GitLab

DeepSeek open-sourced DeepSeek-R1, an LLM fine-tuned with reinforcement knowing (RL) to improve thinking capability. DeepSeek-R1 attains outcomes on par with OpenAI's o1 design on several benchmarks, wakewiki.de consisting of MATH-500 and SWE-bench.

DeepSeek-R1 is based on DeepSeek-V3, a mixture of specialists (MoE) design 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 team likewise performed knowledge distillation from DeepSeek-R1 to open-source Qwen and Llama designs and launched several variations of each; these designs exceed bigger models, consisting of GPT-4, on mathematics and coding benchmarks.

[DeepSeek-R1 is] the initial step towards enhancing language model reasoning capabilities utilizing pure reinforcement learning (RL). Our goal is to explore the capacity of LLMs to establish reasoning capabilities without any monitored information, concentrating on their self-evolution through a pure RL process...DeepSeek-R1 ... master a large range of tasks, including creative writing, basic question answering, hb9lc.org modifying, summarization, and more. Additionally, DeepSeek-R1 shows outstanding efficiency on jobs needing long-context understanding, significantly exceeding DeepSeek-V3 on long-context criteria.

To establish the design, DeepSeek started with DeepSeek-V3 as a base. They first tried fine-tuning it just with RL, and with no supervised fine-tuning (SFT), producing a model called DeepSeek-R1-Zero, it-viking.ch which they have also released. This model shows performance, however" powerful reasoning behaviors, it faces several problems. For instance, DeepSeek-R1-Zero has a hard time with challenges like bad readability and language mixing."

To resolve this, the team utilized a brief phase of SFT to prevent the "cold start" problem of RL. They collected 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, resulting in a dataset of 800k samples. This dataset was used for more fine-tuning and to produce the distilled models from Llama and Qwen.

DeepSeek assessed their model on a variety of reasoning, math, and coding benchmarks and compared it to other models, including Claude-3.5- Sonnet, larsaluarna.se GPT-4o, and o1. DeepSeek-R1 surpassed 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 few days of its release, the LMArena revealed that DeepSeek-R1 was ranked # 3 general in the arena and forum.altaycoins.com # 1 in coding and math. It was also tied for # 1 with o1 in "Hard Prompt with Style Control" classification.

Django framework co-creator Simon Willison discussed his try outs 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 help create the response. [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 awful. But the procedure of getting there was such an interesting insight into how these new designs work.

Andrew Ng's newsletter The Batch discussed DeepSeek-R1:

DeepSeek is rapidly emerging as a strong builder of open designs. Not just are these models great entertainers, however their license allows use of their outputs for distillation, potentially pushing forward the cutting-edge for language designs (and multimodal designs) of all sizes.

The DeepSeek-R1 designs are available on HuggingFace.

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Anthony Alford

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