DeepSeek open-sourced DeepSeek-R1, an LLM fine-tuned with reinforcement knowing (RL) to improve thinking ability. DeepSeek-R1 attains results on par with OpenAI's o1 model on several benchmarks, including MATH-500 and SWE-bench.

DeepSeek-R1 is based on DeepSeek-V3, gratisafhalen.be a mixture of specialists (MoE) design just recently open-sourced by DeepSeek. This base model is fine-tuned using Group Relative Policy Optimization (GRPO), a reasoning-oriented version of RL. The research group likewise carried out knowledge distillation from DeepSeek-R1 to open-source Qwen and Llama models and launched several variations of each; these designs surpass bigger designs, including GPT-4, on math and coding criteria.

[DeepSeek-R1 is] the initial step toward enhancing language model reasoning abilities using pure support learning (RL). Our objective is to explore the potential of LLMs to develop thinking abilities with no monitored data, concentrating on their through a pure RL process...DeepSeek-R1 ... excels in a large range of tasks, including innovative writing, basic question answering, editing, summarization, and more. Additionally, DeepSeek-R1 demonstrates exceptional performance on tasks requiring long-context understanding, considerably surpassing DeepSeek-V3 on long-context standards.

To establish the model, DeepSeek started with DeepSeek-V3 as a base. They first attempted fine-tuning it just with RL, and with no supervised fine-tuning (SFT), producing a model called DeepSeek-R1-Zero, which they have also launched. This design exhibits strong reasoning performance, but" effective reasoning behaviors, it deals with several issues. For example, DeepSeek-R1-Zero has problem with obstacles like poor readability and language blending."

To resolve this, the group used a short phase of SFT to avoid the "cold start" problem of RL. They collected several thousand examples of chain-of-thought thinking to utilize in SFT of DeepSeek-V3 before running RL. After the RL process assembled, they then gathered more SFT information using rejection tasting, setiathome.berkeley.edu leading to a dataset of 800k samples. This dataset was utilized for additional fine-tuning and to produce the distilled models from Llama and Qwen.

DeepSeek examined their model on a range of reasoning, mathematics, and coding criteria and compared it to other designs, including Claude-3.5- Sonnet, GPT-4o, and o1. DeepSeek-R1 outshined all of them on several of the criteria, including AIME 2024 and MATH-500.

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

Within a few days of its release, the LMArena announced that DeepSeek-R1 was ranked # 3 general in the arena and # 1 in coding and mathematics. It was also connected for # 1 with o1 in "Hard Prompt with Style Control" category.

Django framework co-creator Simon Willison discussed his try outs one of the DeepSeek distilled Llama models on his blog:

Each action begins with a ... pseudo-XML tag containing the chain of thought used to assist produce the action. [Given the timely] "a joke about a pelican and a walrus who run a tea space together" ... It then thought for 20 paragraphs before outputting the joke! ... [T] he joke is awful. But the process of getting there was such a fascinating insight into how these new models work.

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

DeepSeek is quickly becoming a strong builder of open designs. Not only are these models fantastic entertainers, however their license permits use of their outputs for distillation, potentially pressing forward the cutting-edge for language models (and multimodal designs) of all sizes.

The DeepSeek-R1 designs are available on HuggingFace.

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Anthony Alford

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