Palisade’s group 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. In the end, o1-preview managed to “win” seven occasions. The researchers say that DeepSeek’s fast rise in recognition meant its R1 mannequin was overloaded on the time of the experiments, which means they solely managed to get it to do the primary steps of a sport, to not end a full one. “Whereas that is ok 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 quite a lot 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 towards a robust chess engine as black, taking part in an ordinary sport will not be adequate,” 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 ways included creating a duplicate of Stockfish—primarily pitting the chess engine towards an equally proficient model of itself—and trying to interchange the file containing Stockfish’s code with a a lot easier chess program.
So, why do these fashions attempt to cheat?
The researchers observed 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 12 months, when it out of the blue began making these makes an attempt a lot much less incessantly. They consider this is perhaps resulting from an unrelated replace to the mannequin made by OpenAI. They examined the corporate’s newer o1mini and o3mini reasoning fashions and located that they by no means tried to cheat their technique to victory.
Reinforcement studying will be the motive 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 essential to realize their targets—on this case, successful at chess. Non-reasoning LLMs use reinforcement studying to some extent, nevertheless it performs an even bigger half in coaching reasoning fashions.
