This research paper examines the effectiveness of using additional computation at inference time to improve the performance of large language models (LLMs) on challenging tasks, such as solving complex mathematical problems. The authors focus on two main strategies for scaling inference-time computation: refining the LLM's output distribution through iterative revisions and searching against a verifier that assesses the correctness of each step in the solution. They find that the efficacy of each strategy depends on the difficulty of the problem, which motivates the development of a “compute-optimal” scaling strategy that adapts the approach based on the prompt's difficulty. The authors demonstrate that these compute-optimal strategies can significantly improve performance compared to traditional baselines, even outperforming larger models trained with more compute. This suggests that in certain settings, investing in test-time compute scaling might be more efficient than solely focusing on scaling model parameters during pre-training.
The meaning of "test time"
Test time refers to the period when a Large Language Model (LLM) is actively working on a given task or prompt, as opposed to the time spent training the model. The paper focuses on how to optimise the computational resources used by an LLM during this test time to enhance its problem-solving abilities.
For example: Here's an example to illustrate the concept of "test time" based on the information from the source you provided:
Let's imagine we have trained an LLM to solve maths word problems. We'll call our model "MathSolver".
Training time: This is the period when we fed MathSolver a massive dataset of maths problems and their solutions. We used vast computational resources to train MathSolver, enabling it to learn patterns and relationships between problem descriptions and their solutions.
Test Time: Now, MathSolver is ready to be tested. We give it a new maths problem it has never seen before, for example: "Sarah has 6 apples and gives 2 to John. How many apples does Sarah have left?" This is the test time – the moment when MathSolver needs to apply its learned knowledge to solve a new problem.
During this test time, we can choose to allocate additional computational resources to MathSolver. The research paper explores various methods for utilising this test-time compute to improve performance. These methods generally fall under two categories:
Improving the problem-solving process (the proposal distribution): We could give MathSolver the ability to try different approaches, revise its initial solution, and refine its answer based on its own intermediate steps, much like a human student might double-check their work. This is conceptually similar to the methods described as 'revisions' in the research paper.
Evaluating the solution quality (using a verifier): We could also equip MathSolver with a separate component, a "verifier", to assess the quality of its proposed solution. The verifier could check the logic and accuracy of each step in MathSolver's solution, providing feedback to guide the model towards a more accurate final answer. This aligns with the methods discussed in the paper as 'verifiers' and 'search'.
The key takeaway is that by strategically utilising additional computational power during test time, even a smaller LLM can potentially achieve higher accuracy on challenging tasks. This challenges the traditional approach of solely focusing on larger pre-trained models.
This research paper examines the effectiveness of using additional computation at inference time to improve the performance of large language models (LLMs) on challenging tasks, such as solving complex mathematical problems. The authors focus on two main strategies for scaling inference-time computation: refining the LLM's output distribution through iterative revisions and searching against a verifier that assesses the correctness of each step in the solution. They find that the efficacy of each strategy depends on the difficulty of the problem, which motivates the development of a “compute-optimal” scaling strategy that adapts the approach based on the prompt's difficulty. The authors demonstrate that these compute-optimal strategies can significantly improve performance compared to traditional baselines, even outperforming larger models trained with more compute. This suggests that in certain settings, investing in test-time compute scaling might be more efficient than solely focusing on scaling model parameters during pre-training.
The meaning of "test time"
Test timerefers to the period when a Large Language Model (LLM) is actively working on a given task or prompt, as opposed to the time spent training the model. The paper focuses on how to optimise the computational resources used by an LLM during this test time to enhance its problem-solving abilities.For example: Here's an example to illustrate the concept of "test time" based on the information from the source you provided:
Let's imagine we have trained an LLM to solve maths word problems. We'll call our model "MathSolver".
Training time: This is the period when we fed MathSolver a massive dataset of maths problems and their solutions. We used vast computational resources to train MathSolver, enabling it to learn patterns and relationships between problem descriptions and their solutions.
Test Time: Now, MathSolver is ready to be tested. We give it a new maths problem it has never seen before, for example: "Sarah has 6 apples and gives 2 to John. How many apples does Sarah have left?" This is the test time – the moment when MathSolver needs to apply its learned knowledge to solve a new problem.
During this test time, we can choose to allocate additional computational resources to MathSolver. The research paper explores various methods for utilising this test-time compute to improve performance. These methods generally fall under two categories:
The key takeaway is that by strategically utilising additional computational power during test time, even a smaller LLM can potentially achieve higher accuracy on challenging tasks. This challenges the traditional approach of solely focusing on larger pre-trained models.