GitLab

DeepSeek open-sourced DeepSeek-R1, an LLM fine-tuned with reinforcement learning (RL) to enhance reasoning capability. DeepSeek-R1 attains results on par with OpenAI's o1 model on numerous standards, including MATH-500 and SWE-bench.

DeepSeek-R1 is based on DeepSeek-V3, a mix of experts (MoE) design just recently open-sourced by DeepSeek. This base model is fine-tuned utilizing Group Relative Policy Optimization (GRPO), a reasoning-oriented variation of RL. The research group also performed understanding distillation from DeepSeek-R1 to open-source Qwen and Llama designs and launched several variations of each; these models outperform bigger models, including GPT-4, on math and coding criteria.

[DeepSeek-R1 is] the primary step towards improving language design reasoning abilities using pure support learning (RL). Our goal is to explore the capacity of LLMs to establish thinking abilities without any supervised information, focusing on their self-evolution through a pure RL process...DeepSeek-R1 ... excels in a broad range of tasks, consisting of creative writing, general question answering, modifying, summarization, and more. Additionally, DeepSeek-R1 shows outstanding efficiency on jobs requiring long-context understanding, systemcheck-wiki.de significantly outshining DeepSeek-V3 on long-context benchmarks.

To develop the model, DeepSeek began with DeepSeek-V3 as a base. They initially tried fine-tuning it only with RL, and without any supervised fine-tuning (SFT), producing a model called DeepSeek-R1-Zero, which they have actually likewise . This design exhibits strong reasoning performance, however" effective thinking behaviors, it faces numerous problems. For example, DeepSeek-R1-Zero deals with challenges like poor readability and language blending."

To address this, the team utilized a short phase of SFT to prevent the "cold start" problem of RL. They collected numerous thousand examples of chain-of-thought thinking to utilize in SFT of DeepSeek-V3 before running RL. After the RL process converged, 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 bytes-the-dust.com to produce the distilled models from Llama and Qwen.

DeepSeek assessed their design on a variety of thinking, math, and pipewiki.org coding standards 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 criteria, consisting of AIME 2024 and MATH-500.

DeepSeek-R1 Performance. Image Source: wiki.rolandradio.net 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 mathematics. It was also connected 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:

Each action begins with a ... pseudo-XML tag containing the chain of thought used to assist produce the action. [Given the prompt] "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 terrible. But the procedure of getting there was such a fascinating insight into how these brand-new models work.

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

DeepSeek is rapidly becoming a strong builder of open designs. Not just are these models excellent entertainers, but their license permits 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.

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Anthony Alford

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