GitLab

DeepSeek open-sourced DeepSeek-R1, an LLM fine-tuned with reinforcement learning (RL) to improve thinking ability. DeepSeek-R1 attains outcomes on par with OpenAI's o1 design on numerous criteria, including MATH-500 and SWE-bench.

DeepSeek-R1 is based on DeepSeek-V3, a mixture of specialists (MoE) model just recently open-sourced by DeepSeek. This base design is fine-tuned using Group Relative Policy Optimization (GRPO), a reasoning-oriented version of RL. The research group likewise carried out knowledge distillation from DeepSeek-R1 to open-source Qwen and Llama designs and launched several variations of each; these designs exceed larger models, consisting of GPT-4, on math and .

[DeepSeek-R1 is] the initial step towards improving language model thinking capabilities using pure reinforcement learning (RL). Our goal is to explore the capacity of LLMs to establish reasoning capabilities with no supervised data, concentrating on their self-evolution through a pure RL process...DeepSeek-R1 ... master a wide variety of jobs, consisting of imaginative writing, basic concern answering, editing, summarization, and more. Additionally, DeepSeek-R1 shows outstanding performance on jobs needing long-context understanding, substantially outperforming DeepSeek-V3 on long-context standards.

To establish the model, DeepSeek started with DeepSeek-V3 as a base. They initially tried fine-tuning it only with RL, and with no monitored fine-tuning (SFT), producing a design called DeepSeek-R1-Zero, which they have actually also released. This design shows strong reasoning performance, but" powerful reasoning behaviors, it faces numerous issues. For instance, DeepSeek-R1-Zero has a hard time with difficulties like bad readability and language mixing."

To resolve this, the group utilized a brief phase of SFT to avoid the "cold start" issue of RL. They collected several thousand examples of chain-of-thought reasoning to utilize in SFT of DeepSeek-V3 before running RL. After the RL process assembled, they then gathered more SFT information using rejection tasting, leading to a dataset of 800k samples. This dataset was utilized for further fine-tuning and to produce the distilled designs from Llama and higgledy-piggledy.xyz Qwen.

DeepSeek evaluated their model on a variety of reasoning, oeclub.org math, and coding criteria and compared it to other models, consisting of Claude-3.5- Sonnet, GPT-4o, and o1. DeepSeek-R1 surpassed all of them on several of the benchmarks, trademarketclassifieds.com 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 overall in the arena and # 1 in coding and math. It was also tied for # 1 with o1 in "Hard Prompt with Style Control" category.

Django framework co-creator Simon Willison discussed his explores among the DeepSeek distilled Llama models on his blog site:

Each action starts with a ... pseudo-XML tag containing the chain of idea utilized to help generate the action. [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 horrible. But the procedure of arriving was such a fascinating insight into how these new designs work.

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

DeepSeek is quickly emerging as a strong home builder of open models. Not just are these designs great entertainers, however their license permits usage of their outputs for distillation, possibly pressing forward the cutting-edge for language designs (and multimodal models) of all sizes.

The DeepSeek-R1 designs are available on HuggingFace.

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Anthony Alford

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