GitLab

DeepSeek open-sourced DeepSeek-R1, an LLM fine-tuned with support learning (RL) to improve thinking ability. DeepSeek-R1 attains results on par with OpenAI's o1 design on numerous benchmarks, including 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 design is fine-tuned using Group Relative Policy Optimization (GRPO), a reasoning-oriented variant of RL. The research study group also performed knowledge distillation from DeepSeek-R1 to open-source Qwen and Llama models and released several variations of each; these designs outperform bigger designs, consisting of GPT-4, on math and coding criteria.

[DeepSeek-R1 is] the initial step towards improving language model reasoning capabilities using pure reinforcement learning (RL). Our objective is to explore the potential of LLMs to develop reasoning capabilities with no monitored information, concentrating on their self-evolution through a pure RL process...DeepSeek-R1 ... excels in a large range of tasks, including imaginative writing, basic question answering, modifying, summarization, and more. Additionally, DeepSeek-R1 demonstrates exceptional efficiency on jobs requiring long-context understanding, substantially surpassing DeepSeek-V3 on long-context benchmarks.

To develop the model, DeepSeek began with DeepSeek-V3 as a base. They first tried fine-tuning it just with RL, and it-viking.ch without any supervised fine-tuning (SFT), producing a design called DeepSeek-R1-Zero, which they have likewise released. This model displays strong reasoning performance, but" powerful reasoning habits, it deals with numerous concerns. For circumstances, DeepSeek-R1-Zero fights with obstacles like poor readability and language mixing."

To resolve this, the group used a short phase of SFT to avoid the "cold start" issue of RL. They collected numerous thousand examples of chain-of-thought reasoning to utilize in SFT of DeepSeek-V3 before running RL. After the RL process converged, they then collected more SFT data using rejection sampling, leading to a dataset of 800k samples. This dataset was used for more fine-tuning and to produce the distilled models from Llama and Qwen.

DeepSeek assessed their model on a range of thinking, math, and coding standards and compared it to other designs, 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 couple of days of its release, the LMArena revealed that DeepSeek-R1 was ranked # 3 general in the arena and # 1 in coding and math. It was likewise tied for # 1 with o1 in "Hard Prompt with Style Control" classification.

Django structure co-creator Simon Willison blogged about his try outs among the DeepSeek distilled Llama models on his blog site:

Each action starts with a ... pseudo-XML tag containing the chain of idea utilized 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 horrible. But the process of getting there was such an interesting insight into how these new designs work.

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

DeepSeek is quickly becoming a strong builder of open models. Not just are these designs great entertainers, however their license allows use of their outputs for distillation, possibly the state of the art for language designs (and multimodal models) of all sizes.

The DeepSeek-R1 models are available on HuggingFace.

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Anthony Alford

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