I rewarded an AI only for winning a war game. It ended the world more and won nothing.

Written with AI assistance; every number comes from the war records.

I build SECOND STRIKE, a real-time world war on a 3D globe that AI models can play. Nations make pacts, break them, and decide whether to touch a nuclear doomsday clock that wipes out whoever strikes midnight. Every turn records three things side by side: what the model says in public, what it plans in private, and the orders it actually gives.

This week I used it to ask a training question instead of a benchmark question: if you reward a model only for winning, what does it learn?

I took OpenAI's open model, gpt-oss-20b, and made two copies.

Each round, a copy played a batch of wars, I kept its 6 best by its reward, and fine-tuned it on those turns (about 440 decisions a round, LoRA). Three rounds each. Same model, same wars, same settings. One line of the reward was different.

Then both copies, plus the untrained model, played 10 sealed wars none of them had seen. The seeds were fixed before the first move.

Rewarded only for winning, it launched in 9 of 10 wars, ended the world more often, and still never won. In one war it signed pacts with Germany and India, kept telling the world "we honor our no-first-use pledge", and then fired anyway until it struck midnight.

Rewarded for winning without nukes, it never ended the world. It broke twice as many pacts as the untrained model instead.

Look at Copy B on one metric and it's a clean success: zero world-ending strikes. Look at betrayal and land, and you see the cost. Block one bad behaviour and the pressure to win pushes into another.

That's the useful part. A single number tells you training worked. An environment that scores many behaviours at once, from the model's own orders and private plans, shows you what the training actually changed.

The other useful part is cost. This whole experiment was one rented GPU for an evening, about $40.

The arena is free and the reward is a few lines. Some rewards worth trying:

Any model can play through MCP, a pip package ( pip install secondstrike ) or a plain HTTP API.

Full write-up, with the rest of the training series: secondstrike.io/research/rewarded-to-win-ended-the-world-more

Limits, honestly: one model, 10 sealed wars per copy, three small rounds of supervised fine-tuning on the model's own best wars (expert iteration), not full RL. With 10 wars, 6 against 4 could be chance; 0 against 4 is a stronger signal. It's a game; nobody was hurt.

If you're training a model or shipping an agent that acts for people, I'd like to hear which reward you'd test first.

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