GitLab

DeepSeek open-sourced DeepSeek-R1, an LLM fine-tuned with support learning (RL) to enhance reasoning capability. DeepSeek-R1 attains outcomes on par with OpenAI's o1 model on several benchmarks, including MATH-500 and SWE-bench.

DeepSeek-R1 is based on 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 version of RL. The research group also performed understanding distillation from DeepSeek-R1 to open-source Qwen and Llama designs and launched a number of variations of each; these models outshine bigger designs, consisting of GPT-4, on math and coding benchmarks.

[DeepSeek-R1 is] the primary step towards improving language model thinking abilities using pure support knowing (RL). Our goal is to check out the potential of LLMs to develop thinking capabilities without any monitored information, concentrating on their self-evolution through a pure RL process...DeepSeek-R1 ... excels in a large range of jobs, consisting of innovative writing, general question answering, modifying, summarization, and more. Additionally, DeepSeek-R1 demonstrates impressive performance on jobs requiring long-context understanding, substantially outshining DeepSeek-V3 on long-context standards.

To establish the design, DeepSeek started with DeepSeek-V3 as a base. They first tried fine-tuning it just with RL, and without any supervised fine-tuning (SFT), a model called DeepSeek-R1-Zero, which they have actually likewise launched. This design exhibits strong reasoning performance, but" powerful reasoning behaviors, it faces numerous problems. For instance, DeepSeek-R1-Zero battles with difficulties like bad readability and language blending."

To resolve this, the group used a brief stage of SFT to prevent 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 process assembled, they then collected more SFT information using rejection tasting, leading to a dataset of 800k samples. This dataset was used for further fine-tuning and to produce the distilled designs from Llama and Qwen.

DeepSeek evaluated their design on a range of thinking, math, and coding criteria and compared it to other designs, consisting of Claude-3.5- Sonnet, GPT-4o, and o1. DeepSeek-R1 outshined 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 couple of 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 tied for # 1 with o1 in "Hard Prompt with Style Control" category.

Django framework co-creator Simon Willison discussed his experiments with among the DeepSeek distilled Llama designs on his blog site:

Each action starts with a ... pseudo-XML tag containing the chain of idea used to assist produce the reaction. [Given the prompt] "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 terrible. But the procedure of arriving was such an interesting insight into how these brand-new designs 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 great entertainers, however their license permits use of their outputs for 89u89.com distillation, possibly pushing forward the cutting-edge for language models (and multimodal models) of all sizes.

The DeepSeek-R1 designs are available on HuggingFace.

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Anthony Alford

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