GitLab

DeepSeek open-sourced DeepSeek-R1, an LLM fine-tuned with reinforcement learning (RL) to enhance thinking capability. DeepSeek-R1 attains outcomes on par with OpenAI's o1 design on numerous benchmarks, including MATH-500 and SWE-bench.

DeepSeek-R1 is based on DeepSeek-V3, hb9lc.org a mixture of professionals (MoE) model just recently open-sourced by DeepSeek. This base model is fine-tuned using Group Relative Policy Optimization (GRPO), a reasoning-oriented variation of RL. The research group also carried out understanding distillation from DeepSeek-R1 to open-source Qwen and Llama models and released numerous variations of each; these models outshine bigger designs, including GPT-4, on mathematics and coding standards.

[DeepSeek-R1 is] the primary step towards improving language model thinking capabilities utilizing pure support learning (RL). Our objective is to explore the potential of LLMs to develop thinking abilities with no supervised information, concentrating on their self-evolution through a pure RL process...DeepSeek-R1 ... excels in a large range of tasks, including imaginative writing, general concern answering, editing, summarization, and more. Additionally, DeepSeek-R1 demonstrates outstanding efficiency on tasks requiring 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 only with RL, and without any supervised fine-tuning (SFT), producing a design called DeepSeek-R1-Zero, which they have also released. This model shows strong thinking efficiency, but" powerful reasoning habits, it deals with several problems. For circumstances, DeepSeek-R1-Zero battles with difficulties like bad readability and language mixing."

To address this, the group used a brief stage of SFT to avoid the "cold start" issue of RL. They collected numerous thousand examples of chain-of-thought reasoning to use in SFT of DeepSeek-V3 before running RL. After the RL process converged, they then gathered more SFT data using rejection sampling, leading to a dataset of 800k samples. This dataset was utilized for more fine-tuning and to produce the distilled models from Llama and Qwen.

DeepSeek assessed their model on a variety of reasoning, mathematics, and coding standards and compared it to other designs, consisting of Claude-3.5- Sonnet, GPT-4o, and o1. DeepSeek-R1 surpassed all of them on several of the standards, consisting of AIME 2024 and MATH-500.

DeepSeek-R1 Performance. Image Source: DeepSeek-R1 Technical Report

Within a couple of days of its release, the LMArena revealed that DeepSeek-R1 was ranked # 3 general in the arena and # 1 in coding and wiki.asexuality.org math. It was also tied for # 1 with o1 in "Hard Prompt with Style Control" classification.

Django structure co-creator Simon Willison discussed his try outs among the DeepSeek distilled Llama models on his blog:

Each reaction starts with a ... pseudo-XML tag containing the chain of thought used to assist create the action. [Given the timely] "a joke about a pelican and a walrus who run a tea room together" ... It then believed for 20 before outputting the joke! ... [T] he joke is horrible. But the procedure of getting there was such an intriguing insight into how these brand-new designs work.

Andrew Ng's newsletter The Batch blogged about DeepSeek-R1:

DeepSeek is rapidly becoming a strong home builder of open models. Not just are these models fantastic entertainers, however their license allows use of their outputs for distillation, possibly pushing forward the cutting-edge for language designs (and multimodal models) of all sizes.

The DeepSeek-R1 designs are available on HuggingFace.

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Anthony Alford

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