GitLab

DeepSeek open-sourced DeepSeek-R1, an LLM fine-tuned with reinforcement learning (RL) to enhance thinking capability. DeepSeek-R1 attains outcomes 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 mixture of specialists (MoE) model just recently open-sourced by DeepSeek. This base model is fine-tuned utilizing Group Relative Policy Optimization (GRPO), a reasoning-oriented version of RL. The research team also performed understanding distillation from DeepSeek-R1 to open-source Qwen and Llama models and released a number of variations of each; these designs outshine bigger designs, consisting of GPT-4, on mathematics and coding criteria.

[DeepSeek-R1 is] the initial step toward improving language model thinking abilities utilizing pure support knowing (RL). Our objective is to explore the potential of LLMs to establish thinking capabilities without any monitored data, concentrating on their self-evolution through a pure RL process...DeepSeek-R1 ... master a large range of tasks, including creative writing, basic question answering, editing, summarization, and more. Additionally, DeepSeek-R1 shows exceptional efficiency on tasks needing long-context understanding, substantially outperforming DeepSeek-V3 on long-context criteria.

To develop the design, DeepSeek began with DeepSeek-V3 as a base. They initially attempted fine-tuning it just with RL, and without any monitored fine-tuning (SFT), producing a model called DeepSeek-R1-Zero, which they have actually also launched. This design shows strong reasoning performance, but" effective thinking habits, it faces a number of issues. For example, DeepSeek-R1-Zero battles with challenges like bad readability and language mixing."

To address this, the team used a short phase of SFT to prevent 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 converged, they then gathered more SFT information using rejection tasting, resulting in a dataset of 800k samples. This dataset was used for further fine-tuning and to produce the distilled designs from Llama and Qwen.

DeepSeek assessed their model on a variety of thinking, mathematics, and coding benchmarks and compared it to other designs, consisting of Claude-3.5- Sonnet, GPT-4o, and o1. DeepSeek-R1 outperformed all of them on numerous of the criteria, consisting of AIME 2024 and setiathome.berkeley.edu 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 connected for # 1 with o1 in "Hard Prompt with Style Control" classification.

Django framework co-creator Simon Willison blogged about his try outs one of the DeepSeek distilled Llama models on his blog:

Each action starts with a ... pseudo-XML tag containing the chain of thought used to assist create the action. [Given the timely] "a joke about a pelican and a walrus who run a tea space together" ... It then thought for 20 paragraphs before the joke! ... [T] he joke is terrible. But the process 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 quickly becoming a strong builder of open models. Not only are these designs terrific entertainers, but their license permits usage of their outputs for distillation, potentially pushing forward the state of the art 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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