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Breaking down The DeepSeek-R1 Training Process-no PhD Required
DeepSeek just made an advancement: you can train a model to match OpenAI o1-level reasoning utilizing pure support knowing (RL) without using labeled data (DeepSeek-R1-Zero). But RL alone isn’t perfect – it can result in challenges like bad readability. A mix of methods in a multi-stage training fixes these (DeepSeek-R1).
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The launch of GPT-4 permanently altered the AI industry. But today, it seems like an iPhone 4 compared to the next wave of reasoning designs (e.g. OpenAI o1).
These “thinking models” introduce a chain-of-thought (CoT) thinking stage before creating an answer at reasoning time, which in turn improves their thinking performance.
While OpenAI kept their techniques under wraps, DeepSeek is taking the opposite technique – sharing their progress honestly and earning praise for remaining real to the open-source mission. Or as Marc stated it best:
Deepseek R1 is one of the most amazing and excellent advancements I’ve ever seen – and as open source, an extensive gift to the world. This open-source reasoning design is as excellent as OpenAI’s o1 in jobs like mathematics, coding, and rational thinking, which is a substantial win for the open-source community … and the world (Marc, your words not ours!)
As someone who spends a lot of time dealing with LLMs and directing others on how to them, I chose to take a more detailed look at the DeepSeek-R1 training process. Using their paper as my guide, I pieced all of it together and broke it down into something anyone can follow-no AI PhD required. Hopefully you’ll discover it beneficial!
Now, let’s begin with the basics.
A fast primer
To much better understand the backbone of DeepSeek-R1, let’s cover the basics:
Reinforcement Learning (RL): A model finds out by getting rewards or charges based on its actions, improving through trial and error. In the context of LLMs, this can include standard RL approaches like policy optimization (e.g., Proximal Policy Optimization, PPO), value-based techniques (e.g., Q-learning), or hybrid strategies (e.g., actor-critic techniques). Example: When training on a prompt like “2 + 2 =”, the model gets a reward of +1 for outputting “4” and a charge of -1 for any other response. In modern-day LLMs, benefits are typically determined by human-labeled feedback (RLHF) or as we’ll quickly discover, with automated scoring techniques like GRPO.
Supervised fine-tuning (SFT): A base design is re-trained utilizing labeled information to carry out better on a particular task. Example: Fine-tune an LLM using a labeled dataset of client support concerns and responses to make it more accurate in handling typical questions. Great to utilize if you have an abundance of labeled information.
Cold begin information: A minimally labeled dataset utilized to assist the model get a general understanding of the task. * Example: Fine-tune a chatbot with a basic dataset of FAQ pairs scraped from a website to establish a fundamental understanding. Useful when you do not have a great deal of identified data.
Multi-stage training: A model is trained in phases, each concentrating on a specific improvement, such as accuracy or positioning. Example: Train a design on basic text information, then fine-tune it with support learning on user feedback to improve its conversational capabilities.
Rejection sampling: A method where a design creates multiple potential outputs, however only the ones that meet particular requirements, such as quality or relevance, are selected for additional usage. Example: After a RL process, a design generates several responses, but only keeps those that are useful for retraining the model.
First design: DeepSeek-R1-Zero
The group at DeepSeek wished to prove whether it’s possible to train an effective reasoning model utilizing pure-reinforcement learning (RL). This type of “pure” reinforcement learning works without labeled information.
Skipping labeled information? Looks like a bold move for RL worldwide of LLMs.
I’ve discovered that pure-RL is slower upfront (trial and error takes time) – however iteliminates the expensive, time-intensive labeling traffic jam. In the long run, it’ll be much faster, scalable, and way more efficient for developing thinking models. Mostly, due to the fact that they find out by themselves.
DeepSeek did an effective run of a pure-RL training – matching OpenAI o1’s performance.
Calling this a ‘substantial accomplishment” feels like an understatement-it’s the very first time anybody’s made this work. However, maybe OpenAI did it first with o1, but we’ll never ever understand, will we?
The greatest concern on my mind was: ‘How did they make it work?’
Let’s cover what I discovered out.
