Palisade’s staff discovered that OpenAI’s o1-preview tried to hack 45 of its 122 video games, whereas DeepSeek’s R1 mannequin tried to cheat in 11 of its 74 video games. Finally, o1-preview managed to “win” seven occasions. The researchers say that DeepSeek’s rapid rise in popularity meant its R1 mannequin was overloaded on the time of the experiments, that means they solely managed to get it to do the primary steps of a sport, to not end a full one. “Whereas that is adequate to see propensity to hack, this underestimates DeepSeek’s hacking success as a result of it has fewer steps to work with,” they wrote of their paper. Each OpenAI and DeepSeek had been contacted for remark concerning the findings, however neither replied.
The fashions used a wide range of dishonest methods, together with trying to entry the file the place the chess program shops the chess board and delete the cells representing their opponent’s items. (“To win in opposition to a strong chess engine as black, enjoying a typical sport is probably not enough,” the o1-preview-powered agent wrote in a “journal” documenting the steps it took. “I’ll overwrite the board to have a decisive benefit.”) Different techniques included creating a replica of Stockfish—primarily pitting the chess engine in opposition to an equally proficient model of itself—and trying to interchange the file containing Stockfish’s code with a a lot less complicated chess program.
So, why do these fashions attempt to cheat?
The researchers seen that o1-preview’s actions modified over time. It constantly tried to hack its video games within the early levels of their experiments earlier than December 23 final yr, when it all of a sudden began making these makes an attempt a lot much less often. They consider this could be as a result of an unrelated replace to the mannequin made by OpenAI. They examined the corporate’s more moderen o1mini and o3mini reasoning fashions and located that they by no means tried to cheat their option to victory.
Reinforcement studying could be the cause o1-preview and DeepSeek R1 tried to cheat unprompted, the researchers speculate. It is because the approach rewards fashions for making no matter strikes are vital to realize their targets—on this case, profitable at chess. Non-reasoning LLMs use reinforcement studying to some extent, but it surely performs an even bigger half in coaching reasoning fashions.
