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

DeepSeek-R1 is based upon DeepSeek-V3, a mixture of experts (MoE) design recently open-sourced by DeepSeek. This base model is fine-tuned using Group Relative Policy Optimization (GRPO), a reasoning-oriented variant of RL. The research team also carried out knowledge distillation from DeepSeek-R1 to open-source Qwen and Llama models and launched numerous variations of each; these designs outshine larger designs, including GPT-4, on math and coding criteria.

[DeepSeek-R1 is] the very first action toward enhancing language model reasoning capabilities utilizing pure support learning (RL). Our goal is to check out the potential of LLMs to establish reasoning abilities with no monitored data, focusing on their self-evolution through a pure RL process...DeepSeek-R1 ... excels in a vast array of jobs, consisting of innovative writing, general concern answering, editing, summarization, and more. Additionally, DeepSeek-R1 demonstrates impressive efficiency on jobs needing long-context understanding, significantly outperforming DeepSeek-V3 on long-context benchmarks.

To establish the model, DeepSeek began with DeepSeek-V3 as a base. They first attempted fine-tuning it just with RL, and without any supervised fine-tuning (SFT), producing a model called DeepSeek-R1-Zero, which they have likewise launched. This design shows strong reasoning performance, however" effective reasoning behaviors, it deals with a number of concerns. For example, DeepSeek-R1-Zero fights with difficulties like poor readability and language blending."

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

DeepSeek assessed their model on a range of thinking, mathematics, and coding criteria and compared it to other models, including Claude-3.5- Sonnet, 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 forum.batman.gainedge.org math. It was also connected for # 1 with o1 in "Hard Prompt with Style Control" category.

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

Each response starts with a ... pseudo-XML tag containing the chain of thought used to help produce the action. [Given the timely] "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 awful. But the process of getting there was such an interesting insight into how these brand-new designs work.

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

DeepSeek is quickly becoming a strong home builder of open models. Not only are these designs great entertainers, but their license allows usage of their outputs for distillation, potentially pressing forward the state of the art 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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