Tool Verification for Test-Time Reinforcement Learning
Test-time reinforcement learning (TTRL) has emerged as a promising paradigm for Recursive Self-Improving AI (RSI) by adapting Large Reasoning Models (LRMs) on unlabeled test inputs, using self-consensus rewards derived from majority voting over sampled rollouts. However, majority voting can mistake popularity for correctness: a spurious yet high-frequency unverified consensus may become a biased reward signal, causing test-time RL to reinforce frequent but wrong answers and collapse into an incorrect mode. We address this false-popular failure mode with T$^3$RL (Tool-Verification for Test-Time Reinforcement Learning), a verification-aware test-time RL framework. T$^3$RL grounds pseudo-lab...
| Authors | Ruotong Liao, Nikolai Röhrich, Xiaohan Wang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 2 March 2026 |
| Last updated at source | 26 August 2026 |
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