The key findings from the research paper are:

• Sleeptime compute allows AI models to preprocess context and make inferences before being prompted, reducing the latency and cost of test-time compute.
• Sleeptime compute can match or outperform test-time compute in terms of accuracy, while using 5 times less compute resources.
• The effectiveness of sleeptime compute is dependent on the predictability of the queries based on the provided context. It works best when the queries are closely related to the context.
• For more difficult questions, test-time compute may still outperform sleeptime compute, but the increased cost of test-time compute can be significant.
• Scaling up the sleeptime compute budget can further improve the performance, with up to 13-18% gains observed in the benchmarks.
• Amortizing the cost of sleeptime compute across multiple queries can reduce the average cost per query by up to 2.5 times.

Overall, the sleeptime compute approach presents a promising way to optimize the performance and cost of AI models, especially in scenarios where the queries are predictable based on the provided context.

Test time scaling has been identified as a new scaling law, where the more test time compute you throw at problems, the better the output from the models. However, there are two main problems with test time compute:

1. Latency: The thinking process takes time, which can range from seconds to minutes, depending on the complexity of the problem. This latency can be problematic for use cases that require quick responses.

2. Cost: The GPU processing required for the thinking process is expensive, costing up to tens of dollars per query.

These drawbacks are partly due to the fact that the current approach to applying test time compute assumes that problems are stateless. This means that the models have to start over and understand the context every single time they run inference against a prompt. In practice, many LLM applications are inherently stateful and work in conjunction with persisted, reused context, such as in the case of document question-answering, coding agents, and conversational assistants.

The paper proposes a solution called "sleeptime compute," which allows the model to preprocess the context and figure out potential questions and answers before the user even prompts the model. This approach can significantly reduce the latency and cost of test time compute, as the model can provide the answers more efficiently by leveraging the precomputed context.

The key idea behind sleeptime compute is to enable AI models to precompute and store insights about the context before a user query is presented. This allows the model to provide responses with lower latency and reduced computational cost, compared to the traditional approach of processing the full context at query time.

The main benefits of sleeptime compute are:

1. Reduced Latency: By precomputing insights about the context, the model can respond to user queries more quickly, without the need to perform extensive reasoning at query time.

2. Lower Computational Cost: The precomputation can be done during periods of lower demand on the computational resources, reducing the cost of the high-demand query processing.

3. Improved Accuracy: In many cases, the precomputed insights can enhance the model's understanding of the context, leading to more accurate responses.

The paper demonstrates that sleeptime compute can outperform traditional test-time compute approaches, especially for queries that are predictable based on the context. The authors also show that the benefits of sleeptime compute can be amplified by scaling up the precomputation budget, and by amortizing the precomputation cost across multiple queries.

Overall, the introduction of sleeptime compute represents a promising direction for improving the efficiency and performance of large language models in real-world applications.

The researchers conducted extensive benchmarking to evaluate the performance of their sleeptime compute approach against traditional test-time compute methods. They used two different benchmarks to assess the tradeoffs:

1. Stateless Benchmark: In this benchmark, the user provides a query along with the full context. The researchers tested both reasoning models (01, 03 mini, CLA 3.7, Sonnet, DeepSeek R1) and non-reasoning models (GPT-4 mini, GPT-4) on this benchmark.

   • For the non-reasoning models, the researchers varied the test-time verbosity to control the amount of compute used.
   • The results showed that at lower test-time budgets, sleeptime compute significantly outperformed the baseline, achieving comparable performance with 5 times less compute.

2. Stateful Benchmark: This benchmark reflects more realistic scenarios where the context is persistent and can be reused across multiple queries.

   • For the reasoning models, the researchers found that sleeptime compute consistently outperformed parallel sampling (asking for multiple responses and selecting the best one) at the same test-time token budget.
   • Scaling up the sleeptime compute budget improved performance by 13-18% on the benchmarks, demonstrating the benefits of increased pre-processing.
   • The researchers also found that the more predictable the questions are based on the context, the more effective sleeptime compute becomes.

Overall, the benchmarking results indicate that sleeptime compute can be a more effective and cost-efficient approach compared to traditional test-time compute, especially in settings where the queries are predictable based on the context. However, for more challenging or unpredictable queries, test-time compute may still be the better option.

