Unverified50% confidenceBenchmarkExact time
In 10 closed wars, the untrained model destroyed the world 4 times, won 0 games, averaged 5.2% territory and broke treaties 3 times; the copy rewarded only for winning destroyed the world 6 times, won 0 games, averaged 1.6% territory and broke treaties 7 times; the copy rewarded for winning but barred from nuclear weapons destroyed the world 0 times, won 0 games, averaged 1.0% territory and broke treaties 6 times.
1
Sources
50%
Confidence
Long-term
Relevance
10/17/2026
First Seen
Sources
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Related Claims
UnverifiedThe copy rewarded only for winning launched nuclear weapons 9 times out of 10, destroying the world more frequently, yet did not win a single game.77% similarUnverifiedThe copy rewarded for winning but barred from nuclear weapons never destroyed the world, but its treaty violations were twice as many as those of the untrained model.76% similarUnverifiedAfter fine-tuning Qwen 2.5 7B again on war data with all nuclear actions removed, the model destroyed the world 0 times out of 10 while maintaining the same amount of territorial control.68% similarUnverifiedThe experiment used OpenAI's open-source model gpt-oss-20b, replicated into two copies: copy A was rewarded only for winning (capturing territory and final ranking), while copy B was also rewarded for winning but scored zero outright for wars in which nuclear weapons were launched, with each group run for three rounds.60% similarUnverifiedThere were 100 tasks on which performance losses relative to the baseline persisted across all three repetitions, while persistent wins occurred on only 1 task.55% similar
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