What is Task Decomposition? Task decomposition is the process of breaking down a complex task into smaller, more manageable subtasks. This technique makes it easier to tackle sophisticated problems by dividing them into simpler components that can be addressed independently. For example, if you need to summarize a book, you could break down this larger task into summarizing each chapter individually. Each chapter summary then contributes to the overall summary of the book.

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Decomposing tasks can be thought of as a method of overcoming complexity. Humans navigate the world's complexity by using layers of abstraction, where each layer hides most of the underlying details, allowing us to focus on manageable chunks of information. This ability is important because humans can only keep track of a few pieces of information in their mind simultaneously. Task decomposition helps us solve highly complex problems by breaking them down into simpler subproblems.

What is Recursive Task Decomposition? Recursive task decomposition extends the basic concept by breaking down each sub-task into even more granular subtasks. This iterative process continues until each task is simple enough to solve directly. Continuing with the book summarization example, recursive task decomposition would involve further breaking down each chapter summary into page summaries, and each page summary into paragraph summaries until the task is simple enough to evaluate directly.

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How does task decomposition help generate better training signals? As we mentioned in the earlier sections, when AI systems become more capable, it becomes difficult for humans to provide the right training signals or data, especially for tasks that have subjective evaluation criteria (fuzzy tasks). The core thing that we are trying to do with task decomposition is to reduce the difficulty of providing a training signal to human judgment. Simpler tasks, means that it is easier to provide a training signal, it also means that the tasks are easier to verify. Therefore task decomposition is quite important to the success of many scalable oversight techniques.

Decomposing a task involves breaking it down into smaller, more manageable parts. These parts help you understand and manage the task better, but they may not always be independently solvable or reusable. The key properties we want from a good decomposition include:

Task Decomposition in the learning process. When we break down a complicated task into smaller sub-tasks, each sub-task becomes simpler to understand and solve. This process allows learners to build their knowledge incrementally, focusing on one small piece at a time. As each piece is understood and mastered, the learner gradually constructs a comprehensive understanding of the larger task. Since this principle works well for humans, a natural question is whether we can use something similar in the machine learning process to provide better training signals to our models. This is what we explore in the next sections, trying to emulate the entire human cognitive process through factored cognition.

Factored Cognition #

What is Factored Cognition? Factored cognition is a way to help machine learning (ML ) models replicate human thinking (cognition) by breaking down complex cognitive tasks into smaller subtasks. It leverages this principle of overcoming complexity by decomposing a problem into smaller subproblems that are easier to solve.

By imagining cognition itself as a fuzzy task, we can use task factorization to decompose thinking into a series of tasks and make it possible to train ML models with accurate training signals emulating human cognition. By recording how humans solve problems using explicit actions in narrow contexts, we can train ML systems to imitate these processes. These systems can then serve as trusted assistants, handling more tasks and augmenting human cognitive capacity for evaluation and oversight.

Consider the cognitive task of deciding how to invest $100,000 to achieve the most social good. A human would need to analyze various factors, predict outcomes, and make informed decisions. By subdividing this task into smaller, more manageable subtasks, each of which can be solved using clear training signals, we can delegate these tasks to ML systems.

Key advantages of factored cognition are:

This approach to decomposing problems mirrors human thought. When we think, we often alternate between decomposing and solving problems. By breaking down a problem into smaller parts and solving each part, we manage complexity and make the task more approachable.

Factored cognition is a general approach that can be used in different ways. Later sections will explore iterated distillation and amplification (IDA) and debate both of which use task factoring as a tool in their larger alignment approach to AI Safety.