My guess is that RL training being done with particular generation parameters makes models much more brittle to changes in these parameters, and that's why we're seeing changes like this across model providers. But I don't really know.
temperature, top_p, and top_k are deprecated and ignored. In future model generations, supplying these parameters returns an HTTP 400 error. Remove these parameters from all requests.
Obligatory "The Conspiracy Against High Temperature Sampling":
https://gist.github.com/Hellisotherpeople/71ba712f9f899adcb0...
> To improve determinism, define a system instruction with explicit rules for your specific use case.
Is this guaranteed to work any better than top_k or top_p? This just sounds like making a smaller version of a Agent.md doc.
My guess is that RL training being done with particular generation parameters makes models much more brittle to changes in these parameters, and that's why we're seeing changes like this across model providers. But I don't really know.
I'm inclined to agree given how unstable Gemma 4 is when not using the "official" sampler settings
fwiw sonnet-5 also drops temperature (sonne-4 had it)
Good. These have been basically useless for the past few generations of models, and most of the time made the model perform worst.
1. Sampling parameter deprecation (temperature, top_p, top_k)
temperature, top_p, and top_k are deprecated and ignored. In future model generations, supplying these parameters returns an HTTP 400 error. Remove these parameters from all requests.