Sleep-time Compute: Beyond Inference Scaling at Test-time
Introduces sleep-time compute to reduce test-time compute requirements by pre-computing context, improving accuracy.
Key Findings
Methodology
The paper introduces a novel computation method called sleep-time compute, which pre-computes inferences on context before user queries arrive, significantly reducing test-time compute requirements. Researchers modified the GSM-Symbolic and AIME datasets to create stateful versions and introduced Multi-Query GSM-Symbolic to handle multiple related queries within the same context.
Key Results
- On Stateful GSM-Symbolic and Stateful AIME, sleep-time compute reduced test-time compute requirements by approximately 5x, while increasing accuracy by 13% on Stateful GSM-Symbolic and 18% on Stateful AIME.
- By handling multiple related queries within the same context, the average cost per query was reduced by 2.5x.
- Analysis showed that the predictability of user queries is highly correlated with the effectiveness of sleep-time compute.
Significance
This research significantly reduces the compute cost and latency of large language models at test-time by introducing sleep-time compute. This method is particularly suitable for tasks requiring frequent reasoning, such as document question-answering, code debugging, and conversational assistants. By pre-processing context before user queries, models can respond more quickly, enhancing user experience.
Technical Contribution
The paper innovates on existing test-time compute methods by introducing sleep-time compute, which leverages context inference before user queries to reduce test-time compute requirements. This method effectively reduces overall compute costs by sharing context inference results.
Novelty
Sleep-time compute is the first to introduce the concept of leveraging context inference before user queries, significantly reducing compute costs and latency compared to traditional test-time compute methods.
Limitations
- In scenarios where user queries are unpredictable, sleep-time compute may be less effective as pre-computed results may not directly address actual queries.
- The method relies on the availability and quality of context; incomplete or inaccurate context may affect inference results.
Future Work
Future research could explore applying sleep-time compute to more task types and further optimize the pre-computation process to enhance efficiency and accuracy.
AI Executive Summary
In the application of large language models, scaling test-time compute has become crucial for solving complex problems. However, this approach often comes with high latency and cost. This paper introduces a novel computation method called sleep-time compute, which pre-computes inferences on context before user queries arrive, significantly reducing test-time compute requirements.
Researchers modified the GSM-Symbolic and AIME datasets to create stateful versions and introduced Multi-Query GSM-Symbolic to handle multiple related queries within the same context. Experimental results show that sleep-time compute reduced test-time compute requirements by approximately 5x on Stateful GSM-Symbolic and Stateful AIME, while increasing accuracy by 13% on Stateful GSM-Symbolic and 18% on Stateful AIME.
The introduction of this method not only reduces compute costs and latency but also opens new possibilities for the widespread use of large language models in applications such as document question-answering, code debugging, and conversational assistants. Future research could explore applying sleep-time compute to more task types and further optimize the pre-computation process to enhance efficiency and accuracy.
Deep Analysis
Background
As large language models are increasingly applied to complex reasoning tasks, scaling test-time compute has become key to enhancing model performance. However, this approach often results in high latency and cost, especially in tasks requiring frequent reasoning. Existing methods primarily focus on sequential or parallel scaling of test-time compute, but these methods fail to effectively address the issues of compute cost and latency.
Core Problem
In most applications, user queries and context information are provided to the model simultaneously, leading to redundant computation for each query, increasing compute cost and latency. Thus, reducing compute cost without increasing latency is a significant research challenge.
Innovation
The proposed sleep-time compute method pre-computes inferences on context before user queries arrive, significantly reducing test-time compute requirements. Unlike traditional methods, this approach leverages the predictability and shareability of context, allowing multiple related queries to be handled within the same context, thereby reducing overall compute costs.
Methodology
- �� Pre-compute inferences on existing context before user queries arrive, generating a new context representation.
- �� At test-time, combine pre-computed context with user queries for rapid inference.
- �� Share context inference results across multiple queries to reduce average compute cost.
Experiments
Researchers used modified GSM-Symbolic and AIME datasets to evaluate the effectiveness of sleep-time compute. The experimental design included comparing standard test-time compute and sleep-time compute performance across different datasets and analyzing the impact of query predictability on compute efficiency.
