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@KL_Div asserts that XM (Explorative Models) appears to be the same method as…

2026-08-02 · 23:31 UTC ·@KL_Div ·1 min read

Brief

KL_Div argues XM looks equivalent to IMLE and provides background from years of IMLE work in response to Alexi Gladstone's 'Explorative Modeling' thread, which claims exploration is a third pretraining axis that scales with data/parameter/compute (7%→36%, 13%→23%, double at 3× compute) and delivers major efficiency and inference reductions (6.2× data, 4.1× FLOPs, 47% params, 1.43 FID, up to 256× less inference compute).

Why it matters

@KL_Div asserts that XM (Explorative Models) appears to be the same method as IMLE and offers historical context from years of IMLE work.

Key details

  • Alexi Gladstone claims exploration is a third pretraining axis and reports gains that grow with scale: data gains 7%→36%, parameter gains 13%→23%, and gains double at 3× the compute.
  • Adding exploration reportedly improves data efficiency 6.2×, FLOP efficiency 4.1×, parameter efficiency 47%, yields a 1.43 unguided FID on ImageNet, and XMs can match diffusion with up to 256× less inference compute.
Source evidence

Was just as excited as everyone else to read this cool new paper, and it felt like a trip down memory lane!

XM seems to be the same as IMLE, and the motivation and insights are also similar. Having worked on IMLE for years, I'll provide some context for my followers 👇.

1/n

Alexi Gladstone (@AlexiGlad)

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 Modeling.

TLDR:
- Gains from exploration grow with scale: 7%→36% as data scales, 13%→23% as parameters scale, and gains double at 3× the compute
- Adding exploration to ~SOTA baselines improves data efficiency by 6.2×, FLOP efficiency by 4.1×, parameter efficiency by 47%, and hits a near-SOTA 1.43 unguided FID on ImageNet
- Exploration lets you trade training compute for generalization, and scales how end-to-end your generative model is
- End-to-end Explorative Models (XMs) match diffusion performance on control tasks with up to 256× less inference compute

🧵Thread:

— https://nitter.net/AlexiGlad/status/2083230922196107288#m