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DeepSeek Open-Sources DeepSeek-R1 LLM with Performance Comparable To OpenAI’s O1 Model
DeepSeek open-sourced DeepSeek-R1, an LLM fine-tuned with reinforcement learning (RL) to enhance thinking capability. DeepSeek-R1 attains results on par with OpenAI’s o1 design on numerous criteria, consisting of MATH-500 and pipewiki.org SWE-bench.
DeepSeek-R1 is based on DeepSeek-V3, a mix of specialists (MoE) model recently open-sourced by DeepSeek. This base model is fine-tuned utilizing Group Relative Policy Optimization (GRPO), a reasoning-oriented variant of RL. The research group also performed understanding distillation from DeepSeek-R1 to open-source Qwen and Llama designs and launched numerous variations of each; these models surpass bigger designs, including GPT-4, on mathematics and coding benchmarks.
[DeepSeek-R1 is] the initial step towards improving language design reasoning abilities using pure support knowing (RL). Our goal is to explore the capacity of LLMs to develop thinking abilities with no monitored data, concentrating on their self-evolution through a pure RL process…DeepSeek-R1 … master a wide variety of jobs, including innovative writing, general concern answering, modifying, summarization, and more. Additionally, DeepSeek-R1 demonstrates exceptional efficiency on tasks needing long-context understanding, considerably outshining DeepSeek-V3 on long-context criteria.
To establish the model, DeepSeek began with DeepSeek-V3 as a base. They initially attempted fine-tuning it just with RL, and with no monitored fine-tuning (SFT), producing a design called DeepSeek-R1-Zero, which they have also launched. This design displays efficiency, however” powerful thinking behaviors, it faces a number of concerns. For instance, DeepSeek-R1-Zero has problem with difficulties like poor readability and language mixing.”
To address this, the group utilized a brief phase of SFT to avoid the “cold start” problem of RL. They collected a number of thousand examples of chain-of-thought reasoning to utilize in SFT of DeepSeek-V3 before running RL. After the RL procedure converged, they then collected more SFT data utilizing rejection sampling, leading to a dataset of 800k samples. This dataset was utilized for further fine-tuning and to produce the distilled models from Llama and Qwen.
DeepSeek evaluated their design on a variety of thinking, mathematics, and coding benchmarks and compared it to other models, including Claude-3.5- Sonnet, GPT-4o, and o1. DeepSeek-R1 outperformed all of them on numerous of the criteria, consisting of AIME 2024 and MATH-500.
DeepSeek-R1 Performance. Image Source: DeepSeek-R1 Technical Report
Within a few days of its release, the LMArena revealed that DeepSeek-R1 was ranked # 3 general in the arena and # 1 in coding and math. It was also connected for # 1 with o1 in “Hard Prompt with Style Control” classification.
Django structure co-creator Simon Willison discussed his experiments with among the DeepSeek distilled Llama models on his blog site:
Each reaction starts with a … pseudo-XML tag containing the chain of thought utilized to assist create the action. [Given the prompt] “a joke about a pelican and a walrus who run a tea space together” … It then believed for 20 paragraphs before outputting the joke! … [T] he joke is dreadful. But the process of arriving was such an intriguing insight into how these brand-new designs work.
Andrew Ng’s newsletter The Batch discussed DeepSeek-R1:
DeepSeek is quickly emerging as a strong contractor of open models. Not just are these designs great entertainers, but their license allows use of their outputs for distillation, possibly pushing forward the cutting-edge for language designs (and multimodal designs) of all sizes.
The DeepSeek-R1 models are available on HuggingFace.
About the Author
Anthony Alford
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