6 ms·
Show HN: AutoThink – Boosts local LLM performance with adaptive reasoning
I built AutoThink, a technique that makes local LLMs reason more efficiently by adaptively allocating computational resources based on query complexity.
The core idea: instead of giving every query the same "thinking time," classify queries as HIGH or LOW complexity and allocate thinking tokens accordingly. Complex reasoning gets 70-90% of tokens, simple queries get 20-40%.
I also implemented steering vectors derived from Pivotal Token Search (originally from Microsoft's Phi-4 paper) that guide the model's reasoning patterns during generation. These vectors encourage behaviors like numerical accuracy, self-correction, and thorough exploration.
Results on DeepSeek-R1-Distill-Qwen-1.5B:
- GPQA-Diamond: 31.06% vs 21.72% baseline (+43% relative improvement)
- MMLU-Pro: 26.38% vs 25.58% baseline
- Uses fewer tokens than baseline approaches
Works with any local reasoning model - DeepSeek, Qwen, custom fine-tuned models. No API dependencies.
The technique builds on two things I developed: an adaptive classification framework that can learn new complexity categories without retraining, and an open source implementation of Pivotal Token Search.
Technical paper: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5253327 https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5253327
Code and examples: https://github.com/codelion/optillm/tree/main/optillm/autothink https://github.com/codelion/optillm/tree/main/optillm/autoth...
PTS implementation: https://github.com/codelion/pts https://github.com/codelion/pts
I'm curious about your thoughts on adaptive resource allocation for AI reasoning. Have you tried similar approaches with your local models?
- codelion 1y agoThe motivation for AutoThink came from watching how current reasoning models waste computation - they spend the same amount of "thinking time" on "what's 2+2?" as they do on complex mathematical proofs. This seemed obviously inefficient. The breakthrough was combining two techniques I'd been working on separately: adaptive classification (which can learn new categories without retraining) and an open source implementation of Pivotal Token Search from Microsoft's Phi-4 paper. When I put them together with dynamic token budgeting, the performance gains were much better than expected. What surprised me most was that the technique actually uses fewer tokens on average while improving performance. The adaptive allocation means simple queries finish faster, offsetting the extra computation on complex ones. A few technical notes: - The steering vectors are small (typically <1MB per pattern) and add minimal memory overhead - Classification adds about 10ms latency, which is negligible - Target layer selection matters - I found middle layers (15-20) work best for most models I'd love feedback on: - Have you tried similar adaptive approaches with your models? - What other reasoning patterns would be useful to steer toward? - Ideas for automatically detecting the optimal target layer? Thanks for checking it out! Happy to answer any questions about the implementation or results.
- behnamoh 1y ago> they spend the same amount of "thinking time" on "what's 2+2?" as they do on complex mathematical proofs. Not anymore. Have you seen Gemini 2.5 Pro? Ask it simple questions and it almost doesn't "think". Ask it a coding question and it'll write a long reasoning article. I think the same goes for o3.
- codelion 1y agoYes, we started with the idea of trying to replicate similar control on thinking processes for open reasoning models. They also announced the Deep Think approach at IO which goes even further and combines parallel CoTs at inference.
- sigmoid10 1y agoThe original o1 also didn't do this. Neither did the actual DeepSeek R1. You could even get it to answer immediately without any reasoning tokens. These highly distilled versions just lost most of their common sense for this.
- shing3232 1y agoWell, it does overthink quite a bit. if It can reduce overthink,it s gonna be useful
- victorbjorklund 1y agoOverthink is subjectibe. It really depends on how much you value the answer. "how long break distance does a train need if going in 100 km/hour?" Just need a quick reply and you dont care so much (maybe showerthought)? Or is life and death depending on the answer? The same question can need different amount of thinking.
- normie3000 1y ago> is life and death depending on the answer? In this situation I suspect you'd still want the answer quickly.
- blocksocial130 1y ago[dead]
- nssnsjsjsjs 1y agoThis is an obvious optimisation. Surprised this isn't been done already. Good job writing it up and showing how it can be done.
- transfire 1y agoThat’s awesome! Now have it mark blocks of text on or off, so it can ignore irrelevant, or worse erroneous material — no need to include it in the context window.
- codelion 1y agoThis sounds like an interesting idea, can you elaborate more may be with a concrete example. I am wondering if this can be implemented easily as a plugin in optillm.
- pkoird 1y agoBack to TF IDF we go.
- knuppar 1y agoOne could argue TF-IDF is a case of an attention layer... but not quadratic in inference/training and kinda just a quotient. Yeah maybe we should go back
- Stem0037 1y ago[dead]
- shah_akshat 1y agoSurprised this didn't exist. Great work @codelion
- danielhanchen 1y agoSuper cool and the results look pretty solid as well! Will give it a try!
