GitLab

DeepSeek open-sourced DeepSeek-R1, an LLM fine-tuned with support learning (RL) to enhance thinking ability. DeepSeek-R1 attains outcomes on par with OpenAI's o1 design on a number of criteria, including MATH-500 and SWE-bench.

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

[DeepSeek-R1 is] the initial step towards enhancing language model reasoning abilities utilizing pure reinforcement knowing (RL). Our goal is to check out the capacity of LLMs to develop reasoning abilities without any monitored information, focusing on their self-evolution through a pure RL process...DeepSeek-R1 ... excels in a large range of tasks, consisting of innovative writing, basic concern answering, editing, summarization, and more. Additionally, DeepSeek-R1 demonstrates exceptional efficiency on jobs needing long-context understanding, significantly exceeding DeepSeek-V3 on long-context standards.

To develop the design, DeepSeek started with DeepSeek-V3 as a base. They initially attempted fine-tuning it only with RL, and with no monitored fine-tuning (SFT), producing a design called DeepSeek-R1-Zero, which they have likewise released. This design exhibits strong thinking efficiency, but" powerful thinking behaviors, it faces a number of problems. For example, DeepSeek-R1-Zero battles with difficulties like poor readability and language blending."

To address this, the group used a brief phase of SFT to prevent the "cold start" problem of RL. They collected several thousand examples of chain-of-thought reasoning to use in SFT of DeepSeek-V3 before running RL. After the RL process converged, pipewiki.org they then collected more SFT data using rejection sampling, resulting in a dataset of 800k samples. This dataset was used for additional fine-tuning and to produce the distilled models from Llama and Qwen.

DeepSeek examined their model on a range of thinking, 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 few days of its release, the LMArena revealed that DeepSeek-R1 was ranked # 3 total in the arena and # 1 in coding and math. It was also tied for # 1 with o1 in "Hard Prompt with Style Control" classification.

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

Each action begins with a ... pseudo-XML tag containing the chain of idea used to help generate the response. [Given the prompt] "a joke about a pelican and a walrus who run a tea room together" ... It then thought for 20 paragraphs before outputting the joke! ... [T] he joke is awful. But the process of arriving was such an intriguing insight into how these brand-new designs work.

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

DeepSeek is rapidly emerging as a strong contractor of open designs. Not just are these models great entertainers, but their license allows usage of their outputs for distillation, possibly pushing forward the state of the art for language models (and multimodal models) of all sizes.

The DeepSeek-R1 models are available on HuggingFace.

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Anthony Alford

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