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

DeepSeek-R1 is based upon DeepSeek-V3, a mix of specialists (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 team likewise performed knowledge distillation from DeepSeek-R1 to open-source Qwen and Llama designs and launched several versions of each; these designs outperform larger models, including GPT-4, on mathematics and coding standards.

[DeepSeek-R1 is] the initial step towards improving language model thinking capabilities utilizing pure reinforcement learning (RL). Our goal is to check out the potential of LLMs to develop reasoning capabilities without any supervised data, concentrating on their self-evolution through a pure RL process...DeepSeek-R1 ... master a large range of jobs, consisting of creative writing, basic question answering, editing, summarization, and more. Additionally, DeepSeek-R1 shows outstanding performance on tasks needing long-context understanding, considerably outperforming DeepSeek-V3 on long-context standards.

To develop the model, DeepSeek started with DeepSeek-V3 as a base. They first attempted fine-tuning it just with RL, and with no supervised fine-tuning (SFT), producing a design called DeepSeek-R1-Zero, which they have also launched. This design shows strong reasoning performance, but" effective thinking habits, it deals with several concerns. For example, DeepSeek-R1-Zero fights with obstacles like bad readability and language mixing."

To address this, the group utilized a short phase of SFT to prevent 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 procedure converged, they then collected more SFT information utilizing rejection sampling, resulting in a dataset of 800k samples. This dataset was used for further fine-tuning and to produce the distilled designs from Llama and Qwen.

DeepSeek evaluated their model on a range of thinking, math, disgaeawiki.info and coding standards and compared it to other models, including Claude-3.5- Sonnet, engel-und-waisen.de GPT-4o, and bytes-the-dust.com o1. DeepSeek-R1 exceeded 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 couple of 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" classification.

Django structure co-creator Simon Willison blogged about his experiments with among the DeepSeek distilled Llama designs on his blog:

Each response starts with a ... pseudo-XML tag containing the chain of idea used to help create the reaction. [Given the prompt] "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 dreadful. But the process of arriving was such a fascinating insight into how these brand-new designs work.

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

DeepSeek is rapidly emerging as a strong contractor of open models. Not just are these designs great entertainers, but their license allows usage of their outputs for distillation, potentially pressing 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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