- deleted 1y ago[deleted]
- keeganpoppen 1y agoi have definitely observed a similar pattern in the Big Label Foundation Models… so, i’m glad to see it in this realm too <3
- bufferoverflow 1y agoBut how do you classify a question as high vs low complexity? Some seemingly simple questions can turn out to be very very complex. For example, integer solution to x³ + y³ + z³ = 42 took over a hundred years of compute time to find. Or another seemingly simple equation with positive integers x,y,z x/(y+z)+y/(z+x)+z/(x+y) = 4 requires elliptic curve knowledge, and the solution is huge x = 154476802108746166441951315019919837485664325669565431700026634898253202035277999 y = 36875131794129999827197811565225474825492979968971970996283137471637224634055579 z = 4373612677928697257861252602371390152816537558161613618621437993378423467772036 (Solution is discussed here: https://www.quora.com/How-do-you-find-the-positive-integer-solutions-to-frac-x-y+z-+-frac-y-z+x-+-frac-z-x+y-4 https://www.quora.com/How-do-you-find-the-positive-integer-s...)
- codelion 1y agoQuery complexity in this context is based on how many tokens it took for the model to respond to a query correctly based on a ground truth dataset like GSM8k. The adaptive classifier learns over this dataset and then we use it at inference for classification.
- bufferoverflow 1y agoSo it can be very very wrong. You're trading correctness for speed.
- codelion 1y agoYes, the goal here is to avoid overthinking and be as efficient as possible in terms of the minimal tokens required to solve a query. Often, queries that require too many tokens are unlikely to lead to correct answers anyways otherwise they would show up when we are learning the classifier.
- VagabundoP 1y agoIf you ask it to rethink the problem again because you've found a flaw, does it bump up the complexity and actually think about it. Like a person might give you a quick answer to something and then questioning the answer would cause them to think deeper about it.
- vintermann 1y agoIf host models for others, then sure, I'm happy to save some computation time for really simple queries. Sure the cost is that the model will be effectively dismissive of questions it judges to be "easy", but I'm not the one carrying that cost I suppose. However, for a local model, answering my own queries? That's the last thing I want. I already spent way too much money on that GPU, might as well get use out of it.
- MagicMoonlight 1y agoYou didn’t invent this. Models like o3 already do it, that’s why the amount of thinking time varies.
- rohansood15 1y agoHe's not claiming he did. It says right there that it's an open-source implementation to run with local models.
- shwouchk 1y agoVery interesting, thanks for sharing! FWIW gemini explicitly told me that it ranks question difficulty from 1 to 100 and depending on the bin allocates more or less resources to answering it
- NitpickLawyer 1y ago> gemini explicitly told me Do you mean someone from the gemini team? If you "asked" the LLM then it's likely a "hallucinated" answer. They say all sort of things about "themselves" only because they were trained to do so. They likely have 0 knowledge about their true architecture.
- shwouchk 1y agothis might be true in the future. right now a lot of the “architecture” is directly built into the prompt. even in the future, the model that is directly responding to you will likely have to know some details of its architecture to be useful.
- throwaway314155 1y agoGemini has no access to its internal processes outside of whats in its system prompt - and even then LLM's are known to fabricate information about their inception.
- shwouchk 1y agoYou obviously know enough to stake your reputation on the line, anon
- throwaway314155 1y agoI don't give a shit. Be wrong for all I care. There's nothing that says I owe you my reputation or whatever just to participate online (where people can and regularly do lie about who they are and what they've done anyway...)
- casenmgreen 1y agoIt seems to me inadvisable to say "think" and "reason", because those words have particular meanings, and those particular meanings are not in use by LLMs. They are a computing method, where we can choose to use more or less run time (and so processor time), to generate results.
- falcor84 1y agoThe ship has sailed, just like "computers" once referred to a human profession and now referred to machines.
- dgb23 1y agoMy worldview is materialist and deterministic in principle. But day to day I'm an existentialist with a touch of spiritualism. To me, a fairly pragmatic way of characterizing these tools day to day is to anthropomorphize them. One benefit of this heuristic: they simulate conversation and it's much easier to use them with a conversational flow. Another one is to create an approximation of a character, which makes it easier to build a useful intuition for what they can and cannot do. Obviously these kinds of heuristics do break down. But it's obvious enough when they do so one can switch into a more precise and analytical mode of thinking.
- dymk 1y agoWhen you “ping” and IP address, are you bouncing sound waves off of the metal hull of the other computer? No, but the word is used anyways, as it’s a useful metaphor for what’s really going on.
