GitLab

DeepSeek open-sourced DeepSeek-R1, an LLM fine-tuned with support knowing (RL) to improve reasoning capability. DeepSeek-R1 attains results on par with o1 model on numerous benchmarks, consisting of MATH-500 and SWE-bench.

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

[DeepSeek-R1 is] the initial step towards enhancing language model thinking capabilities using pure reinforcement knowing (RL). Our goal is to check out the capacity of LLMs to develop reasoning capabilities without any supervised information, concentrating on their self-evolution through a pure RL process...DeepSeek-R1 ... master a wide variety of jobs, consisting of imaginative writing, general concern answering, editing, summarization, and more. Additionally, DeepSeek-R1 demonstrates exceptional performance on tasks 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 only with RL, and with no supervised fine-tuning (SFT), producing a model called DeepSeek-R1-Zero, which they have actually likewise released. This design shows strong thinking performance, but" effective thinking behaviors, it deals with several concerns. For instance, DeepSeek-R1-Zero has problem with challenges like bad readability and language blending."

To address this, the group used a brief phase of SFT to prevent the "cold start" problem of RL. They gathered a number of thousand examples of chain-of-thought reasoning to use in SFT of DeepSeek-V3 before running RL. After the RL procedure converged, they then gathered more SFT information using rejection sampling, resulting in a dataset of 800k samples. This dataset was used for additional fine-tuning and to produce the distilled models from Llama and Qwen.

DeepSeek assessed their design on a variety of reasoning, math, and coding criteria and compared it to other models, including Claude-3.5- Sonnet, GPT-4o, and o1. DeepSeek-R1 exceeded all of them on numerous 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 also tied for # 1 with o1 in "Hard Prompt with Style Control" category.

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

Each response starts with a ... pseudo-XML tag containing the chain of idea used to assist generate the reaction. [Given the timely] "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 dreadful. But the procedure of arriving was such an intriguing insight into how these brand-new designs work.

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

DeepSeek is quickly emerging as a strong contractor of open models. Not just are these models excellent entertainers, however their license allows use of their outputs for distillation, potentially pressing forward the state of the art for language designs (and multimodal models) of all sizes.

The DeepSeek-R1 designs are available on HuggingFace.

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Anthony Alford

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