Xingzuo Li

Hi-TTRL: Regulating Consensus with Hints for Test-Time Reinforcement Learning

Test-time reinforcement learning (TTRL) improves the reasoning capabilities of large language models without labeled data by updating the policy with pseudo-labels constructed through majority voting. While effective, the reward signal assigned from majority voting is highly sensitive to consensus strength, defined as...