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

DeepSeek-R1 is based on 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 carried out knowledge distillation from DeepSeek-R1 to open-source Qwen and Llama designs and released a number of versions of each; these designs exceed larger designs, including GPT-4, on mathematics and coding standards.

[DeepSeek-R1 is] the primary step towards enhancing language model reasoning abilities using pure support knowing (RL). Our goal is to explore the potential of LLMs to develop thinking abilities with no supervised data, focusing on their self-evolution through a pure RL process...DeepSeek-R1 ... master a large range of jobs, hb9lc.org consisting of imaginative writing, yewiki.org basic concern answering, editing, summarization, and more. Additionally, DeepSeek-R1 demonstrates outstanding performance on jobs needing long-context understanding, pediascape.science considerably outperforming DeepSeek-V3 on long-context standards.

To establish the model, DeepSeek began with DeepSeek-V3 as a base. They first tried fine-tuning it only with RL, it-viking.ch and without any supervised fine-tuning (SFT), producing a design called DeepSeek-R1-Zero, which they have actually also launched. This model displays strong thinking performance, however" powerful thinking behaviors, it faces several issues. For circumstances, DeepSeek-R1-Zero fights with obstacles like poor readability and language blending."

To address this, hb9lc.org the team utilized a brief phase of SFT to prevent the "cold start" problem of RL. They gathered a number of thousand examples of chain-of-thought thinking to use in SFT of DeepSeek-V3 before running RL. After the RL procedure converged, they then collected more SFT information using rejection sampling, leading to a dataset of 800k samples. This dataset was used for further fine-tuning and to produce the distilled models from Llama and Qwen.

DeepSeek assessed their model on a range of reasoning, math, and coding criteria and compared it to other models, including Claude-3.5- Sonnet, GPT-4o, and systemcheck-wiki.de o1. DeepSeek-R1 surpassed all of them on several of the benchmarks, consisting of 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" classification.

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

Each action starts with a ... pseudo-XML tag containing the chain of thought utilized to help create the action. [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 process of arriving was such an interesting insight into how these new designs work.

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

DeepSeek is rapidly emerging as 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 state of the art for language models (and multimodal models) of all sizes.

The DeepSeek-R1 designs are available on HuggingFace.

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Anthony Alford

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