---
schema: 1
type: term
title: _non-autoregressive_
description: >-
  **_Non-autoregressive_** describes a model that generates output **without
  sequences of tokens**. In this channel's material it is applied to
  **Energy-Based Models (EBMs)**, which — unlike autoregressive LLMs that commit
  to one token at a time — maintain a **first view all the time** and can
  therefore **reassess and choose a different route** when they spot a problem.
  [^c10ebe98-0850-5dfe-8c53-b988c268c606@11]
  [^c10ebe98-0850-5dfe-8c53-b988c268c606@22]
canonical: >-
  https://liquidgold.cc/channel/everyinc/explore/term/non-autoregressive-37d9f28c4fcc
markdown: >-
  https://liquidgold.cc/channel/everyinc/explore/term/non-autoregressive-37d9f28c4fcc.md
llms: null
parent: 'https://liquidgold.cc/channel/everyinc/llms.txt'
channel: everyinc
updated: '2026-10-23T22:12:35.346Z'
language: null
children: []
---


# \_non-autoregressive\_

Canonical: https://liquidgold.cc/channel/everyinc/explore/term/non-autoregressive-37d9f28c4fcc

Parent: [Channel index](https://liquidgold.cc/channel/everyinc/llms.txt)

## Definition

**_Non-autoregressive_** describes a model that generates output **without sequences of tokens**. In this channel's material it is applied to **Energy-Based Models (EBMs)**, which — unlike autoregressive LLMs that commit to one token at a time — maintain a **first view all the time** and can therefore **reassess and choose a different route** when they spot a problem. [^c10ebe98-0850-5dfe-8c53-b988c268c606@11] [^c10ebe98-0850-5dfe-8c53-b988c268c606@22]

In this channel's material, **_non-autoregressive_** describes a model that produces output **without sequences of tokens** — the defining contrast with an LLM, which generates one token after another. It is used specifically to characterize **Energy-Based Models (EBMs)**. [^c10ebe98-0850-5dfe-8c53-b988c268c606@11]The clearest usage comes from the navigation analogy in *The AI Model Built for What LLMs Can't Do*: an **autoregressive LLM** driving a car picks **one direction at a time**, may hallucinate a wrong turn, and **cannot turn back** even when it sees the hole in the road ahead. An EBM, by contrast, is said to have **a first view all the time** — so when it spots a problem, it **can choose a different route**. [^c10ebe98-0850-5dfe-8c53-b988c268c606@22]> There are no sequences of tokens and that's what makes it fundamentally different.That ability to reassess rather than commit token-by-token is presented as the **core of the EBM's non-autoregressive nature**, and the reason it suits **deterministic, verifiable AI** for mission-critical tasks like **code generation** and **chip design**. [^c10ebe98-0850-5dfe-8c53-b988c268c606@126]- _Non-autoregressive_ = **no sequences of tokens** during generation. - It is the property that makes **EBMs fundamentally different from LLMs** in this material. - The payoff: an EBM keeps a **first view all the time** and can **change course**, while an autoregressive LLM **cannot turn back**. - The channel ties it to **verifiable, deterministic AI** for software and hardware correctness.[^c10ebe98-0850-5dfe-8c53-b988c268c606@11]: The AI Model Built for What LLMs Can't Do, from 0:11. [^c10ebe98-0850-5dfe-8c53-b988c268c606@22]: The AI Model Built for What LLMs Can't Do, from 0:22. [^c10ebe98-0850-5dfe-8c53-b988c268c606@126]: The AI Model Built for What LLMs Can't Do, from 2:06.