DeepSeek Open-Sources DeepSeek-R1 LLM with Performance Comparable To OpenAI's O1 Model
DeepSeek open-sourced DeepSeek-R1, an LLM fine-tuned with support learning (RL) to enhance thinking capability. DeepSeek-R1 attains outcomes on par with OpenAI's o1 model on several criteria, 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 version of RL. The research study group likewise performed knowledge distillation from DeepSeek-R1 to open-source Qwen and Llama designs and released numerous variations of each; these designs exceed bigger designs, consisting of GPT-4, on mathematics and coding standards.
[DeepSeek-R1 is] the initial step towards improving language design reasoning abilities using pure reinforcement knowing (RL). Our objective is to check out the capacity of LLMs to develop reasoning capabilities without any supervised data, focusing on their self-evolution through a pure RL process...DeepSeek-R1 ... excels in a wide variety of jobs, including innovative writing, general question answering, editing, summarization, and more. Additionally, DeepSeek-R1 shows outstanding performance on jobs needing long-context understanding, substantially surpassing DeepSeek-V3 on long-context criteria.
To establish the model, DeepSeek started with DeepSeek-V3 as a base. They first tried fine-tuning it just with RL, and without any supervised fine-tuning (SFT), producing a model called DeepSeek-R1-Zero, which they have also released. This model exhibits strong thinking efficiency, however" powerful thinking habits, it faces a number of problems. For instance, DeepSeek-R1-Zero deals with difficulties like poor readability and language mixing."
To address this, the group utilized a short stage of SFT to prevent the "cold start" problem of RL. They collected numerous thousand examples of chain-of-thought thinking to utilize in SFT of DeepSeek-V3 before running RL. After the RL procedure converged, they then collected more SFT information utilizing rejection sampling, leading to a dataset of 800k samples. This dataset was used for more fine-tuning and to produce the distilled models from Llama and Qwen.
DeepSeek evaluated their design on a variety of thinking, math, and coding criteria and compared it to other designs, consisting of Claude-3.5- Sonnet, GPT-4o, and o1. DeepSeek-R1 surpassed all of them on numerous of the criteria, 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 total in the arena and # 1 in coding and mathematics. It was likewise connected for # 1 with o1 in "Hard Prompt with Style Control" .
Django framework co-creator Simon Willison discussed his experiments with among the DeepSeek distilled Llama designs on his blog site:
Each action begins with a ... pseudo-XML tag containing the chain of idea utilized to help create the reaction. [Given the timely] "a joke about a pelican and a walrus who run a tea room together" ... It then believed for 20 paragraphs before outputting the joke! ... [T] he joke is dreadful. But the procedure of getting there was such an interesting insight into how these new models work.
Andrew Ng's newsletter The Batch wrote about DeepSeek-R1:
DeepSeek is quickly emerging as a strong home builder of open models. Not only are these models excellent entertainers, however their license permits usage of their outputs for distillation, wakewiki.de possibly pushing forward the cutting-edge 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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