TTRL: Test-Time Reinforcement Learning
What this work does
Estimates labels by majority agreement among sampled answers, then uses agreement-based rewards for RL on otherwise unlabeled test questions.
Connection to discovery
A useful self-consistency baseline when external labels are unavailable.
Evidence
The reward pseudocode and implementation disclose response counts, sampling and separate benchmark adaptation, making the available feedback and compute budget explicit.
Scope and limitations
Consensus is not truth and can amplify shared misconceptions. It cannot certify novel causal claims; adaptation also consumes the test distribution, so its protocol differs from frozen-model evaluation.
THE RESEARCH CONVERSATION
Discussion & reactions
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