Summary of Scaling Llm Test-time Compute Optimally Can Be More Effective Than Scaling Model Parameters, by Charlie Snell et al.
Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters
by Charlie Snell, Jaehoon Lee, Kelvin Xu, Aviral Kumar
First submitted to arxiv on: 6 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper investigates the scaling of inference-time computation in Large Language Models (LLMs) to improve their outputs. The authors focus on answering whether an LLM can improve its performance on challenging prompts by using a fixed but non-trivial amount of inference-time compute. This question has implications for the achievable performance of LLMs, as well as the future of LLM pretraining and how to tradeoff inference-time and pre-training compute. The authors analyze two primary mechanisms to scale test-time computation: searching against dense, process-based verifier reward models; and updating the model’s distribution over a response adaptively, given the prompt at test time. They find that the effectiveness of different approaches to scaling test-time compute critically varies depending on the difficulty of the prompt. This observation motivates applying a “compute-optimal” scaling strategy, which acts to most effectively allocate test-time compute adaptively per prompt. The authors demonstrate that using this compute-optimal strategy can improve the efficiency of test-time compute scaling by more than 4x compared to a best-of-N baseline. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how Large Language Models (LLMs) can get better at answering questions and completing tasks. Right now, LLMs are really good at some things, but not as good at others. The authors want to know if they can make an LLM better by giving it more “brain power” during the last step of processing a question or task. They tested two ways to do this: one way is like searching through lots of possibilities to find the best answer; and another way is like updating what the model knows based on the prompt. The authors found that which method works best depends on how hard the task is. This means we need to be clever about when to use each method. By using a special strategy, they were able to make an LLM do tasks much more efficiently. |
Keywords
» Artificial intelligence » Inference » Pretraining » Prompt
