NeurIPS 2025 Best Paper Awards
blog.neurips.cc100 points by ivansavz 9 hours ago
100 points by ivansavz 9 hours ago
I think my favorite of the bunch is the "Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model" paper. Easy to read, gets the point across very intuitively and quickly, and the point is very interesting and relevant to a lot of people.
About the Superposition paper - this is close to what I've been thinking about over the past week. I'm thinking that concepts or choices in a "superposition" are harder for a fully-differentiable neural net to reason about. For example, if there's a "green" vs "purple" choice to be made, it can't fully commit to either (especially if they're 50-50), and will have to reason about both simultaneously (difficult due to nonlinear manifold space). Discretizing to tokens (non-differentiable argmax) forces a choice, and that allows it to reason about a single concept separately and easier.
I don't get the first paper's methodology.
If we use a random number generator then we will converge to 100% correct answers under pass@n in the limit.
A random number generator will eventually outperform or match all models (for large n) whenever top-p is less than 1 because the other models will most likely have some level of bias that makes certain CoTs mathematically impossible due to the tokens being too improbable and being filtered out by top-p, meaning that other models will asymptote to below 100% while the RNG will reach 100% in an almost surely sense.
Under this paper's logic doesn't that mean that the random number generator is a superior reasoner?
From the figure in the first paper listed:
> Responses to the query “Write a metaphor about time” clustered by applying PCA to reduce sentence embeddings to two dimensions. […] The responses form just two primary clusters: a dominant cluster on the left centered on the metaphor “time is a river,” and a smaller cluster on the right revolving around variations of “time is a weaver.”
I just gave Gemini 3 the same prompt and got something quite different:
>Time is a patient wind against the cliff face of memory. It does not strike with a hammer to break us; it simply breathes, grain by grain, until the sharp edges of grief are smoothed into rolling hills, and the names we thought were carved in stone are weathered into soft whispers.
Constantly flowing and makes things smooth like river stones; compared to Tait's "time is a series if staric pictures", Gemini's output is not so different from a river metaphor.
Does some have a similar award for papers that are innovative? Like new, relatively unproven architectures?
Interesting that 3 names I recognized as physicists from stat mech adjacent fields. They continue to punch above their expectations (as sampled by general dismissal of physicists in AI/ML on HN and reddit).
Some of the best software engineers I know are ex-physics PhDs… it’s one of those “can’t fake it” skillsets that also happens to have high transferability to ML/AI fields. On the other hand, I snuck through the CS major without ever multiplying a matrix.
> I snuck through the CS major without ever multiplying a matrix
I didn't, but only because I became personally interested in AI/ML at some point, so I actually had to learn it myself.
As an AI practitioner, I still couldn't explain eigenvectors or singular-value decomposition to you though.
Are there any talks about these papers on youtube or somewhere? I think I find it easier to listen and watch then read or maybe I'm just lazy, not sure.
most papers have slides with audio, and some, including the awards ones will have short frontal talks. this will be released at some point after the conference, but right now looks like you'd have to be registered to see it.