GitLab

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

DeepSeek-R1 is based on DeepSeek-V3, a mix of (MoE) model just 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 study group also carried out understanding distillation from DeepSeek-R1 to open-source Qwen and Llama designs and launched several variations of each; these models exceed larger designs, consisting of GPT-4, on math and coding benchmarks.

[DeepSeek-R1 is] the primary step towards enhancing language model thinking abilities utilizing pure support knowing (RL). Our objective is to check out the capacity of LLMs to establish thinking abilities without any supervised information, concentrating on their self-evolution through a pure RL process...DeepSeek-R1 ... excels in a vast array of tasks, including creative writing, general concern answering, editing, summarization, and more. Additionally, DeepSeek-R1 shows impressive performance on jobs requiring long-context understanding, significantly outperforming DeepSeek-V3 on long-context standards.

To establish the design, DeepSeek began with DeepSeek-V3 as a base. They initially attempted fine-tuning it only with RL, and with no monitored fine-tuning (SFT), producing a model called DeepSeek-R1-Zero, which they have likewise launched. This model displays strong thinking performance, but" effective reasoning behaviors, it faces numerous concerns. For instance, DeepSeek-R1-Zero has a hard time with obstacles like poor readability and language mixing."

To address this, the team used a brief phase of SFT to avoid the "cold start" problem of RL. They gathered several thousand examples of chain-of-thought reasoning to use in SFT of DeepSeek-V3 before running RL. After the RL procedure converged, they then collected more SFT data using rejection tasting, leading to a dataset of 800k samples. This dataset was used for further fine-tuning and to produce the distilled models from Llama and Qwen.

DeepSeek evaluated their design on a variety of reasoning, math, and coding criteria 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 standards, including AIME 2024 and MATH-500.

DeepSeek-R1 Performance. Image Source: DeepSeek-R1 Technical Report

Within a few days of its release, the LMArena announced 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" classification.

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

Each reaction begins with a ... pseudo-XML tag containing the chain of idea utilized 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 dreadful. But the process of getting there was such an interesting insight into how these new models work.

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

DeepSeek is quickly becoming a strong contractor of open models. Not only are these designs terrific entertainers, but their license allows usage of their outputs for distillation, potentially pressing forward the state of the art 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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