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True computer use is fully general. FDM-1 uses arrow keys on a computer to steer a car in San Francisco with less than 1 hour of fine-tuning data. The action policy is critical: tuning FDM-1 to drive gets much higher accuracy than tuning just the video encoder on the... show more

Standard Intelligence

10,412 subscribers

72,372 Aufrufe • vor 6 Monaten •via X (Twitter)

Wissenschaft & Technologie

Anya Rossi• Live Now

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14 Kommentare

Profilbild von Standard Intelligence

Standard Intelligencevor 6 Monaten

FDM-1 completes complex tasks and navigates interfaces well enough to use CAD applications. Forking VMs allow us to snapshot when a successful operation completes (extrusion, selection, etc.), letting us apply test-time compute to computer use.

Profilbild von Standard Intelligence

Standard Intelligencevor 6 Monaten

Inspired by VPT (Baker et al), we train an inverse dynamics model (IDM) to predict frame-by-frame computer actions. The IDM leverages 40k hours of contractor data to label 11 million hours of screen recordings—550,000x larger than the largest open-source computer use dataset.

Profilbild von Standard Intelligence

Standard Intelligencevor 6 Monaten

We’ve made two main advances: the ability to train on our 11M+ hour computer action dataset and understand long-context video. Our video encoder can fit nearly two hours of 30FPS, high-resolution video into a 1M token context window, ~50x more efficient than existing SOTA.

Profilbild von Standard Intelligence

Standard Intelligencevor 6 Monaten

Computer use models shouldn't learn from screenshots. We built a new foundation model that learns from video like humans do. FDM-1 can construct a gear in Blender, find software bugs, and even drive a real car through San Francisco using arrow keys.

Profilbild von Standard Intelligence

Standard Intelligencevor 6 Monaten

Here’s the blog post, where you can learn more about how we trained this model:

Profilbild von agniv

agnivvor 6 Monaten

HOLY DUCK

Profilbild von _____

_____vor 6 Monaten

amazing work! any idea why the baseline for the self driving task starts out worse than random guessing though?

Profilbild von Yuze

Yuzevor 6 Monaten

fascinating work on the context compression part! Wondering if the model would be able to know what is a good quality action at system 2 level. eg. when to raise/fall on playing card games 😀

Profilbild von Yuval Grossman

Yuval Grossmanvor 6 Monaten

Can you explain what do you mean here by “tuning the video encoder”?

Profilbild von Samu

Samuvor 6 Monaten

nice

Profilbild von madhavan

madhavanvor 6 Monaten

holy fire

Profilbild von Gary Basin

Gary Basinvor 6 Monaten

> less than 1 hour of fine-tuning data

Profilbild von FallenOne

FallenOnevor 6 Monaten

that's so cool

Profilbild von Haris Poljo

Haris Poljovor 6 Monaten

@comma_ai

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