Trust Issues? Fixing Test-Time RL with Verified Votes
A model can be wrong in a very human way: not by hesitating, but by becoming popular with itself. That is the uncomfortable premise behind Tool Verification for Test-Time Reinforcement Learning, a new paper proposing T3RL, or Tool-Verification for Test-Time Reinforcement Learning.1 The paper studies a specific weakness in label-free test-time reinforcement learning: when a reasoning model generates many candidate solutions, uses majority voting as a pseudo-label, and then trains itself toward that answer, the “most common” answer may simply be the most common mistake. ...