Understanding DeepSeek R1
DeepSeek-R1 is an open-source language design developed on DeepSeek-V3-Base that's been making waves in the AI neighborhood. Not just does it match-or even surpass-OpenAI's o1 design in many standards, but it likewise comes with totally MIT-licensed weights. This marks it as the very first non-OpenAI/Google design to provide strong thinking capabilities in an open and available manner.
What makes DeepSeek-R1 especially exciting is its openness. Unlike the less-open approaches from some market leaders, DeepSeek has published a detailed training approach in their paper.
The model is also remarkably cost-efficient, with input tokens costing just $0.14-0.55 per million (vs o1's $15) and output tokens at $2.19 per million (vs o1's $60).
Until ~ GPT-4, the typical knowledge was that much better models required more information and compute. While that's still valid, models like o1 and R1 demonstrate an option: inference-time scaling through thinking.
The Essentials
The DeepSeek-R1 paper provided several designs, however main among them were R1 and R1-Zero. Following these are a series of distilled models that, while fascinating, I will not talk about here.
DeepSeek-R1 utilizes 2 major concepts:
1. A multi-stage pipeline where a small set of cold-start information kickstarts the model, followed by massive RL.
2. Group Relative Policy Optimization (GRPO), a reinforcement learning method that depends on comparing several design outputs per prompt to avoid the need for a separate critic.
R1 and R1-Zero are both reasoning designs. This basically suggests they do Chain-of-Thought before answering. For the R1 series of designs, this takes type as believing within a tag, before responding to with a final summary.
R1-Zero vs R1
R1-Zero uses Reinforcement Learning (RL) straight to DeepSeek-V3-Base without any monitored fine-tuning (SFT).
DeepSeek-R1 is an open-source language design developed on DeepSeek-V3-Base that's been making waves in the AI neighborhood. Not just does it match-or even surpass-OpenAI's o1 design in many standards, but it likewise comes with totally MIT-licensed weights. This marks it as the very first non-OpenAI/Google design to provide strong thinking capabilities in an open and available manner.
What makes DeepSeek-R1 especially exciting is its openness. Unlike the less-open approaches from some market leaders, DeepSeek has published a detailed training approach in their paper.
The model is also remarkably cost-efficient, with input tokens costing just $0.14-0.55 per million (vs o1's $15) and output tokens at $2.19 per million (vs o1's $60).
Until ~ GPT-4, the typical knowledge was that much better models required more information and compute. While that's still valid, models like o1 and R1 demonstrate an option: inference-time scaling through thinking.
The Essentials
The DeepSeek-R1 paper provided several designs, however main among them were R1 and R1-Zero. Following these are a series of distilled models that, while fascinating, I will not talk about here.
DeepSeek-R1 utilizes 2 major concepts:
1. A multi-stage pipeline where a small set of cold-start information kickstarts the model, followed by massive RL.
2. Group Relative Policy Optimization (GRPO), a reinforcement learning method that depends on comparing several design outputs per prompt to avoid the need for a separate critic.
R1 and R1-Zero are both reasoning designs. This basically suggests they do Chain-of-Thought before answering. For the R1 series of designs, this takes type as believing within a tag, before responding to with a final summary.
R1-Zero vs R1
R1-Zero uses Reinforcement Learning (RL) straight to DeepSeek-V3-Base without any monitored fine-tuning (SFT).