What objective is reasoning solving?

FLeXMurphy

What is the library, if any, used for the equation/formula rendering? Looks tastefully done.

NickNaraghi

> reasoning as pathfinding through token space Sounds like it's hillclimbing all the way down! Cool framing. I'm excited for the parallel construction of ___ as pathfinding through weight space.

dogscatstrees

Wang's argument contradicts the view that intelligence necessarily improves through more reasoning and undermines any assumption that recursive self-improvement (RSI) will automatically converge toward greater intelligence.

jrflo

Content aside, that's a beautiful webiste

avereveard

Normalizes user voice to goal so the action has more chances to success you can see this on model that dont hide traces sending very badly spelled or incomplete sentences

vova_hn2

I'm pretty sure that the current approach of baking in chain-of-thought reasoning into weights is not the end of the line. It just seems to accidental and ad-hoc. P. S. The animated bird is truly magical!

akavi

My understanding of the math is weak (to say the least), but intuitively what I'm getting from this is: 1) Pretraining creates a bias _towards_ "correct answers" with each inference step 2) A longer walk is more likely to land in the correct region, given it's easier to verify you have the correct answer than to create it 3) RL selects the subset of walks that lands in the correct region and tilts the distribution in favor of such walks 4) By 2, that trains towards longer walks ...Is that correct? EDIT: Wait, but this contradicts the post's claim that "The gains we credit to “thinking longer” come from the search we paid for during training, rather than at inference.". This requires that thinking longer is not at all causal for correct answers, which contradicts 2.

derefr

It solves for the same thing a "train of thought" (internal vocalization) solves for in the human brain: there's a lot of stuff you "know" as episodic memories of words you've heard/read, but haven't yet internalized via practice into reflex reactions. It's a lot faster and cheaper to acquire a memory of some words, than it is to acquire an entire new skill, so you end up picking up far more memories-of-words than concrete skills from a given training corpus. Reasoning (i.e. bubbling those remembered words up into your mental context, and then using a special "trigger effectively arbitrary synaptic activations dependent on the content of the attention-masked context and an ongoing state" bridging reflex on them, a.k.a. "system-2 thinking") is how you leverage memories-of-words into useful behavior. Rather than executing native skills embedded into your synaptic weights, you're running a single very fancy native skill that acts as an interpreter for word-memory data, slowly and strenuously turning words you're holding in mind into more words (synthesis), or into associations to other memories (word-memories or otherwise), or into actions/outputs, etc. (An especially useful "trick" of this system is planning , i.e. chaining the interpreter through "mere words" that conjure more "mere words" to mind until you end up at words that form a declaration of what you're about to do. You can then rely on a special part of the bridging reflex, namely the "instruction-following" reflex, to react in a way where your actions are constrained by the plan you've generated.)

niemandhier

That is how I have been thinking about LLM reasoning pretty much since it arrived. I am not sure I support his statement about RL “searching for successful paths and then training the model to reproduce them”. Don’t we train the model to use a certain method to explore the space in search for an answer? As soon as you have tool calling and a way to verify your result, such as a theorem prover for example you are essentially running an optimization algorithm.

piker

> Consider the problem: “what’s 1 +2 +3 +⋯ +10”? One solution is to add the numbers one at a time: 1, 3, 6, 10, and so on up to 55. Another way is to pair them from the outside in: 1 +10, 2 +9, 3 +8, 4 +7 and 5 +6 each make 11, and five pairs of 11 make 55. Both of these paths land on the correct answer, 55. Watching my son work through difficult math problems has forced me into this realization. In my head there is one "correct" path, and I often have to stop myself from interrupting his chain of thought. He often is just getting there a different way.

0c3ca83

Mostly, it seems to be solving the problem of people thinking about things themselves.

ryanschaefer

Lot of Alex Wangs out there

vatsachak

Alex also had the transformers as memory blog. Beautiful. Somebody give him the bag

htrp

>I'm a researcher based in San Francisco. I like to imagine where the world is headed, and what it takes to get there. >I did my PhD in computer science at Stanford, where I designed long-context architectures for sequence modeling and memory. Before that, I studied math & physics at Cornell. Most recently, I was a research scientist at Cartesia, working on in-context tokenization and novel architectures (HNets). Not the MSL Alex Wang

nycdatasci

A beautiful extension of a comment I made ~20 hours ago: https://news.ycombinator.com/item?id=50435002 Initially, I typed "intelligence is a path in token space" but then I decided that was too abstract. In case relevant, I was typing on a device with Muse installed.

drfloyd51

I don’t understand this part: if we assume a function that can validate an answer… what do we need AI to do? The existence of a function that can validate an answer means we have a proven way of finding the answer.

usernametaken29

> Train the model to generate better samples This is precisely what humans do. We use small world statistics to drastically reduce the search space of problems. Of course there’s the whole issue of contextual search which LLMs address in no way whatsoever, so even improving sampling won’t help much, as inevitably one starts to generate too many hypotheses (reasoning traces) to get a good average.

bugreport671

FYI all equations are broken (tops are cut off)

gertlabs

This is a great article. In our RL work, we think of reasoning as a skill -- a set of tools and protocols to leverage knowledge a model already has to improve the probability of creating knowledge the model doesn't have. Although specifically defining reasoning as a skill is hard, we've seen success with partners who agree that you don't need to enable reasoning on every single rollout -- you get diminishing returns past a certain point (the model often already has a complete toolkit for how to explore ideas and run calculations inside of its reasoning budget). Instead, it's more compute efficient to spend some percentage of rollouts training models with reasoning disabled, on problems that they are calibrated to have a good solve rate on without reasoning. This improves the cognitive core/latent reasoning directly which translates to fewer total tokens needed to solve problems, and harder problems that a model can solve. And it's more realistic, considering that humans don't need an inner monologue to solve many problems (even hard ones, e.g. a flow state).

tristenharr

This is fascinating to me, I imagine this is in large part the future. This along with the weight space programming work that came out a few days ago are where I think we are headed. I wonder if ideally we could skip ALL the "thinking/reasoning" steps if you trained a model to jump from "question space" to "answer space". Imagine a diffusion model or something that learns to map the start of the path to the destination. Very interesting