Alexi Gladstone ✓

@AlexiGlad

ASI whisperer. Working on EBTs/EBMs, XMs, Generative Modeling, World Models, Reasoning. PhD @ UIUC. Prev @flappyairplanes @Meta, @PalantirTech

8 321Followers

808Following

623Posts total

1.3MViews on collected posts

Neueste Beiträge

Alexi Gladstone ✓@AlexiGlad31 Jul

@AlexiGlad We did this in DeltaWorld, called it BoM. Cool to see that it can improve a DiT! My hesitation is that BoM alone can’t scale to many modes, and doesn’t guarantee noise space coverage. Your Reverse XM is interesting, but wouldn’t it incentivize memorization over general 7.1K views · 34 likes · 0 reposts · 5 replies Open on X →

Alexi Gladstone ✓@AlexiGlad31 Jul

P.S. Every single one of these results was predicted in advance by Mode Forcing, a unifying and predictive theory of generative modeling. That paper drops soon! If you work on image gen, video gen, world models, robot policies, or MDLMs, exploration should be a drop-in 13.6K views · 165 likes · 8 reposts · 7 replies Open on X →

Alexi Gladstone ✓@AlexiGlad31 Jul

[10/N] Website: https://t.co/rKTx0sZq2j Paper: https://t.co/hGV6QaWdeC GitHub: https://t.co/gob266aL4j Hugging Face: https://t.co/qvjctxpKRG Huge thanks to @du_yilun, @hengjinlp, @LaudeInstitute, Flapping Airplanes, @bradenjhancock, @k_tighe @HessianFree and so many others for h

1:02

15.8K views · 132 likes · 12 reposts · 3 replies Open on X →

Alexi Gladstone ✓@AlexiGlad31 Jul

[8/N] Does exploration also help generalization? During generative pretraining, the same input often gets different valid targets. To a model that can only predict one thing, this looks like noise, and fitting noise is memorization. With exploration, each prediction commits to

14.7K views · 67 likes · 5 reposts · 3 replies Open on X →

Alexi Gladstone ✓@AlexiGlad31 Jul

[9/N] How end-to-end can generative models actually become with exploration? There are two ways to handle the many-valid-outputs problem: decompose generation (existing approaches), or decompose training, which is Explorative Modeling. It turns out these are interchangeable, h

15.5K views · 66 likes · 5 reposts · 3 replies Open on X →

Alexi Gladstone ✓@AlexiGlad31 Jul

[6/N] How do gains from exploration change with scale? They grow. 7%→36% as data scales. 13%→23% as models scale. Efficiency gains more than doubled when we tripled compute. There's a cool reason for this. For the past decade we’ve scaled parameters, which set what a model htt

16.4K views · 81 likes · 5 reposts · 1 replies Open on X →

Alexi Gladstone ✓@AlexiGlad31 Jul

[7/N] Does this hold beyond images? We tested video generation and Masked Diffusion Language Models (MDLMs), alongside the image models from above. In all three, more exploration monotonically improved performance. Some video models improved by over 20%, and MDLMs improved http

15.6K views · 84 likes · 6 reposts · 1 replies Open on X →

Alexi Gladstone ✓@AlexiGlad31 Jul

[5/N] Exploration doesn't just enable end-to-end models, it turns out existing generative models may also need exploration... Breaking down generation doesn't always solve averaging, as each step's prediction can still face many valid outputs at once. Because of this, even ~SOTA

17.5K views · 93 likes · 4 reposts · 2 replies Open on X →

Alexi Gladstone ✓@AlexiGlad31 Jul

[4/N] What happens as models explore more? We trained fully end-to-end models (no breaking generation into steps) on 2D data, images, and text, varying only the amount of exploration K. At K=1 (no exploration), you get the averaging problem where models predict a single dot, a

19.9K views · 92 likes · 3 reposts · 1 replies Open on X →

Alexi Gladstone ✓@AlexiGlad31 Jul

[3/N] Can we avoid breaking down generation? Generative models only have two processes to decompose: generation and training. If we can’t decompose generation, the only thing left to decompose is training… So that's what we do. At each training step, the model generates K https

22.3K views · 119 likes · 7 reposts · 3 replies Open on X →

Alexi Gladstone ✓@AlexiGlad31 Jul

[2/N] How do existing generative models solve this problem? They break down generation into many small steps, so each step has essentially one right output and there is nothing to average. Autoregressive LLMs predict one token at a time. Diffusion denoises a little at a time. h

39.4K views · 109 likes · 6 reposts · 1 replies Open on X →

Alexi Gladstone ✓@AlexiGlad31 Jul

Project Page: https://t.co/rKTx0sZq2j [1/N] Why is generative modeling hard? "Generate a dog" has billions of valid outputs. So training a model to directly predict dog images gives you the average of them all, which is just a brown blur. Capturing all those valid outputs, htt

38K views · 131 likes · 9 reposts · 1 replies Open on X →

Alexi Gladstone ✓@AlexiGlad31 Jul

We discovered a third pretraining axis beyond parameters and data: exploration. Scaling exploration monotonically improves existing models across images/video/language, and unlocks end-to-end generation. In the simplest case, it's just a for loop. Introducing Explorative https

1.1M views · 2.8K likes · 318 reposts · 114 replies Open on X →

Im Vergleich zu Konten gleicher Größe

13 Beiträge aus den letzten 90 Tagen, verglichen mit der Größenklasse under 10K Follower. übliche Reichweite für diese Größe, schwächere Reaktion als bei den meisten.

Medianaufrufe16 381dieses Konto3 752Median für under 10K

Reichweite, %196.86%dieses Konto246.52%Median für under 10K

Interaktion, %0.53%dieses Konto1.41%Median für under 10K

Kennzahl	Dieses Konto	Median für under 10K	Verhältnis
Medianaufrufe pro Beitrag	16 381	3 752	4.37×
Reichweite (Aufrufe ÷ Follower)	196.86%	2.5× audience	0.80×
Interaktionsrate	0.53%	1.41%	0.38×

Weitere Konten dieser Größe → Mit einem anderen Konto vergleichen → Wie diese Vergleichswerte entstehen →

Growth & engagement

How the posts we collected actually performed: views and reaction rate post by post, what the audience did with them, and where the follower count goes.

Views per post

1.1M31 Jul

38K

39.4K

22.3K

19.9K

17.5K

15.6K

16.4K

15.5K

14.7K

15.8K

13.6K

7.1K

Last 13 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

0.30%31 Jul

0.37%

0.29%

0.58%

0.48%

0.57%

0.59%

0.53%

0.48%

0.51%

0.93%

1.32%

0.55%

Reactions — likes, reposts, replies and quotes — divided by views. Median for under 10K accounts is 1.41%.

What the audience does

Likes49.1%3 970 in total

Reposts4.8%388 in total

Replies1.8%145 in total

Bookmarks44.3%3 588 in total

Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.

The follower curve appears once this account has two daily snapshots — we take one a day, and this one is on its first.

Neueste Beiträge

Im Vergleich zu Konten gleicher Größe

Growth & engagement

Views per post

Engagement rate per post

What the audience does

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