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cyanydeeztoday at 1:35 AM3 repliesview on HN

--thinking-budget and --thinking-message is all you need in llamacpp to keep it progressing.

the message can be some combination of tool calling, summarizing, etc. It's overthinking often is a bunch of recursion, so simply stopping t and redirecting is all you need to do.

If someones building a harness for llamacpp, you can set this per message, so it's possible to dynamically control it by watching for the expansion of the thinking traces, and redirecting it.

I use the message to tell it to use subagents, add additional logging and to use opencode's dynamic context pruning.

As such, we'll just whisper here _skill issue_.


Replies

dofmtoday at 1:41 AM

Unfortunately in xhigh thinking it goes down rabbit holes in such an extreme depth-first way, that whenever you choose to cut it off, there is a very good chance it will not have got round to musing on even half of the prompt! It doesn’t really obviously loop in xhigh, so I am not sure if an “overthinking guard” proxy would have much to go on, but it does obsessively ruminate on edge cases. I have seen it overcomplicate simple code as a result even in my limited testing.

Probably the better solution if you want it to be quicker but still fairly thorough appears to be to configure reasoning effort instead of thinking budget. It seems to do very well still even on the Low setting; on the Medium setting it can get stuck in loops like 3.6 does.

I think xhigh reasoning effort was an absurd choice for a default, and so was not sorting out the chat template so LM Studio could offer the reasoning effort dropdown.

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bitexplodertoday at 1:42 AM

Yeah, but be fair. Working with small models is a different ball game. Not all the batteries come included :)

bellowsgulchtoday at 1:52 AM

This is definitely such a cool feature that I wish cloud providers would expose.