GitLab

DeepSeek open-sourced DeepSeek-R1, an LLM fine-tuned with support learning (RL) to improve reasoning capability. DeepSeek-R1 attains results on par with OpenAI's o1 design on several standards, wiki.myamens.com consisting of MATH-500 and SWE-bench.

DeepSeek-R1 is based on DeepSeek-V3, a mixture of experts (MoE) design just recently open-sourced by DeepSeek. This base model is fine-tuned using Group Relative Policy Optimization (GRPO), a reasoning-oriented variation of RL. The research study group also performed understanding distillation from DeepSeek-R1 to open-source Qwen and Llama designs and several versions of each; these designs exceed larger models, including GPT-4, on mathematics and coding benchmarks.

[DeepSeek-R1 is] the initial step toward enhancing language design thinking abilities utilizing pure reinforcement learning (RL). Our goal is to check out the potential of LLMs to develop thinking capabilities with no monitored data, concentrating on their self-evolution through a pure RL process...DeepSeek-R1 ... master a large range of jobs, including creative writing, general concern answering, editing, summarization, and more. Additionally, DeepSeek-R1 shows impressive performance on tasks needing long-context understanding, significantly exceeding DeepSeek-V3 on long-context criteria.

To develop the design, DeepSeek started with DeepSeek-V3 as a base. They initially attempted fine-tuning it only with RL, and without any supervised fine-tuning (SFT), producing a design called DeepSeek-R1-Zero, which they have likewise released. This design exhibits strong thinking efficiency, but" effective reasoning behaviors, it faces numerous issues. For instance, DeepSeek-R1-Zero struggles with obstacles like bad readability and language blending."

To address this, the team utilized a brief phase of SFT to avoid the "cold start" issue of RL. They collected several thousand higgledy-piggledy.xyz examples of chain-of-thought reasoning to use in SFT of DeepSeek-V3 before running RL. After the RL procedure assembled, they then collected more SFT information using rejection sampling, resulting in a dataset of 800k samples. This dataset was used for more fine-tuning and to produce the distilled designs from Llama and Qwen.

DeepSeek evaluated their model on a range of reasoning, mathematics, and coding benchmarks and compared it to other models, including 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 few days of its release, the LMArena revealed that DeepSeek-R1 was ranked # 3 general in the arena and # 1 in coding and trademarketclassifieds.com math. It was also tied for # 1 with o1 in "Hard Prompt with Style Control" classification.

Django framework co-creator Simon Willison blogged about his experiments with one of the DeepSeek distilled Llama models on his blog:

Each response begins with a ... pseudo-XML tag containing the chain of idea used to help produce the action. [Given the timely] "a joke about a pelican and a walrus who run a tea space together" ... It then believed for 20 paragraphs before outputting the joke! ... [T] he joke is horrible. But the procedure of arriving was such an interesting insight into how these brand-new models work.

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

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

The DeepSeek-R1 models are available on HuggingFace.

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Anthony Alford

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