RL can be viewed as verified online data synthesis, highlighting test-time tool verification as a key mechanism for stabilizing self-evolution.
Metadata
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Raw Data (Debug)
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2603.02203v1</id>\n <title>Tool Verification for Test-Time Reinforcement Learning</title>\n <updated>2026-03-02T18:57:52Z</updated>\n <link href='https://arxiv.org/abs/2603.02203v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2603.02203v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>Test-time reinforcement learning (TTRL) has emerged as a promising paradigm for self-evolving large reasoning models (LRMs), enabling online adaptation on unlabeled test inputs via self-induced rewards through majority voting. However, a spurious yet high-frequency unverified consensus can become a biased and reinforced reward signal, leading to incorrect mode collapse. We address this failure mode with T^3RL (Tool-Verification for Test-Time Reinforcement Learning), which introduces test-time tool verification into reward estimation. Concretely, a verifier uses an external tool as evidence (e.g., from code execution) to upweight verified rollouts in a verification-aware voting, producing more reliable pseudo-labels for training. Across various math difficulties (MATH-500, AMC, and AIME 2024) and diverse backbone types, T^3RL significantly improves over TTRL, with larger gains on harder problems. More broadly, T^3RL can be viewed as verified online data synthesis, highlighting test-time tool verification as a key mechanism for stabilizing self-evolution.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.AI'/>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.CL'/>\n <published>2026-03-02T18:57:52Z</published>\n <arxiv:comment>12 pages, 11 figures</arxiv:comment>\n <arxiv:primary_category term='cs.AI'/>\n <author>\n <name>Ruotong Liao</name>\n </author>\n <author>\n <name>Nikolai Röhrich</name>\n </author>\n <author>\n <name>Xiaohan Wang</name>\n </author>\n <author>\n <name>Yuhui Zhang</name>\n </author>\n <author>\n <name>Yasaman Samadzadeh</name>\n </author>\n <author>\n <name>Volker Tresp</name>\n </author>\n <author>\n <name>Serena Yeung-Levy</name>\n </author>\n </entry>"
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