Here are the first five sentences from the abstract:
Large Language Models (LLMs) are increasingly adept at complex reasoning, yet many state-of-the-art approaches rely on massive datasets and extensive reinforcement learning (RL) pipelines. In contrast, Stanford's s1 introduces a streamlined, data-efficient method that surpasses previous open-source and open-weights reasoning models-most notably DeepSeek-R1-using only a tiny fraction of the data and compute. A core innovation of s1 is its "s1K" dataset, a meticulously curated set of 1,000 high-quality, step-by-step reasoning examples drawn from challenging math, logic, and science problems. Fine-tuning on this compact dataset required only minutes of GPU time, demonstrating unprecedented sample-and cost-efficiency. A second breakthrough is s1's inference-time "budget forcing" mechanism, which allows controllable test-time scaling.
The following report provides insights into s1 and DeepSeek-R1 that you may find valuable:
From Brute Force to Brain Power: How Stanford's s1 Surpasses DeepSeek-R1
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5130864
Here are the first five sentences from the abstract:
Large Language Models (LLMs) are increasingly adept at complex reasoning, yet many state-of-the-art approaches rely on massive datasets and extensive reinforcement learning (RL) pipelines. In contrast, Stanford's s1 introduces a streamlined, data-efficient method that surpasses previous open-source and open-weights reasoning models-most notably DeepSeek-R1-using only a tiny fraction of the data and compute. A core innovation of s1 is its "s1K" dataset, a meticulously curated set of 1,000 high-quality, step-by-step reasoning examples drawn from challenging math, logic, and science problems. Fine-tuning on this compact dataset required only minutes of GPU time, demonstrating unprecedented sample-and cost-efficiency. A second breakthrough is s1's inference-time "budget forcing" mechanism, which allows controllable test-time scaling.
19 pages