Peking University and Samsung paper uses a slow model as offline coach to raise Jev's live game score
On RealtimeBench, Jev scored 13.51 live, and after two rounds of offline instructions written by the slow reasoning model TORSI, its score rose to 24.19, improving all six game types.
New Peking University and Samsung paper shows that a slow reasoning model can coach a fast model offline, raising its live game score from 13.51 to 24.19. shows that the stronger reasoning model in paused games can fall far behind a faster model once the game keeps moving. Many game evaluations pause the world while the model thinks, so slow answers cost nothing. In real settings, a correct action can arrive too late. They built RealtimeBench, 180 tasks across 6 games like Mario, Snake, and Tetris that keep running during inference. GPT-6.1-sol beat the faster Jev in 5 of 6 games when paused, but scored 1.51 versus 13.51 out of 100 live. Their fix, TORSI, has the slow model study the fast model's failures between games and write instructions the fast model follows during play. After 2 rounds, Jev's average score rose from 13.51 to 24.19, improving all 6 game types. For real-time agents, keep a fast model in the loop and use the slow model offline to write and test its instructions.