GitLab

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

DeepSeek-R1 is based on DeepSeek-V3, a mix 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 group likewise carried out knowledge distillation from DeepSeek-R1 to open-source Qwen and Llama models and surgiteams.com released numerous of each; these designs outperform bigger models, consisting of GPT-4, on math and coding benchmarks.

[DeepSeek-R1 is] the initial step towards improving language design reasoning abilities utilizing pure reinforcement knowing (RL). Our goal is to explore the capacity of LLMs to develop reasoning abilities with no monitored information, concentrating on their self-evolution through a pure RL process...DeepSeek-R1 ... master a wide variety of jobs, consisting of innovative writing, general question answering, editing, summarization, and more. Additionally, DeepSeek-R1 demonstrates exceptional performance on tasks requiring long-context understanding, substantially outperforming DeepSeek-V3 on long-context benchmarks.

To develop the model, DeepSeek started with DeepSeek-V3 as a base. They initially attempted fine-tuning it just with RL, and with no supervised fine-tuning (SFT), producing a model called DeepSeek-R1-Zero, which they have actually also released. This design shows strong thinking performance, however" powerful thinking habits, it deals with numerous problems. For example, DeepSeek-R1-Zero has a hard time with challenges like poor readability and language blending."

To address this, the group used a short stage of SFT to avoid the "cold start" issue of RL. They collected several thousand examples of chain-of-thought thinking to use in SFT of DeepSeek-V3 before running RL. After the RL procedure assembled, they then collected more SFT data using rejection tasting, resulting in a dataset of 800k samples. This dataset was used for more fine-tuning and to produce the distilled designs from Llama and Qwen.

DeepSeek assessed their design on a variety of thinking, math, and coding criteria and compared it to other designs, consisting of Claude-3.5- Sonnet, GPT-4o, and o1. DeepSeek-R1 exceeded all of them on numerous of the standards, 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 announced that DeepSeek-R1 was ranked # 3 total in the arena and # 1 in coding and math. It was likewise connected for # 1 with o1 in "Hard Prompt with Style Control" classification.

Django framework co-creator Simon Willison composed about his try outs among the DeepSeek distilled Llama designs on his blog:

Each reaction begins with a ... pseudo-XML tag containing the chain of thought utilized to assist produce the reaction. [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 arriving was such an interesting insight into how these brand-new models work.

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

DeepSeek is quickly becoming a strong home builder of open designs. Not only are these designs great entertainers, but their license allows usage of their outputs for distillation, potentially pushing forward the cutting-edge for language models (and multimodal models) of all sizes.

The DeepSeek-R1 designs are available on HuggingFace.

About the Author

Anthony Alford

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