Tool Verification for Test-Time Reinforcement Learning

• URL: http://arxiv.org/abs/2603.02203v1
• Date: Mon, 02 Mar 2026 18:57:52 GMT
• Title: Tool Verification for Test-Time Reinforcement Learning
• Authors: Ruotong Liao, Nikolai Röhrich, Xiaohan Wang, Yuhui Zhang, Yasaman Samadzadeh, Volker Tresp, Serena Yeung-Levy,
• Abstract summary: Test-time reinforcement learning (TTRL) has emerged as a promising paradigm for self-evolving large reasoning models.<n>We present T3RL (Tool-Verification for Test-Time Reinforcement Learning), which introduces test-time tool verification into reward estimation.
• Score: 70.09740926883818
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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