- mentalgear 1y agoIt's great how small models help small teams and individual researchers everywhere now compete with big AI labs by allowing them to demonstrate new innovative approaches on small experiments. Also, as small language models (SML) become more competent, it's amazing what they can do on-device !
- chrisweekly 1y ago> "small language models (SML)" that should be SLM, right?
- Dowwie 1y agoHey, this is really interesting. What are the features you used to measure the reasoning complexity? In other words, how does one evaluate a query during classification?
- codelion 1y agoWe use an adaptive classifier to learn how many tokens the model takes to respond correctly on a known dataset. I used the https://huggingface.co/adaptive-classifier/llm-router https://huggingface.co/adaptive-classifier/llm-router for experiments it is based on distilbert.
- kristel100 1y ago[dead]
- lostmsu 1y agoOfficial https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-... says GPQA-Diamond is 33.8
- codelion 1y agoHey, yes the reported results do not restrict any time limit or token limit for the benchmarks. We run our baseline with the same config 0.6 temp and max_token 32k but we set a timeout after 600 secs. Otherwise it would take forever to benchmark with the resources we had. I have a note in the actual paper on that in the implementation details section.
- lostmsu 1y agoGPQA-Diamond is 200 questions. Any GPU since 2019 with 12GB of VRAM should be able to run tens if not hundreds of queries for a 1.5B model in parallel.
- codelion 1y agoIf we try to benchmark GPQA-Diamond with DeepSeek-R1 in the suggested configuration of 0.6 temp and 32k max_tokens and say if every instance takes the maximum tokens it will require 6.4 M tokens. Which without batching on a single H100 at 80 tok/s will take 23 hrs to run. To run with 32k context length on a single H100 a 1.5B model will require ~15-20 GB VRAM so you cannot run 10s or 100s of queries in parallel. MMLU-PRO is 12,000 instances. To avoid this we set a 600 seconds timeout for each instance to run.
- deleted 1y ago[deleted]
- NiloCK 1y agoI, too, built a POC autothink shortly after the Claude 3.7 release that included the `extended thinking` toggle. It's literally also called autothink: https://github.com/NiloCK/autothink https://github.com/NiloCK/autothink https://www.paritybits.me/think-toggles-are-dumb/ https://www.paritybits.me/think-toggles-are-dumb/ My own version took a first pass with an LLM whose job was to assign a 0-100 complexity rating, and then there was more or less a linear scaling of the allocated thinking budget. The OP effort here is obviously higher grade, and I'm really tickled to see quantitative results. Well done.
- GENIXUS 1y agoI’m very new to the world of LLMs and AI, but this project really caught my attention. From what I understood, AutoThink helps the AI “think more wisely” by adjusting how much effort it spends based on how hard the question is. That makes a lot of intuitive sense — like how people don’t spend 10 minutes figuring out what 2+2 is, but do take time with tricky problems. Even though I don’t know the technical parts (like token budgeting or steering vectors), it’s fascinating to see how these methods can make the AI both faster and smarter at the same time. Thanks for sharing — I’m definitely going to follow this kind of work more closely from now on.
- CMay 1y agoIn terms of reasoning models like QwQ or Qwen 3 I didn't waste too much time trying to improve their results aside from coming up with various ways to constrain their reasoning token output with prompts. Even though Gemma 3 27B QAT is not a reasoning model, it's so good at instruction following and being used in LLM chains/routes that it can be used for classifying/language optimization steps before instructing it how to reason about the prompt in the next step. You can even have it output intermediate answers interspersed between multiple think tags in the same response. In many ways for these models I just define thinking as any tokens that are helping the model arrive at the conclusion, but are not fully formed parts of the answer. Instructing it to use certain words (tokens) and types of phrasing preferentially is something that is known to improve results in general, not just in LLMs and I've seen improved results by encouraging certain types of language to be used. AutoThink using the highest performing tokens out of a dataset _could_ be a nice way to optimize towards that in a more general way. It seems like there's a risk of using so many pivotal tokens that it almost overfits responses to benchmark questions, though. So, while I have personally seen careful word/token selection improve result quality and also see it as a potential low cost high return optimization, I'd still want to see how AutoThink generalizes.
- SamScout 1y agoGreat food for thought! We will discuss this approach as we find our evolving AI-crawler should ideally be able to recognize when a site we visit needs more vs. less queries. For context, we're samaritanscout.org a search engine that is attempting to provide a comprehensive view into all local volunteering opportunities posted on a range of nonprofit websites.
- shirman 1y agoHi, it does not work with llama.cpp right?
- codelion 1y agoOptillm works with llama.cpp but this approach is implemented as a decoding strategy in PyTorch so at the moment you will need to use the local inference server in optillm to use it.