[HN Gopher] NeurIPS 2025 Best Paper Awards
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NeurIPS 2025 Best Paper Awards
Author : ivansavz
Score : 162 points
Date : 2025-12-05 01:15 UTC (21 hours ago)
(HTM) web link (blog.neurips.cc)
(TXT) w3m dump (blog.neurips.cc)
| Scene_Cast2 wrote:
| 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.
| energy123 wrote:
| I am not sure how to interpret the first paper's results.
|
| 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 correct 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?
| Scene_Cast2 wrote:
| I'm not sure how likely it is that an answer would fall
| outside of the top-p of 0.95 (used in the paper). A random
| number generator would also need an unreasonably high number
| of samples to get a correct answer. I think figures 17 and 18
| are interesting for this discussion too, they show
| performance at various sampling temperatures. I think the
| point of the paper is that RL "sharpens" the distribution of
| non-RL nets, but it does not uncover any new reasoning paths
| - non-RL nets already had multiple decently high probability
| paths of answering questions to begin with, and RL reuses a
| subset of those.
| robrenaud wrote:
| I agree that pass@k feels a bit weird for large k. But for
| LLMs, it's a decent proxy for "are the
| knowledge/skills/circuit necessary to solve the problem
| somewhere in the model". Note that choices for large k is on
| the order of 256, and the range of valid answers is much
| larger than that. So your infinite monkeys critique, while
| true in the limit, wouldn't actually outperform models in the
| tested regime.
|
| Also, in practice, models don't have that much semantic
| entropy of a given prompt. With temperature based sampling,
| models will tend to generate very similar but not identical
| responses.
| boroboro4 wrote:
| To me intellect has two parts to it: "creativity" and
| "correctness". And from this perspective random sampler is
| infinitely "creative" - over (infinite) time it can come up
| with answer to any given problem. And from this perspective
| it does feel natural that base models are more "creative"
| (because that's what being measured in the paper), while RL
| models are more "correct" (that's a slope of the curve from
| the paper).
| ilaksh wrote:
| Does some have a similar award for papers that are innovative?
| Like new, relatively unproven architectures?
| robrenaud wrote:
| From what I've seen at neurips, in terms of most different but
| maybe viable, it would be this.
|
| https://sakana.ai/ctm/
|
| In terms of a fresh perspective on designing learning systems,
| nested learning seems very interesting.
|
| https://abehrouz.github.io/files/NL.pdf
|
| Hearing the clarity, creativity, and force behind his thoughts
| and speech, I'd give a more than 1/200 chance Ali Behrouz gets
| himself a Turing award. At the very least, I think he will end
| making major contributions to AI.
| djrhails wrote:
| There is TITANS - https://arxiv.org/abs/2501.00663
| chermi wrote:
| 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).
| chatmasta wrote:
| 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.
| ctxc wrote:
| Haha, nice bio. Seeing that font on HN is quite a shock.
| miki123211 wrote:
| > 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.
| mnky9800n wrote:
| Do people not like physicists?
| jmalicki wrote:
| https://xkcd.com/793/ captures the stereotype well.
| peterfirefly wrote:
| Especially because those annoying dilettante know-it-all
| physicists are often right.
| niceguy4 wrote:
| 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.
| neves wrote:
| There conference had interesting lectures. Will they be posted
| online?
| cosmic_ape wrote:
| 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.
| FrozenSynapse wrote:
| use NotebookLM
| cubefox wrote:
| Whenever I search for the title a new machine learning paper,
| there are a bunch of YouTube videos about it which are just
| NotebookLM slop. It's straight up environmental pollution.
| Der_Einzige wrote:
| One of the most popular of those slop videos was about our
| antislop sampler. Ironic.
|
| https://youtu.be/PHSqcdIc5gM?si=I62bduoDgnlNFPZ6
| gradascent wrote:
| 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.
| SiempreViernes wrote:
| 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.
| djoldman wrote:
| Oh man, this link is worth it just for the "Reflections from the
| Selection Committee."
|
| These days, abstracts are so marketing/advertising forward that
| it's hard to even understand the claim.
| yanhangyhy wrote:
| seems lots of chinese.
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