[bob1029]

> To improve determinism, define a system instruction with explicit rules for your specific use case.

What if I want to do the other thing? When performing research with many sub agents, having a lot of diversity in the hypotheses is a big deal. If my 5 parallel sub agents all produce the same conclusion I might as well have only ran one.

The latest OAI models have done the same thing. I'm currently adding random variation to prompts to compensate for the lack of higher temperature sampling.

[orbital-decay]

Current post-trained models are stochastically and semantically collapsed to a large extent, you'll never get meaningful semantic diversity if your prompt stays the same. This makes the temperature brittle, the model becomes dumb much earlier than meaningfully diverse. There are fancy samplers that claim to overcome this (comments ITT mentioned them), but a) you need a local inference stack to use them and b) I've yet to see any definitive evidence they add any deep semantic diversity without dumbing the model down, most of what I've seen is pretty superficial, including distribution-aware samplers.

>I'm currently adding random variation to prompts

This is better than naive temperature tweaks, if done right.