Video Pretraining: Teaching AI to Use Tools Like Humans

Most automation projects fail for a boring reason: the AI never sees enough real human behavior.

In OpenAI’s Minecraft experiment, a model learned to complete multi-step tasks after watching 70,000 hours of human gameplay video—then, with the right fine-tuning, it could craft diamond tools, a long-horizon objective that takes proficient humans 20+ minutes and roughly 24,000 in-game actions. That’s not “AI playing a game.” It’s a clean demonstration of something U.S. software and digital services care about: teaching agents to operate inside existing interfaces—the same kind people use every day.

This post sits in our AI in Robotics & Automation series for a reason. The core idea behind Video PreTraining (VPT) is a blueprint for practical automation: if an agent can learn from how people operate digital environments, it can begin to handle real workflows—customer support consoles, back-office systems, design tools, ticketing queues—without every action being meticulously labeled.

Why VPT matters: unlabeled video is the most underused training data

VPT matters because it turns “watching” into “doing” at scale. The internet is full of demonstrations—tutorials, walkthroughs, screen recordings, training videos—but most of it is unlabeled. You see what happened, not the exact inputs that produced it.

For businesses, that’s the daily reality too:

• Teams record training sessions and enablement calls, but the recordings don’t include the exact clickstream.
• Support organizations have endless screen shares showing how issues were resolved, but not structured action logs.
• Operations teams document procedures in video or SOPs, but the step-by-step interaction data is incomplete.

VPT offers a practical stance: stop waiting for perfect labels. Learn to infer them. That’s a direct parallel to how AI is powering technology and digital services in the United States—especially SaaS platforms trying to automate workflows while keeping humans in the loop.

The real bottleneck in digital automation

If you’ve tried to build an “agent” that does real work, you’ve probably hit one of these walls:

1. Action space explosion: Real interfaces have thousands of possible actions (clicks, drags, shortcuts, text inputs).
2. Long-horizon tasks: Business outcomes aren’t one-step predictions; they’re chains of decisions.
3. Label scarcity: You don’t have “the correct next action” for millions of examples.

Minecraft is a good proxy for this problem because it’s open-ended, messy, and filled with multi-step plans—similar to real-world digital operations.

How Video PreTraining works (and why it’s clever)

VPT works by using a small amount of labeled data to create labels for a huge unlabeled dataset. The pipeline is simple enough to explain without hand-waving:

1. Collect a small contractor dataset where you record both:
   • the video (what the human saw)
   • the actions (keyboard and mouse inputs)
2. Train an Inverse Dynamics Model (IDM) that predicts the action taken at each moment.
3. Use the IDM to label a massive dataset of unlabeled online videos.
4. Train a behavioral cloning policy on those generated labels.

Here’s the key detail that makes the IDM feasible: it can use past and future frames to infer the action.

That sounds minor, but it changes the data economics. Predicting intent from only the past is hard; inferring the action when you can also see the outcome is easier. If you watch someone’s cursor end up on a menu item and the menu opens, it’s much simpler to infer “click” than it is to infer the person’s intention beforehand.

Snippet-worthy take: An inverse dynamics model doesn’t need to guess what a person wanted—it only needs to infer what they did, and future frames reveal the result.

Native human interfaces are the point

A lot of earlier game-agent work simplified the controls to make learning easier. VPT didn’t. It used the native human interface: mouse movement + keypresses at 20Hz.

That choice is exactly why this research maps so well to U.S. digital services. Most companies don’t want to rebuild their tooling around “AI-friendly” APIs. They want AI that can operate the software they already pay for.

What the Minecraft results really show (beyond the hype)

The Minecraft benchmarks show that behavioral priors beat random exploration. With VPT pretraining, the model could do meaningful “early game” behavior zero-shot (no task-specific training):

• collect logs
• craft planks
• craft a crafting table

OpenAI notes that this sequence takes proficient humans about 50 seconds and roughly 1,000 actions. The important part isn’t the exact number—it’s that this is structured, sequential competence without explicit reward shaping.

The model also learned behaviors that look like “common sense” inside the world:

• swimming
• hunting animals
• eating food
• pillar jumping

These aren’t just tricks. They’re evidence that the policy internalized procedural patterns people repeat.

Fine-tuning: where foundation behavior becomes reliable behavior

Fine-tuning turned general skill into repeatable performance. OpenAI fine-tuned the foundation model on a small dataset: contractors played 10 minutes in new worlds and built a simple house.

That fine-tuning:

• improved reliability on early skills
• pushed the agent deeper into the “tech tree” (wooden and stone tools)
• produced occasional shelter construction and basic exploration

If you work in SaaS automation, this is the familiar story:

• Pretraining gives you broad competence.
• Fine-tuning makes it your workflow.

