GitLab

DeepSeek open-sourced DeepSeek-R1, setiathome.berkeley.edu an LLM fine-tuned with reinforcement learning (RL) to enhance thinking capability. DeepSeek-R1 attains results on par with OpenAI's o1 model on several criteria, including MATH-500 and SWE-bench.

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

[DeepSeek-R1 is] the first action toward enhancing language design reasoning capabilities using pure support knowing (RL). Our objective is to explore the potential of LLMs to develop reasoning abilities with no supervised data, concentrating on their self-evolution through a pure RL process...DeepSeek-R1 ... master a vast array of tasks, consisting of innovative writing, basic question answering, modifying, summarization, and more. Additionally, DeepSeek-R1 demonstrates impressive efficiency on tasks requiring long-context understanding, considerably outshining DeepSeek-V3 on long-context criteria.

To establish the design, DeepSeek began with DeepSeek-V3 as a base. They first attempted fine-tuning it only with RL, and without any monitored fine-tuning (SFT), producing a model called DeepSeek-R1-Zero, which they have likewise launched. This model displays strong reasoning efficiency, but" powerful thinking behaviors, it faces numerous issues. For example, DeepSeek-R1-Zero battles with difficulties like bad readability and language blending."

To resolve this, the group used a short stage of SFT to avoid the "cold start" issue of RL. They gathered numerous thousand examples of chain-of-thought thinking to utilize in SFT of DeepSeek-V3 before running RL. After the RL process assembled, they then collected more SFT data using rejection tasting, leading to a dataset of 800k samples. This dataset was used for wiki.dulovic.tech further fine-tuning and trademarketclassifieds.com to produce the distilled models from Llama and Qwen.

DeepSeek assessed their model on a range of reasoning, math, and coding benchmarks and it to other models, including Claude-3.5- Sonnet, GPT-4o, trademarketclassifieds.com and o1. DeepSeek-R1 surpassed 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 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" classification.

Django framework co-creator Simon Willison composed about his experiments with one of the DeepSeek distilled Llama designs on his blog:

Each reaction starts with a ... pseudo-XML tag containing the chain of thought used to help produce the response. [Given the prompt] "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 process of getting there was such an interesting insight into how these brand-new designs work.

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

DeepSeek is quickly becoming a strong builder of open models. Not only are these models great entertainers, wiki.snooze-hotelsoftware.de however their license allows use of their outputs for distillation, possibly pushing forward the cutting-edge for language models (and multimodal designs) of all sizes.

The DeepSeek-R1 models are available on HuggingFace.

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Anthony Alford

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