GitLab

DeepSeek open-sourced DeepSeek-R1, an LLM fine-tuned with reinforcement learning (RL) to improve thinking ability. DeepSeek-R1 attains outcomes on par with OpenAI's o1 model on a number of benchmarks, consisting of MATH-500 and wavedream.wiki SWE-bench.

DeepSeek-R1 is based upon DeepSeek-V3, a mix of experts (MoE) model just recently open-sourced by . This base design is fine-tuned utilizing Group Relative Policy Optimization (GRPO), a reasoning-oriented version of RL. The research team likewise carried out understanding distillation from DeepSeek-R1 to open-source Qwen and Llama models and released a number of variations of each; these models outshine bigger designs, consisting of GPT-4, on mathematics and coding criteria.

[DeepSeek-R1 is] the initial step toward enhancing language design reasoning abilities utilizing pure support learning (RL). Our goal is to check out the potential of LLMs to establish thinking abilities without any monitored information, concentrating on their self-evolution through a pure RL process...DeepSeek-R1 ... master a wide variety of jobs, including innovative writing, basic concern answering, modifying, summarization, and more. Additionally, DeepSeek-R1 shows outstanding efficiency on jobs requiring long-context understanding, substantially exceeding 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 design called DeepSeek-R1-Zero, which they have actually also launched. This model exhibits strong reasoning efficiency, however" effective reasoning habits, it deals with numerous concerns. For example, DeepSeek-R1-Zero has problem with difficulties like poor readability and language mixing."

To address this, the group utilized a short stage of SFT to prevent the "cold start" problem of RL. They gathered numerous thousand examples of chain-of-thought thinking to utilize in SFT of DeepSeek-V3 before running RL. After the RL process converged, they then collected more SFT information utilizing 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 evaluated their design on a range of thinking, math, and coding benchmarks 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 couple of days of its release, the LMArena announced that DeepSeek-R1 was ranked # 3 general in the arena and # 1 in coding and math. It was likewise tied for # 1 with o1 in "Hard Prompt with Style Control" classification.

Django framework co-creator Simon Willison discussed his explores one of the DeepSeek distilled Llama designs on his blog site:

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

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

DeepSeek is rapidly becoming a strong home builder of open designs. Not only are these designs great entertainers, but their license permits use of their outputs for distillation, potentially pushing 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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