Results
Experimental results show that sleep-time compute reduced test-time compute requirements by approximately 5x on Stateful GSM-Symbolic and Stateful AIME, while increasing accuracy by 13% on Stateful GSM-Symbolic and 18% on Stateful AIME. Additionally, handling multiple related queries within the same context reduced the average cost per query by 2.5x.
Applications
Sleep-time compute is suitable for tasks requiring frequent reasoning, such as document question-answering, code debugging, and conversational assistants. By pre-processing context before user queries, models can respond more quickly, enhancing user experience.
Limitations & Outlook
Despite its effectiveness in reducing compute cost and latency, sleep-time compute may be less effective in scenarios where user queries are unpredictable. Additionally, the method relies on the availability and quality of context; incomplete or inaccurate context may affect inference results.
Plain Language Accessible to non-experts
Imagine a factory where workers are busy during the day, and at night, machines automatically perform maintenance and preparation for more efficient work the next day. Sleep-time compute is like the factory's nighttime preparation. By pre-computing inferences on context before user queries arrive, the model is like having everything ready in advance, waiting for user needs. This way, when users ask questions, the model can provide answers more quickly, just like a factory ready to start work in the morning.
ELI14 Explained like you're 14
Imagine you're playing a game with many levels, each with different challenges. Usually, you spend time at the start of each level learning the rules and strategies. But if you had a helper who analyzed each level's rules and best strategies before you played, you could progress faster. That's what sleep-time compute does! It prepares in advance so you can get help faster when you need answers.
Glossary
Large Language Model
An AI model capable of understanding and generating natural language text, typically used for complex language tasks.
In this paper, large language models are used for reasoning and computation at test-time.
Test-time Compute
The computation process that occurs when user queries arrive, typically used to generate answers or perform reasoning.
The paper explores how sleep-time compute reduces the need for test-time compute.
Sleep-time Compute
The computation process that occurs before user queries arrive, pre-computing inferences on context to reduce test-time compute needs.
The paper introduces sleep-time compute to lower test-time compute costs.
Stateful Task
Tasks that require reasoning using persistent context information.
The paper creates stateful tasks by modifying datasets to evaluate sleep-time compute.
Multi-Query
The ability to handle multiple related queries within the same context.
The paper introduces Multi-Query GSM-Symbolic to evaluate sleep-time compute effectiveness.
Open Questions Unanswered questions from this research
- 1 How to improve sleep-time compute effectiveness when user queries are unpredictable?
- 2 How to apply sleep-time compute to more types of tasks?
- 3 How to further optimize the pre-computation process for efficiency and accuracy?
Applications
Immediate Applications
Document Question-Answering
By pre-processing document context before user queries, models can answer questions more quickly, enhancing user experience.
Long-term Vision
Intelligent Assistants
By analyzing conversation history before user interaction, intelligent assistants can respond more intelligently to user needs, providing personalized services.
Abstract
Scaling test-time compute has emerged as a key ingredient for enabling large language models (LLMs) to solve difficult problems, but comes with high latency and inference cost. We introduce sleep-time compute, which allows models to "think" offline about contexts before queries are presented: by anticipating what queries users might ask and pre-computing useful quantities, we can significantly reduce the compute requirements at test-time. To demonstrate the efficacy of our method, we create modified versions of two reasoning tasks - Stateful GSM-Symbolic and Stateful AIME. We find that sleep-time compute can reduce the amount of test-time compute needed to achieve the same accuracy by ~ 5x on Stateful GSM-Symbolic and Stateful AIME and that by scaling sleep-time compute we can further increase accuracy by up to 13% on Stateful GSM-Symbolic and 18% on Stateful AIME. Furthermore, we introduce Multi-Query GSM-Symbolic, which extends GSM-Symbolic by including multiple related queries per context. By amortizing sleep-time compute across related queries about the same context using Multi-Query GSM-Symbolic, we can decrease the average cost per query by 2.5x. We then conduct additional analysis to understand when sleep-time compute is most effective, finding the predictability of the user query to be well correlated with the efficacy of sleep-time compute. Finally, we conduct a case-study of applying sleep-time compute to a realistic agentic SWE task.