GitLab

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

DeepSeek-R1 is based on DeepSeek-V3, a mix of professionals (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 group also carried out knowledge distillation from DeepSeek-R1 to open-source Qwen and Llama models and released several versions of each; these designs exceed bigger designs, consisting of GPT-4, on mathematics and coding criteria.

[DeepSeek-R1 is] the primary step towards enhancing language design reasoning capabilities utilizing pure reinforcement learning (RL). Our objective is to check out the capacity of LLMs to establish reasoning capabilities without any monitored data, focusing on their self-evolution through a pure RL process...DeepSeek-R1 ... master a wide variety of tasks, writing, basic question answering, modifying, summarization, and more. Additionally, DeepSeek-R1 demonstrates impressive efficiency on tasks 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 initially tried fine-tuning it only with RL, and with no supervised fine-tuning (SFT), producing a model called DeepSeek-R1-Zero, which they have actually likewise launched. This model displays strong reasoning efficiency, however" powerful thinking habits, it faces numerous concerns. For example, DeepSeek-R1-Zero deals with challenges like bad readability and language mixing."

To resolve this, the team utilized a brief phase of SFT to prevent the "cold start" issue of RL. They gathered a number of thousand examples of chain-of-thought reasoning to utilize in SFT of DeepSeek-V3 before running RL. After the RL procedure assembled, they then gathered more SFT information utilizing rejection tasting, resulting in a dataset of 800k samples. This dataset was utilized for additional fine-tuning and to produce the distilled models from Llama and Qwen.

DeepSeek examined their model on a variety of reasoning, math, and coding standards and compared it to other designs, including Claude-3.5- Sonnet, GPT-4o, and o1. DeepSeek-R1 exceeded all of them on numerous of the criteria, including AIME 2024 and MATH-500.

DeepSeek-R1 Performance. Image Source: forum.batman.gainedge.org 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 also connected for # 1 with o1 in "Hard Prompt with Style Control" category.

Django framework co-creator Simon Willison discussed his experiments with one of the DeepSeek distilled Llama designs on his blog:

Each response starts with a ... pseudo-XML tag containing the chain of idea used to help produce the action. [Given the timely] "a joke about a pelican and a walrus who run a tea space together" ... It then believed for 20 paragraphs before outputting the joke! ... [T] he joke is awful. But the procedure of getting there was such a fascinating insight into how these new designs work.

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

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

The DeepSeek-R1 models are available on HuggingFace.

About the Author

Anthony Alford

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