GitLab

DeepSeek-R1, an LLM fine-tuned with support learning (RL) to improve thinking capability. DeepSeek-R1 attains results on par with OpenAI's o1 design on numerous benchmarks, consisting of MATH-500 and SWE-bench.

DeepSeek-R1 is based on DeepSeek-V3, a mixture of professionals (MoE) design 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 understanding distillation from DeepSeek-R1 to open-source Qwen and Llama models and launched numerous variations of each; these models outshine bigger designs, consisting of GPT-4, on math and coding benchmarks.

[DeepSeek-R1 is] the initial step toward enhancing language design reasoning abilities using pure reinforcement learning (RL). Our objective is to explore the capacity of LLMs to develop thinking capabilities without any monitored information, focusing on their self-evolution through a pure RL process...DeepSeek-R1 ... master a large variety of jobs, including innovative writing, general question answering, modifying, summarization, and more. Additionally, DeepSeek-R1 shows impressive performance on jobs requiring long-context understanding, significantly outshining DeepSeek-V3 on long-context standards.

To establish the design, DeepSeek started with DeepSeek-V3 as a base. They initially attempted fine-tuning it just with RL, and without any monitored fine-tuning (SFT), producing a design called DeepSeek-R1-Zero, which they have actually likewise released. This design shows strong reasoning performance, but" powerful reasoning behaviors, it faces numerous problems. For example, DeepSeek-R1-Zero has problem with difficulties like poor readability and language blending."

To resolve this, the team utilized a short stage of SFT to prevent the "cold start" problem 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 converged, they then gathered more SFT information utilizing rejection sampling, pipewiki.org resulting in a dataset of 800k samples. This dataset was used for additional fine-tuning and to produce the distilled models from Llama and Qwen.

DeepSeek evaluated their model on a range of thinking, math, and coding benchmarks and compared it to other models, consisting of Claude-3.5- Sonnet, GPT-4o, and o1. DeepSeek-R1 surpassed all of them on numerous of the benchmarks, consisting of 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 connected 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 models 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 a fascinating insight into how these new designs work.

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

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

The DeepSeek-R1 models are available on HuggingFace.

About the Author

Anthony Alford

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