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General Reasoning: Free, open resource for building large reasoning models
- carimura 2y agonginx not happy.
- rosstaylor90 2y agoHappier now, upgraded the backend :) (co-creator here)
- emorning3 2y agoLLMs cannot reason, they can only say things that sound reasonable, there's a difference. Duh.
- rosstaylor90 2y agoWhat's your AIME 2025 score? https://gr.inc/RJT1990/AIME2025/ https://gr.inc/RJT1990/AIME2025/
- nyrikki 2y agoThe is the point of the AIME, it is a 3 hour closed book examination in which each answer is an integer number from 0 to 999 and should only depend on pre-calc...for a human with no calculator, notes, or internet access. The concepts are heavily covered in the training corpus, and if people were allowed to take it more than once, with even a book let alone access to the internet it wouldn't be very hard. Examples: 1) Find the sum of all integer bases $b>9$ for which $17_b$ is a divisor of $97_b.$ In the corpus: https://www.quora.com/In-what-bases-b-does-b-7-divide-into-9b-7-without-any-remainder https://www.quora.com/In-what-bases-b-does-b-7-divide-into-9... And one more: 3) https://artofproblemsolving.com/wiki/index.php/2025_AIME_I_Problems/Problem_3 https://artofproblemsolving.com/wiki/index.php/2025_AIME_I_P... Is just the the number of ways to distribute k indistinguishable balls (players) into n distinguishable boxes (flavors, without exclusion, in such a way that no box is empty. Thus in the corpus for any courses that need to cover combinatorial problems including physics, discreet math, logistics etc... IMHO these concept classes from a typical AIME are so common, the scores you gave demonstrate that those models are doing no "general reasoning" at all and are actually failing at approximate retrieval.
- rosstaylor90 2y agoI disagree, 10 years ago AIs nailing these types of competition would have been seen as very impressive. The fact goal posts can move on this now shows how much AI has progressed. (Also the term “approximate retrieval” is a bad one - reasoning is inherently a process of chaining together associations. What matters is whether the reasoning reaches the right conclusions. Still some way to go, but already very impressive in tasks traditionally considered harbours of human reasoning!)
- CamperBob2 2y agoI disagree, 10 years ago AIs nailing these types of competition would have been seen as very impressive. It would have been seen as witchcraft.
- bossyTeacher 2y ago> What matters is whether the reasoning reaches the right conclusions no, it doesn't. a broken clock is right twice a day, reasoning is about the journey more than the destination
- rosstaylor90 2y agoRL has more than two steps...
- bossyTeacher 2y agoPoint is that reasoning is more about the conclusions. if your steps are wrong, your reasoning is wrong regardless of the conclusion. Poor reasoning is what could make an LLM conclude that 1 + 2 = 3 but what 2 + 1 = [some number other than 3]
- nh23423fefe 2y agojokes on you, they can't even speak. so obviously your sentence is meaningless. arguing about definitions is very fruitful!
- perching_aix 2y agoemorning3 cannot reason, he can only say things that sound reasonable, there's a difference. Duh. good luck. as a reminder, there are people who, with varying degrees of certainty, think their loved ones have been replaced by actors, as well as people who think they're actually the god of the world around them, for it is just their imagination.
- leptons 2y ago> as a reminder, there are people who, with varying degrees of certainty, think their loved ones have been replaced by actors, as well as people who think they're actually the god of the world around them, for it is just their imagination. None of that is any proof at all that LLMs or computers in general can reason. "some humans are dumb, so LLMs are smart" is not a valid argument here.
- fragmede 2y agohow do we test for reasoning? if A -> B and B -> C, then something that can reason could conclude A -> C. If I give A -> B and B -> C to an LLM, and ask it about the relationship between A and C, it'll tell me about the transitive property of implication, graph theory, transitivity. That there's no qualia behind that, that doesn't really reason or think or breath or love, we have to go back and ask what is reasoning. there are some definitions for reasoning that LLMs can pass, there are some they can't. If they're able to outperform dumb humans whom we assume do reason, why does that not mean that LLMs have some capacity to reason?
- vhantz 2y ago> how do we test for reasoning? if A -> B and B -> C, then something that can reason could conclude A -> C. If I give A -> B and B -> C to an LLM, and ask it about the relationship between A and C, it'll tell me about the transitive property of implication, graph theory, transitivity. Not true. A LLM might give you that answer x% of the time, x being a number less than 100. However, any thinking person answering your question, will give you the same answer, no matter how many times you ask it. That's the fundamental difference between thinking and statistically mapping and reproducing the structure of human language.
- CamperBob2 2y agoMy next-token predictor said you would say that next.
- westurner 2y ago"Can Large Language Models Emulate Judicial Decision-Making? [Paper]" (2025) https://news.ycombinator.com/item?id=42927611 https://news.ycombinator.com/item?id=42927611 ; awesome-legal-nlp, LexGLUE, FairLex, LegalBench, "Who hath done it?" exercise : {Thing done}, ({Gdo, You, Others, Unknown/Nobody} x {Ignorance, Malice, Motive, Intent}) ... Did nobody do this? Can LLMs apply a consistent procedure for logic puzzles with logically disjunctive possibilities? Enter: Philosoraptor the LLM
- akomtu 2y agoThe output of a reasoning model must be an algorithm, formulas or something similar in a formal language, that leaves no room for ambiguity. I can "reason" all day about the P=NP problem, but I won't be able to come up with something verifiable. A language model may translate the formal language of a reasoning model into English or Chinese for example. Once this stage is reached, once we can throw piles of data onto a reasoning model and get formal algorithms that explain or predict that data, the new era will begin.
- radioactivist 2y agoThe data set quality seems a really spotty based on looking a few random problems (I looked at about a dozen in the "Physics" subcategory). Several problems had no clear question (or answer) and seemed to be clipped from some longer resource and thus had back references to Sections and Chapters that the models clearly couldn't follow. Worse is that the verification of the answer seems to be via an LLM and not all that reliable; I saw several where the answer was marked correct when it clearly wasn't and several that were correct but not in the precise form given as "the" answer and thus were labelled as incorrect.
- rosstaylor90 2y agoThanks for feedback! Yes, we’re looking to improve quality in the coming months. Couple of notes: - The initial use of data is distillation so we’re less bound by question quality (anything that evinces output diversity is good). - But moving onto RL, we’ll need stronger quality. We have much better things planned both on data filtering and verification! - Surprisingly, a lot of ML datasets actually look like this when you look under hood. We’re hoping having more eyeballs on it will help improve quality in long run over less transparent status quo!
- eternityforest 2y agoI still don't understand why all the datasets have so many general knowledge questions and so much math, when so few people can do any of that stuff. It makes sense for ASI research I suppose, but why are we trying to teach small models to do stuff almost no humans even try to do? What happens if you train them with RAG context in the prompts and calculator calls in the CoT?
- rosstaylor90 2y agoMany math questions are easy to verify, and it's a classic benchmark for reasoning -> so it's a good hill to climb. I agree with your meta-point that better benchmarks testing more types of task would be good!