GitLab

DeepSeek open-sourced DeepSeek-R1, an LLM fine-tuned with reinforcement learning (RL) to improve reasoning ability. DeepSeek-R1 attains outcomes on par with OpenAI's o1 design on numerous benchmarks, including MATH-500 and SWE-bench.

DeepSeek-R1 is based upon DeepSeek-V3, a mix of professionals (MoE) model recently open-sourced by DeepSeek. This base model is fine-tuned utilizing Group Relative Policy Optimization (GRPO), a reasoning-oriented variant of RL. The research group likewise performed knowledge distillation from DeepSeek-R1 to open-source Qwen and Llama models and launched several versions of each; these designs outperform bigger models, including GPT-4, on mathematics and coding standards.

[DeepSeek-R1 is] the initial step towards enhancing language model reasoning capabilities utilizing pure reinforcement knowing (RL). Our objective is to check out the potential of LLMs to develop thinking abilities with no supervised data, concentrating on their self-evolution through a pure RL process...DeepSeek-R1 ... master a wide range of tasks, including creative writing, general question answering, editing, summarization, and more. Additionally, DeepSeek-R1 demonstrates impressive performance on jobs requiring long-context understanding, considerably exceeding DeepSeek-V3 on long-context benchmarks.

To establish the model, DeepSeek started with DeepSeek-V3 as a base. They first tried fine-tuning it only with RL, and without any monitored fine-tuning (SFT), producing a design called DeepSeek-R1-Zero, which they have likewise released. This design shows strong thinking efficiency, however" powerful thinking behaviors, it faces a number of issues. For instance, DeepSeek-R1-Zero has problem with challenges like poor readability and language mixing."

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

DeepSeek evaluated their design 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 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 announced 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 wrote 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 thought utilized to help create the reaction. [Given the timely] "a joke about a pelican and a walrus who run a tea room together" ... It then thought for demo.qkseo.in 20 paragraphs before outputting the joke! ... [T] he joke is awful. But the procedure of getting there was such an interesting insight into how these brand-new models work.

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

DeepSeek is quickly emerging as a strong contractor of open models. Not only are these designs excellent entertainers, however their license permits usage of their outputs for distillation, potentially pressing forward the cutting-edge for language models (and multimodal models) of all sizes.

The DeepSeek-R1 models are available on HuggingFace.

About the Author

Anthony Alford

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