Ask HN: How are thinking efforts implemented?

Claude and ChatGPT have thinking efforts where you can tune the amount of thinking allowed.

Like low, medium, high, xhigh and so on.

But are they different models underneath? Or same model with different parameter?

The reason I ask is because, if I change the effort param mid conversation in Claude code, I get a warning suggesting I’m breaking the cache.

I don’t think this happens in Codex because when I change the effort, the responses are still quick.

20 分 | 作者 simianwords 14小时前

6 条评论

pyentropy 10小时前
Take a look at the harmony repo which specifies the internal OpenAI format - the effort level is specified in the context after the <|start|> tag - https://github.com/openai/harmony
Note that inference libs also have parsers that put hard limits on reasoning tokens with separate counters (similar to how you can put a limit on token generation per completion versus waiting for an <eos>). For that, take a look at vllm reasoning docs.
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- pyentropy 5小时前
  Examples with inference of different reasoning effort levels is in the OpenAI docs as well - https://developers.openai.com/cookbook/articles/openai-harmo...
  https://docs.vllm.ai/en/latest/features/reasoning_outputs/#a...
  https://developers.openai.com/api/docs/guides/reasoning
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  - simianwords 5小时前
    I think you have the right answer but I'm struggling to understand: does changing the effort change the prompt at the start of the conversation? I wonder why come up with this way at all? Why not just add a parameter at the end or something? At least it won't break cache.
    Maybe like: add a secret suffix to your chat in the conversation to think more like
```
   conversation....

   Hey please help
   [think more]
```
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    - pyentropy 5小时前
      I'm considering the possibility that it's good to break the prefix and cache because the LLM itself was rewarded (during post-training) with different prefixes/system prompts, each containing reasoning traces of the correct size.
      I might be very very wrong though and LLMs disagree with me, insisting that cache is preserved and the system message doesn't have to change (even though it often contains effort level in context) if effort level changes across turns, and that all you have to do is tell the inference lib that parses think tags to early-close think tags that are too long.
aabdi 12小时前
Different models do slight variants.
Usually it’s done in post training to enforce behavior based on prompt. Ie. System prompt with thinking:max or low or wtv.
Enforcement then goes via constrained decoding, checking for think token start and end with max lengths, or other variations
sometimelurker 10小时前
they use multitoken prediction behind the scenes, that might interact with the CoT in a strange way. maybe for different thinking modes they have different MTP models? if so thats interesting
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- pyentropy 10小时前
  The number of tokens you predict at time (multi or not) has nothing to do with whether the model wants to emit any, some or a lot of reasoning tokens in reasoning tag -- similar to how branch prediction will not really change the for loop iteration count.
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  - sometimelurker 1小时前
    no it might. a high reasoning task is probably harder than a low reasoning task, so the same MTP LLM will predict more correct tokens on the low reasoning task. to compensate for this, big labs likely have different MTP LLMs for different cases. it would make sense for them to do this
__patchbit__ 12小时前
At a guess. May be associated with token length context window. Down selecting is consistent with warning message, forcing cutoff to context window. The technical term cache being a synonym. Increasing the headroom for more "thinking" should allow the implementation to access more resources without warning about the cache breaking.
bjourne 8小时前
LLMs work by generating the most likely continuation to a prompt. But they can also generate multiple likely continuations. This create multiple branches which in turn can generate even more branches. The LLM can then evaluate the branches, prune the unpromising ones, and merge the best ones. More branches means more tokens, means more effort.
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- simianwords 7小时前
  this has nothing to do with the thinking effort however
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  - bjourne 6小时前
    Yes, it does. Breadth of search is exactly what the effort setting controls.
    [-]
    - pyentropy 5小时前
      LLM-judge/parallel branching ≠ multi-token prediction ≠ reasoning effort.
      See https://developers.openai.com/cookbook/articles/openai-harmo... and src/openai/types/shared/reasoning_effort.py
shanewei 13小时前
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