GitLab

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

DeepSeek-R1 is based on DeepSeek-V3, a mixture of experts (MoE) model just recently open-sourced by DeepSeek. This base design is fine-tuned using Group Relative Policy Optimization (GRPO), a reasoning-oriented variant of RL. The research group likewise performed knowledge distillation from DeepSeek-R1 to open-source Qwen and Llama designs and launched several variations of each; these designs outperform larger designs, consisting of GPT-4, on math and coding standards.

[DeepSeek-R1 is] the initial step towards improving language model reasoning capabilities using pure reinforcement knowing (RL). Our goal is to check out the potential of LLMs to establish thinking capabilities with no monitored data, concentrating on their self-evolution through a pure RL process...DeepSeek-R1 ... master a vast array of tasks, consisting of imaginative writing, basic concern answering, modifying, summarization, and more. Additionally, DeepSeek-R1 shows impressive efficiency on jobs requiring long-context understanding, considerably exceeding DeepSeek-V3 on long-context criteria.

To develop the design, DeepSeek began with DeepSeek-V3 as a base. They initially tried fine-tuning it just with RL, and with no monitored fine-tuning (SFT), producing a model called DeepSeek-R1-Zero, larsaluarna.se which they have also launched. This design displays strong thinking efficiency, but" effective thinking behaviors, it deals with a number of issues. For example, DeepSeek-R1-Zero has problem with obstacles like bad readability and language blending."

To address this, the group used a short phase of SFT to prevent the "cold start" problem of RL. They collected several thousand examples of chain-of-thought reasoning to utilize in SFT of DeepSeek-V3 before running RL. After the RL procedure assembled, they then gathered more SFT data utilizing rejection sampling, resulting in a dataset of 800k samples. This dataset was utilized for further fine-tuning and to produce the distilled designs from Llama and larsaluarna.se Qwen.

DeepSeek assessed their model on a range of reasoning, mathematics, and coding benchmarks and compared it to other designs, consisting of Claude-3.5- Sonnet, GPT-4o, and o1. DeepSeek-R1 outshined all of them on several of the criteria, 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 overall in the arena and # 1 in coding and math. It was also connected for forum.pinoo.com.tr # 1 with o1 in "Hard Prompt with Style Control" category.

Django framework co-creator Simon Willison wrote 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 assist create the response. [Given the prompt] "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 procedure of arriving was such a fascinating insight into how these new models work.

Andrew Ng's newsletter The Batch discussed DeepSeek-R1:

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

The DeepSeek-R1 designs are available on HuggingFace.

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Anthony Alford

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