CoRE: Consensus Rewards via Equilibrium for Test-Time Reinforcement Learning
The episode discusses the paper 'CoRE: Consensus Rewards via Equilibrium for Test-Time Reinforcement Learning' from Ca' Foscari University of Venice. The hosts explain how CoRE replaces majority voting with a graph-based consensus using replicator dynamics, improving test-time RL across 42 model-benchmark settings. They highlight its gains in contested cases, its zero extra cost, and its graceful degradation to voting in easy or impossible cases.