GitLab

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

DeepSeek-R1 is based on DeepSeek-V3, a mixture of professionals (MoE) model just recently open-sourced by DeepSeek. This base model is fine-tuned using Group Relative Policy Optimization (GRPO), a reasoning-oriented variation of RL. The research study team also performed understanding distillation from DeepSeek-R1 to open-source Qwen and Llama designs and launched numerous variations of each; these designs outshine larger models, consisting of GPT-4, on mathematics and coding criteria.

[DeepSeek-R1 is] the initial step toward enhancing language design thinking capabilities using pure support learning (RL). Our objective is to check out the potential of LLMs to establish thinking capabilities with no supervised information, focusing on their self-evolution through a pure RL process...DeepSeek-R1 ... excels in a wide variety of jobs, consisting of imaginative writing, basic concern answering, modifying, summarization, and more. Additionally, DeepSeek-R1 shows impressive performance on tasks requiring long-context understanding, pipewiki.org substantially outshining DeepSeek-V3 on long-context criteria.

To develop the model, DeepSeek started with DeepSeek-V3 as a base. They first 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 model exhibits strong thinking performance, however" effective thinking habits, it faces several concerns. For example, DeepSeek-R1-Zero has a hard time with difficulties like bad readability and language mixing."

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

DeepSeek evaluated their model on a range of reasoning, math, and coding benchmarks and compared it to other models, including Claude-3.5- Sonnet, GPT-4o, and o1. DeepSeek-R1 surpassed all of them on several of the standards, consisting of AIME 2024 and MATH-500.

DeepSeek-R1 Performance. Image Source: DeepSeek-R1 Technical Report

Within a couple of days of its release, the that DeepSeek-R1 was ranked # 3 overall in the arena and # 1 in coding and mathematics. It was also tied for # 1 with o1 in "Hard Prompt with Style Control" category.

Django framework co-creator Simon Willison discussed his explores among the DeepSeek distilled Llama models on his blog site:

Each action starts with a ... pseudo-XML tag containing the chain of thought used to help create the reaction. [Given the prompt] "a joke about a pelican and a walrus who run a tea space together" ... It then thought for 20 paragraphs before outputting the joke! ... [T] he joke is horrible. 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 quickly emerging as a strong home builder of open models. Not only are these designs terrific entertainers, but their license permits use of their outputs for distillation, pediascape.science potentially pressing forward the cutting-edge for language models (and multimodal designs) of all sizes.

The DeepSeek-R1 models are available on HuggingFace.

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Anthony Alford

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