The researchers found that scaling up the amount of sleeptime compute can significantly improve the performance of reasoning models. By varying the "reasoning effort" during the sleeptime compute prompt, they were able to shift the performance outwards, improving it by 13% at similar test-time budgets.

This demonstrates that for tasks with more complicated contexts, additional sleeptime compute can be beneficial. The more time the model is given to pre-process the context during sleeptime, the better its accuracy on the actual queries.

The researchers also explored amortizing the cost of sleeptime compute across multiple queries. By performing the pre-processing once and reusing the learned context, they were able to reduce the average cost per question by 2.5 times, compared to relying solely on test-time compute.

However, the researchers note that sleeptime compute may be less effective in settings where the queries are challenging to predict or unrelated to the context. In such cases, the pre-processing work done during sleeptime may not be as useful. An interesting direction for future work is identifying which contexts are likely to have predictable questions, and allocating compute resources accordingly between sleeptime and test-time.

The paper discusses how the cost of sleeptime compute can be amortized across multiple queries, reducing the average cost per question. By performing the pre-processing of the context during the sleeptime, the model can reuse this learned context for subsequent queries, avoiding the need to reprocess the entire context at test time.

The key benefit of this approach is that the test-time inference, which is the most expensive and latency-sensitive part of the process, can be optimized by leveraging the pre-computed context. This allows the model to respond to user queries with low latency and at a lower overall cost, compared to the traditional approach of performing all the reasoning at test time.

The paper states that by amortizing sleeptime compute against multiple queries, the authors were able to reduce the average cost per question by two and a half times. This significant cost reduction is achieved by performing the more expensive pre-processing during the sleeptime, when the GPU resources are less in demand and therefore less costly.

The authors also note that the more predictable the questions are based on the provided context, the more effective sleeptime compute becomes. This is because the pre-processing can focus on the most relevant aspects of the context, allowing the model to better anticipate and respond to the user's queries.

Overall, the ability to amortize sleeptime compute across multiple queries is a key advantage of this approach, enabling more efficient and cost-effective use of computational resources while maintaining the accuracy and performance of the model.

The effectiveness of sleeptime compute is influenced by several key factors:

1. Predictability of Queries: The more predictable the queries are based on the provided context, the more effective sleeptime compute becomes. When the queries are closely related to the context, the pre-processing done during sleeptime can be leveraged effectively.

2. Complexity of Context: For tasks with more complicated contexts, additional sleeptime compute can be beneficial. Scaling up the sleeptime compute budget improves performance, especially at similar test-time budgets.

3. Latency vs. Cost Tradeoff: Sleeptime compute can provide significant cost savings compared to test-time compute, which can be up to 10 times more expensive during high-demand periods. This makes sleeptime compute an attractive option for latency-optimized inference.

4. Amortization of Sleeptime Compute: By performing the pre-processing once during sleeptime and reusing the learned context for multiple queries, the cost of sleeptime compute can be amortized, leading to a reduction in the average cost per query.

5. Comparison to Parallel Sampling: Sleeptime compute consistently outperforms parallel sampling (asking the model for multiple responses and choosing the best one) at the same test-time token budget, demonstrating its effectiveness as a scaling approach.

In summary, the effectiveness of sleeptime compute is influenced by the predictability of queries, the complexity of the context, the tradeoff between latency and cost, the ability to amortize sleeptime compute, and its performance compared to parallel sampling.

The key findings from the research paper on "sleeptime compute" can be summarized as follows:

• Sleeptime compute allows AI models to preprocess and make inferences about the context before being prompted, reducing the latency and cost of test-time compute.
• For tasks where the queries are predictable based on the context, sleeptime compute can significantly improve performance compared to standard test-time compute, with up to 13-18% accuracy improvements.
• Sleeptime compute is most effective when the context is stable and the queries are predictable. For more unpredictable or difficult queries, standard test-time compute may still be preferable.
• By amortizing the cost of sleeptime compute across multiple queries on the same context, the average cost per query can be reduced by up to 2.5 times.
• The researchers hypothesize that identifying contexts with predictable queries and allocating compute between sleeptime and test-time is an interesting direction for future work.

Overall, the paper demonstrates that sleeptime compute is a promising approach to improve the efficiency and performance of large language models, especially in settings where the context and queries are well-aligned.