GitLab

DeepSeek open-sourced DeepSeek-R1, wiki.dulovic.tech an LLM fine-tuned with reinforcement learning (RL) to enhance reasoning capability. DeepSeek-R1 attains results on par with OpenAI's o1 model on several criteria, including MATH-500 and SWE-bench.

DeepSeek-R1 is based upon DeepSeek-V3, a mix of professionals (MoE) design just recently open-sourced by DeepSeek. This base design is fine-tuned utilizing Group Relative Policy Optimization (GRPO), a reasoning-oriented variation of RL. The research group also performed understanding distillation from DeepSeek-R1 to open-source Qwen and Llama designs and released several variations of each; these designs outshine bigger models, of GPT-4, on math and coding criteria.

[DeepSeek-R1 is] the first action toward improving language model reasoning capabilities using pure support knowing (RL). Our objective is to explore the potential of LLMs to develop reasoning capabilities without any supervised data, focusing on their self-evolution through a pure RL process...DeepSeek-R1 ... master a vast array of jobs, including innovative writing, basic question answering, modifying, summarization, and more. Additionally, DeepSeek-R1 demonstrates outstanding performance on tasks needing long-context understanding, substantially surpassing DeepSeek-V3 on long-context standards.

To develop the design, DeepSeek started with DeepSeek-V3 as a base. They initially tried fine-tuning it just with RL, and without any monitored fine-tuning (SFT), producing a design called DeepSeek-R1-Zero, which they have likewise launched. This model displays strong thinking efficiency, however" effective thinking behaviors, it deals with numerous concerns. For instance, DeepSeek-R1-Zero fights with obstacles like poor readability and language mixing."

To address this, the group used a short stage of SFT to avoid the "cold start" issue of RL. They gathered several thousand examples of chain-of-thought thinking to use in SFT of DeepSeek-V3 before running RL. After the RL process converged, they then collected more SFT information utilizing rejection tasting, resulting in a dataset of 800k samples. This dataset was used for more fine-tuning and to produce the distilled models from Llama and Qwen.

DeepSeek assessed their design on a variety of reasoning, mathematics, and coding criteria and compared it to other models, consisting of Claude-3.5- Sonnet, GPT-4o, and o1. DeepSeek-R1 surpassed 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 couple of days of its release, the LMArena revealed that DeepSeek-R1 was ranked # 3 overall in the arena and # 1 in coding and mathematics. It was likewise connected for # 1 with o1 in "Hard Prompt with Style Control" category.

Django framework co-creator Simon Willison wrote about his try outs among the DeepSeek distilled Llama designs on his blog:

Each action begins with a ... pseudo-XML tag containing the chain of thought used to assist generate the reaction. [Given the timely] "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 brand-new designs work.

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

DeepSeek is quickly becoming a strong home builder of open designs. Not just are these designs fantastic entertainers, however their license permits use of their outputs for distillation, possibly pushing 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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