# chatGPT policy needed?

**URL:** <https://discourse.julialang.org/t/chatgpt-policy-needed/91625>\
**Category:** Meta Discussion\
**Created:** [December 13, 2022, 11:40pm UTC](https://discourse.julialang.org/t/chatgpt-policy-needed/91625 "2022-12-13T23:40:18Z")\
**Posts on this page:** 1\
**Showing post:** 20

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**Author:** ![GregVernon](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/gregvernon/32/5611_2.png) [@GregVernon](https://discourse.julialang.org/u/GregVernon)\
**Post date:** [December 14, 2022, 7:47pm UTC](https://discourse.julialang.org/t/chatgpt-policy-needed/91625/20 "2022-12-14T19:47:48Z")

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To your point, and to use a real example from the forum, consider [this post](https://discourse.julialang.org/t/how-exactly-do-optimizers-reject-infinity-loss/91597/2)

> I was reading the documentation and on this page [Frequently Asked Questions](https://docs.sciml.ai/DiffEqDocs/stable/basics/faq/) of DifferentialEquations.jl, in the Stability and Divergence section, it says:
> 
> **if you see these instability warnings during a parameter estimation process, this is likely the underlying problem. Simply check `sol.retcode != :Success` and throw an `Inf` cost and most optimizers will reject steps in those parameter regimes!**
> 
> I wonder which optimizers will do that? and how exactly do they “reject the steps”? (e.g.: do they simply skip the current step? or do they randomly initialize a new set of parameters?)

The accepted solution was:

> They skip the current step and do some line search thing. I know the Optim.jl algorithms support this. I need to dig around to see which others do it well.

While a ChatGPT solution looks like:

> The specific optimizers that will reject steps in parameter regimes where the solution is unstable will depend on the optimization algorithm being used. DifferentialEquations.jl uses a variety of optimization algorithms, so it’s not possible to say which ones will reject steps in unstable regimes without knowing which algorithm is being used.
> 
> In general, optimizers will often have a way to handle situations where the optimization process encounters an unstable or invalid solution. This could involve skipping the current step and trying a different set of parameters, or it could involve randomly initializing a new set of parameters and starting the optimization process over from the beginning. The specific details will depend on the optimization algorithm being used.

Yuck.

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