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DeepSeek open-sourced DeepSeek-R1, an LLM fine-tuned with support learning (RL) to enhance 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, a mixture of professionals (MoE) design just recently open-sourced by DeepSeek. This base model is fine-tuned utilizing Group Relative Policy Optimization (GRPO), a reasoning-oriented variation of RL. The research group also carried out knowledge distillation from DeepSeek-R1 to open-source Qwen and Llama models and released numerous versions of each; these designs outshine bigger designs, consisting of GPT-4, on math and coding standards.

[DeepSeek-R1 is] the initial step towards improving language model thinking capabilities using pure reinforcement knowing (RL). Our objective is to check out the capacity of LLMs to develop thinking capabilities without any monitored information, concentrating on their self-evolution through a pure RL process...DeepSeek-R1 ... master a wide variety of jobs, including imaginative writing, general question answering, modifying, summarization, and more. Additionally, DeepSeek-R1 shows outstanding efficiency on jobs requiring long-context understanding, considerably exceeding DeepSeek-V3 on long-context standards.

To establish the design, DeepSeek began with DeepSeek-V3 as a base. They first attempted fine-tuning it just with RL, and with no monitored fine-tuning (SFT), producing a model called DeepSeek-R1-Zero, which they have likewise launched. This design displays strong reasoning performance, but" powerful thinking habits, it faces numerous issues. For instance, DeepSeek-R1-Zero has problem with difficulties like bad readability and language blending."

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

DeepSeek examined their design on a variety of thinking, mathematics, and coding benchmarks and compared it to other models, consisting of Claude-3.5- Sonnet, GPT-4o, and o1. DeepSeek-R1 outshined all of them on several of the benchmarks, 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 total 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 framework co-creator Simon Willison composed about his try outs one of the DeepSeek distilled Llama designs on his blog site:

Each action begins with a ... pseudo-XML tag containing the chain of thought used to help generate the response. [Given the prompt] "a joke about a pelican and a walrus who run a tea space together" ... It then thought for pipewiki.org 20 paragraphs before outputting the joke! ... [T] he joke is awful. But the procedure of arriving was such a fascinating insight into how these new models work.

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

DeepSeek is rapidly becoming a strong builder of open designs. Not only are these designs excellent entertainers, but their license allows usage of their outputs for distillation, possibly pressing forward the state of the art for language designs (and multimodal designs) of all sizes.

The DeepSeek-R1 models are available on HuggingFace.

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Anthony Alford

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