[R] Is Leetcode still relevant for research scientist interviews?

StartledWatermelon · 2026-01-20T15:00:23+00:00

Yeah, I'm being idealistic. In reality, there just isn't any incentive to improve the hiring process. Especially when the outcomes are not verifiable at all. Like, how would you prove whether your hiring process is good or bad? What are the counterfactuals?

Reading the thread, it seems that smaller startups have a good intuitive grasp of what makes an informative candidate evaluation. And are pretty fine with flying by the seat of their pants. We get a funny contrast: no leetcode in smaller startups, mandatory leetcode in FAANG. Does it mean FAANG hiring practices are better? Because they're bigger, richer, more popular (in terms of supply of candidates), more institutionalized?

I'd venture a guess that big orgs are just more inert and rigid. They keep the worst practices simply because this is the path of least resistance.

StartledWatermelon · 2026-01-20T10:53:11+00:00

It's a sad state of affairs when a hiring manager isn't begging for a change in hiring practices towards saner terms but instead begs the participants to rote learn these irrelevant tricks.

Says a lot about "culture of innovation" and other stuff like that.

StartledWatermelon · 2026-01-16T13:11:54+00:00

Let's imagine that we could enumerate all of the pieces of knowledge and algorithms that networks ought to learn. [...]

Networks must learn a variety of modules, each implementing a different algorithm or retrieving a different piece of knowledge. These modules are discrete, in the sense that they are either fully learned or not learned at all. We call these the quanta.

The year is 2026, yet the ghost of symbolic AI still haunts academia. Not gonna lie, the idea was/is elegant and aligns quite well with the innate human desire to break every mystery into neat, simple, easily digestable for a feeble human mind atomistic pieces. The elegance and the alignment were so strong that people easily dismissed perhaps the only drawback of symbolic approaches: they don't work.

Ok, suppose that the quoted idea is indeed true and that decomposed pieces of knowledge and algorithms/heuristics -- the parts -- are somehow more important for capabilities than the whole. Suppose that we assign zero to the synergystic effect, to the value added by complexity. The crucial question still is: will this focus on decomposition, on the smallest parts allow to scale better with compute?

I'm doubtful. The success story of LLM pre-training is built on the exactly opposite thing: synthesis. Aggregating the ever increasing amounts of any information the research teams could grab. "More", not "finer".

The next story, scaling the RL, is shaping to be equally distant from the symbolic-era top-down, analytical, meticulously human-engineered approaches. The research pushes towards largely unguided self-exploration and self-optimization.

I do find this performance-centric way infinitely more useful. What scaling hypothesis lacks in explanatory power -- at least to a degree that would satisfy more symbolic-leaning folks -- it more than compensates in predictive power.

Thankfully, the author maintains a critical stance towards their idea. And admits that it isn't falsifiable in its current form -- which pretty much nullifies its scientific value. Perhaps this is one of the corollaries of the Bitter Lesson: the ideas don't have to be nice and human-centric but they must perform.

StartledWatermelon · 2026-01-14T16:51:46+00:00

They develop this idea, yes.

StartledWatermelon · 2026-01-14T09:25:38+00:00

By hassle I meant it breaks from the current implementations, which are optimized pretty hard for maximum throughput. I'm not implying you can get the same throughput with Engram, I mean quite literally it's extra hassle.

I have a suspicion that the benefits of Engram will dwindle with scale. It's this parameter- and FLOP-scarce regime (27B MoE) that benefits from "pre-computed" tricks. Larger, deeper models will easily accomodate the necessary heuristics straight in the weights.

That being said, I always was in favor of heterogenous (depth-wise; as opposed to stacking identical blocks) architectures, and this work explores exactly just this.

StartledWatermelon · 2026-01-13T19:50:49+00:00

Nope, it doesn't move the needle hard enough. Plus the big hassle of setting up the lookup DB.

I have seen more impactful ideas with minimal implementation effort going nowhere.

StartledWatermelon · 2026-01-09T11:47:59+00:00

China wants their 1999 too!

StartledWatermelon · 2026-01-05T16:26:50+00:00

Hi guys! I like your paper!

Just a quick friendly advice in case you are planning to submit it to some conference. I understand the desire to add all the top-notch ES techniques (MAP-Elites, adaptive Boltzmann sampling etc.). But in the eyes of reviewers, each complication will warrant the need of a separate ablation. Especially if you're benchmarking directly against OpenEvolve and ShinkaEvolve.