In practice, this looks like taking a general computer-use agent and specializing it for:

• your CRM conventions
• your support macros
• your approval rules
• your compliance constraints

The biggest lesson for AI automation in U.S. SaaS: label what you can, infer the rest

VPT is a playbook for scaling training data without drowning in annotation costs. Most organizations already have mountains of “weakly labeled” behavioral data:

• screen recordings
• meeting recordings
• chat transcripts paired with outcomes
• ticket histories with resolution notes
• event logs that capture partial actions

The VPT approach suggests a strong stance: start with a small, high-quality dataset where you do capture the full action trace (inputs + context). Use it to train a model that can generate usable labels for the rest.

A concrete enterprise analogy

Think of the IDM as a system that learns to translate “what I see on the screen” into “what action probably happened.”

• You instrument a few dozen power users in your support tool.
• You collect their screen + clicks/keystrokes.
• You train an action-inference model.
• You run it over thousands of hours of historical screen recordings.
• Now you have an enormous behavioral cloning dataset.

This is how you go from “we have videos” to “we have training signals.”

What VPT-style agents could automate next

In the U.S. tech and services economy, the near-term winners won’t be companies that promise full autonomy. They’ll be the ones that ship high-volume, low-risk automation inside existing digital environments.

Examples where a VPT-like approach maps well:

• Customer support triage: navigate tickets, classify, request missing info, draft responses
• Revenue operations: update CRM fields, create follow-ups, log call notes, generate quotes
• IT service desks: reproduce common fixes, gather diagnostics, follow SOPs in consoles
• E-commerce operations: handle returns, update order statuses, trigger refunds with checks

These are “Minecraft-like” in the ways that matter: lots of UI actions, long sequences, and messy edge cases.

Reinforcement learning + VPT: the practical hybrid model

Reinforcement learning is strongest when you already have a decent policy. OpenAI’s result makes the contrast sharp:

• RL from scratch (random init) earned almost no reward.
• RL fine-tuned from a VPT policy could craft a diamond pickaxe in 2.5% of 10-minute episodes.

That number is worth sitting with. 2.5% sounds small until you realize the task is extremely long-horizon and requires many prerequisites. In business terms, this is the difference between:

• an agent that never gets past “open the tool”
• an agent that can sometimes complete an end-to-end workflow, and can be improved with targeted training

The stance I take: don’t pick imitation vs RL—stack them

Teams waste time arguing “should we do supervised learning or reinforcement learning?” VPT is the obvious compromise:

1. Imitation learning gives you a human-like prior (safe, sensible defaults).
2. Reinforcement learning optimizes for outcomes once you can define rewards (speed, accuracy, customer satisfaction, cost).

This hybrid is especially relevant in regulated or brand-sensitive contexts (finance, healthcare, government contractors), where you want the agent to behave like your best operators before you optimize for metrics.

Implementation checklist: what to copy from VPT (without building Minecraft)

You can apply the VPT pattern without being a research lab. Here’s what I’ve found works when translating this idea to real automation programs.

1) Choose workflows where the interface is stable

Start with tools that don’t change weekly. UI churn kills action prediction.

Good candidates:

• internal admin consoles
• mature SaaS products with slow UI changes
• standardized forms and queues

2) Collect “gold” data from a small set of experts

You need a short burst of high-quality data:

• 10–50 users
• 30–120 minutes each
• screen + input capture (clicks/keys) + system events

3) Define the action vocabulary early

Minecraft had mouse + keyboard at 20Hz; you need your equivalent.

A pragmatic action schema might include:

• click types (left/right/double)
• element targets (button IDs, field IDs)
• text input events (with redaction policies)
• scroll and navigation events

4) Build guardrails before you scale autonomy

If your agent can operate tools, it can also make mistakes faster.

Guardrails that pay off quickly:

• approval steps for destructive actions
• immutable audit logs
• sandbox environments for training
• policy checks (PII, compliance, prohibited actions)

5) Measure “actions to completion,” not only accuracy

Minecraft used action counts and end goals. For business automation, measure:

• median actions to complete a task
• completion rate within a time budget
• rollback rate (how often humans have to undo changes)
• customer-impact metrics (CSAT, handle time)

Where this is heading in 2026: agents that learn from your organization’s “how”

Video PreTraining is a reminder that the most valuable business knowledge isn’t stored in documents—it’s embedded in behavior. The way your best people navigate tools, sequence steps, and recover from errors is the real asset.

As part of the AI in Robotics & Automation series, I see VPT as a bridge between physical automation and digital automation. Robotics uses demonstration learning to teach manipulation; VPT shows the same strategy works for digital manipulation—mouse, keyboard, and UI state.

If you’re building AI-powered automation in the U.S. SaaS ecosystem, the practical next step is straightforward: pick one workflow, instrument a small expert dataset, and prove you can infer actions from observation. Once you can do that, your training data problem changes overnight.

So here’s the question to end on: when your best operator solves a problem on a screen, are you capturing a video for documentation—or collecting the raw material for an agent that can do it next time?