Summary of Inference Scaling Vs Reasoning: An Empirical Analysis Of Compute-optimal Llm Problem-solving, by Marwan Abdelhameed et al.
Inference Scaling vs Reasoning: An Empirical Analysis of Compute-Optimal LLM Problem-Solving
by Marwan AbdElhameed, Pavly Halim
First submitted to arxiv on: 20 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Complexity (cs.CC); 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 synergy between reasoning enhancement and computational efficiency in large language models (LLMs). Recent advances have prioritized accuracy and reasoning capabilities over efficiency, leading to impractical deployment due to computational overhead. The study integrates Quiet-STaR, a self-taught reasoner, and REBASE, a reward-balanced search algorithm, on the Mistral-7B model using the GSM8K dataset. While each method excels in its primary objective – Quiet-STaR achieves superior accuracy (32.03%) but high computational cost, while REBASE provides exceptional efficiency (8.47s runtime) with baseline-comparable accuracy (10.94%) – their integration reveals fundamental challenges reconciling reasoning depth with efficiency. The combined approach results in degraded performance (9.38% accuracy, 143.66s runtime), highlighting the need for novel architectures and algorithms to bridge this gap. The study provides concrete directions for future research in compute-efficient reasoning methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how big language models can be improved. Right now, they’re really good at understanding and answering questions, but it takes them a long time to do it. The researchers tested two different ways of making these models better: one that makes them more accurate (but slow) and another that makes them faster (but not as accurate). When they combined the two approaches, something unexpected happened – the model got worse! This shows that there’s a tricky balance between how smart a model is and how fast it can do things. The study suggests new ways to make models better at both accuracy and speed. |