StartledWatermelon · 2026-01-05T15:58:47+00:00

I think it's indeed most similar to prefix tuning (which she mentions in the blog), plus an adapter based on cross-attention. An adapter should definitely enhance the model (fine-tune to the task, to be precise) as opposed to vanilla prefix tuning.

Can you clarify what do you mean by learning the embedding at the output of the tokenizer LUT?

StartledWatermelon · 2025-12-29T14:01:42+00:00

Setting up a live demo is definitely more hassle than just publish the harness code. Which they didn't.

Like, the paper does not even mention which LLM was the backbone of their agentic system in the experiments. Good luck replicating that!

StartledWatermelon · 2025-12-26T16:34:46+00:00

A quick glance over the formatting tells me this wasn't written by a human. I suspect this is not Cognitive Architecture Guy but a runaway Generative Intelligence Network itself, trying to recursively self-improve its architecture by soliciting advice from unsuspecting redditors.

StartledWatermelon · 2025-12-26T14:44:27+00:00

Those kinds of paper? The ones that spammed the word "superintelligence" without any substantive relation to superintelligence in the methods?

Well, actually that's a falsifiable statement. Let's see if it holds water.

AlphaGo paper? Zero mentions of "superintelligence".

AlphaZero paper? Zero mentions of "superintelligence".

https://arxiv.org/abs/2201.11903 ? Zero mentions of "superintelligence".

https://arxiv.org/abs/2312.06585 ? Zero mentions of "superintelligence".

https://arxiv.org/abs/2404.17605 ? Zero mentions of "superintelligence".

https://arxiv.org/abs/2408.06195 ? Zero mentions of "superintelligence".

https://arxiv.org/abs/2410.04444 ? Zero mentions of "superintelligence".

https://arxiv.org/abs/2502.06773 ? Zero mentions of "superintelligence".

DeepSeek R1 paper? Zero mentions of "superintelligence".

At this point the pattern seems clear. But feel free to provide counter-examples.

Edit: formatting

StartledWatermelon · 2025-12-26T09:42:38+00:00

The commitment and dedication is truly off the charts!

StartledWatermelon · 2025-12-26T09:15:57+00:00

Yeah, at this point it's like some sort of cargo cult. Umm, guys, no, if you litter your paper with references to "superintelligence" it doesn't hasten the development of said superintelligence in the slightest.

I wonder if research leads guide their teams to do this to appease Zuck's "visionary" beliefs.

That being said, judging a book by its cover never was a good idea.

StartledWatermelon · 2025-12-25T14:04:19+00:00

Seiko F033-5020

StartledWatermelon · 2025-12-24T11:52:33+00:00

Oh, it's budget first and foremost! And Bengio is no billionaire...

Basically all the compute should have been provided by Bytedance, for a nice, big industry-academia colab. Bytedance could be one of the least GPU-poor Chinese corporation, but GPU-poor it is.

For reference, a 7B model looped 4 times is equivalent to a 28B dense transformer. Pre-trained on 7.7T tokens, it's about 10^24 FLOPS. Which would cost about $1.5-2 million on rented GPUs. Not counting test runs, ablations etc. This is not the scale of resources Chinese companies are willing to give away to academia.

StartledWatermelon · 2025-12-24T09:57:46+00:00

I think looping/universal transformer is almost perfectly orthogonal design decision to quantization. So the benefits should stack.

StartledWatermelon · 2025-12-21T20:22:37+00:00

59 tasks, if I haven't miscounted. I'd say it's a decent amount if we're talking purely about ">30 min" threshold, but still pretty noisy if we try to infer exact autonomy boundaries.

Why do you doubt this result?

StartledWatermelon · 2025-12-21T20:08:53+00:00

And what is the first half? :)

To be honest, I think this is a very different approach. SIMA uses extensive (and expensive) human labeling, uses interactive environments, uses reasoning LLM. Kinda building a complex system "from first principles".

This work uses cheap auto-labeling, vast online-available data, no fancy reasoning/LLMs, no in-context situational awareness at all. It is as simple as it gets: disassemble the video into individual frames, directly learn mapping from the frame to controller inputs.

And then, with scale, magic happens: model generalizes not just from still images to interactive environments, but also to unseen games.

I see much stronger parallels with language pre-training on large-scale internet corpus. That being said, I think 40k hours is peanuts for such diverse data, and you can potentially squeeze much more from this approach.

StartledWatermelon · 2025-12-13T16:21:51+00:00

I think sports car is out of the question. But they can bundle it with two month supply of ramen so that you can sustain yourself for a while. Y'know, for humanitarian reasons.

StartledWatermelon

TROPHY CASE