GitLab

DeepSeek open-sourced DeepSeek-R1, an LLM fine-tuned with reinforcement knowing (RL) to enhance thinking capability. DeepSeek-R1 attains results on par with OpenAI's o1 design on a number of criteria, including MATH-500 and SWE-bench.

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

[DeepSeek-R1 is] the initial step toward enhancing language model thinking capabilities utilizing pure support learning (RL). Our objective is to explore the potential of LLMs to establish thinking capabilities without any supervised information, focusing on their self-evolution through a pure RL process...DeepSeek-R1 ... master a wide variety of tasks, including imaginative writing, basic question answering, editing, summarization, and more. Additionally, DeepSeek-R1 demonstrates exceptional performance on tasks requiring long-context understanding, substantially outperforming DeepSeek-V3 on long-context benchmarks.

To develop the design, DeepSeek began with DeepSeek-V3 as a base. They initially tried fine-tuning it just with RL, and with no supervised fine-tuning (SFT), producing a model called DeepSeek-R1-Zero, which they have also launched. This model exhibits strong thinking performance, but" effective reasoning habits, it faces several issues. For circumstances, DeepSeek-R1-Zero fights with challenges like poor readability and language blending."

To address this, the group used a short stage of SFT to prevent the "cold start" problem of RL. They gathered a number of thousand examples of chain-of-thought reasoning 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 utilized for additional fine-tuning and to produce the distilled models from Llama and Qwen.

their design on a range of reasoning, mathematics, and coding benchmarks and compared it to other designs, consisting of Claude-3.5- Sonnet, wavedream.wiki GPT-4o, and o1. DeepSeek-R1 surpassed all of them on several 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 math. It was also connected for # 1 with o1 in "Hard Prompt with Style Control" category.

Django framework co-creator Simon Willison wrote about his experiments with among the DeepSeek distilled Llama models on his blog:

Each response starts with a ... pseudo-XML tag containing the chain of idea utilized to assist create the reaction. [Given the timely] "a joke about a pelican and a walrus who run a tea space together" ... It then believed for 20 paragraphs before outputting the joke! ... [T] he joke is awful. But the process of arriving was such an intriguing insight into how these brand-new designs work.

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

DeepSeek is quickly becoming a strong home builder of open designs. Not only are these designs excellent entertainers, but their license permits usage of their outputs for distillation, possibly pressing 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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