TTRL-Guard mitigates the Correct-Answer Extinction Window in test-time RL via flip-rate-aware reward scaling, minority-preserving sampling, and risk-conditioned sparse updates, yielding best average pass@1 on Qwen models and +54% relative gain on AIME 2025.
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When the Majority Votes Wrong, the Intervention Timing for Test-Time Reinforcement Learning Hides in the Extinction Window
TTRL-Guard mitigates the Correct-Answer Extinction Window in test-time RL via flip-rate-aware reward scaling, minority-preserving sampling, and risk-conditioned sparse updates, yielding best average pass@1 on Qwen models and +54% relative gain on AIME 2025.