6 ms·
From the file: "Answer is always line 1. Reasoning comes after, never before." LLMs are autoregressive (filling in the completion of what came before), so you'
by xianshou 6mo ago
From the file: "Answer is always line 1. Reasoning comes after, never before."
LLMs are autoregressive (filling in the completion of what came before), so you'd better have thinking mode on or the "reasoning" is pure confirmation bias seeded by the answer that gets locked in via the first output tokens.
- teaearlgraycold 6mo agoI don't think Claude Code offers no thinking as an option. I'm seeing "low" thinking as the minimum.
- ares623 6mo agoUgh. Dictated with such confidence. My god, I hate this LLMism the most. "Some directive. Always this, never that."
- deleted 6mo ago[deleted]
- johnfn 6mo agoIs this true? Non-reasoning LLMs are autoregressive. Reasoning LLMs can emit thousands of reasoning tokens before "line 1" where they write the answer.
- rimliu 6mo agothere are no reasoning LLMs.
- johnfn 6mo agoThis is an interesting denial of reality.
- aqfamnzc 6mo agoA "reasoning" LLM is just an LLM that's been instructed or trained to start every response with some text wrapped in <BEGIN_REASONING></END_REASONING> or similar. The UI may show or obscure this part. Then when the model decides to give its "real" response, it has all that reasoning text in its context window, helping it generate a better answer.
- computerex 6mo agoThey are all autoregressive. They have just been trained to emit thinking tokens like any other tokens.
- bearjaws 6mo agoreasoning is just more tokens that come out first wrapped in <thinking></thinking>
- joquarky 6mo agoFor the more important sessions, I like to have it revise the plan with a generic prompt (e.g. "perform a sanity check") just so that it can take another pass on the beginning portion of the plan with the benefit of additional context that it had reasoned out by the end of the first draft.
- stingraycharles 6mo agoYeah this seems to be a very bad idea. Seems like the author had the right idea, but the wrong way of implementing it. There are a few papers actually that describe how to get faster results and more economic sessions by instructing the LLM how to compress its thinking (“CCoT” is a paper that I remember, compressed chain of thought). It basically tells the model to think like “a -> b”. There’s loss in quality, though, but not too much. https://arxiv.org/abs/2412.13171 https://arxiv.org/abs/2412.13171