Using the GRPO RL structure
Traditionally, RL for training LLMs has actually been most effective when integrated with identified information (e.g the PPO RL Framework). This RL approach employs a critic model that’s like an “LLM coach”, providing feedback on each relocation to help the model enhance. It evaluates the LLM’s actions against labeled data, evaluating how likely the design is to prosper (worth function) and directing the model’s total technique.
The difficulty?
This technique is limited by the identified information it utilizes to evaluate decisions. If the identified information is insufficient, biased, or does not cover the full variety of tasks, the critic can just provide feedback within those restrictions – and it will not generalize well.
Enter, GRPO!
The authors utilized the Group Relative Policy Optimization (GRPO) RL framework (developed by the exact same group, wild!) which removes the critic model.
With GRPO, you avoid the ‘coach’- and the LLM relocations are scored over several rounds by utilizing predefined guidelines like coherence and/or fluency. These designs learn by comparing these ratings to the group’s average.
But wait, how did they understand if these guidelines are the ideal rules?
In this approach, the guidelines aren’t perfect-they’re simply a best guess at what “great” looks like. These rules are created to catch patterns that normally make sense, like:
– Does the response make good sense? (Coherence).
– Is it in the right format? (Completeness).
– Does it match the basic design we anticipate? (Fluency).
For example, for the DeepSeek-R1-Zero design, for mathematical jobs, the design might be rewarded for producing outputs that stuck to mathematical concepts or sensible consistency, even without knowing the exact response.
It makes good sense. and it works!
The DeepSeek-R1-Zero model had piece de resistance on thinking benchmarks. Plus it had a 86.7% of pass@1 score on AIME 2024 (a distinguished mathematics competition for high school students), matching the efficiency of OpenAI-o1-0912.
While this looks like the greatest breakthrough from this paper, the R1-Zero model didn’t come with a couple of challenges: poor readability, and language mixing.
Second design: DeepSeek-R1
Poor readability and language blending is something you ‘d anticipate from using pure-RL, without the structure or format provided by identified information.
Now, with this paper, we can see that multi-stage training can mitigate these obstacles. In the case of training the DeepSeek-R1 design, a great deal of training approaches were used:
Here’s a fast explanation of each training stage and what it was done:
Step 1: They fine-tuned a base design (DeepSeek-V3-Base) with countless cold-start information points to lay a solid foundation. FYI, thousands of cold-start information points is a tiny portion compared to the millions or perhaps billions of identified information points normally needed for monitored learning at scale.
Step 2: Applied pure RL (comparable to R1-Zero) to boost reasoning abilities.
Step 3: Near RL convergence, they used rejection sampling where the model developed it’s own labeled data (artificial information) by selecting the finest examples from the last effective RL run. Those rumors you’ve found out about OpenAI utilizing smaller design to create artificial information for the O1 model? This is generally it.
Step 4: The new artificial information was combined with supervised data from DeepSeek-V3-Base in domains like composing, accurate QA, and self-cognition. This action guaranteed the design could learn from both high-quality outputs and varied domain-specific understanding.
Step 5: After fine-tuning with the new data, the model goes through a final RL process throughout varied prompts and situations.
This feels like hacking – so why does DeepSeek-R1 use a multi-stage procedure?
Because each action builds on the last.
For instance (i) the cold start information lays a structured foundation fixing problems like bad readability, (ii) pure-RL establishes reasoning nearly on auto-pilot (iii) rejection sampling + SFT works with top-tier training information that improves precision, and (iv) another final RL stage guarantees additional level of generalization.
With all these extra actions in the training procedure, the DeepSeek-R1 model attains high scores across all criteria noticeable below:
CoT at inference time relies on RL
To efficiently utilize chain-of-thought at inference time, these reasoning models must be trained with approaches like reinforcement knowing that motivate detailed thinking throughout training. It’s a two-way street: for the model to accomplish top-tier reasoning, it needs to use CoT at inference time. And to allow CoT at reasoning, the model must be trained with RL methods.
If we have this in mind, I wonder why OpenAI didn’t reveal their training methods-especially considering that the multi-stage process behind the o1 model appears simple to reverse engineer.
It’s clear they used RL, produced synthetic information from the RL checkpoint, and used some supervised training to enhance readability. So, what did they truly accomplish by decreasing the competition (R1) by simply 2-3 months?
I guess time will tell.
How to utilize DeepSeek-R1
To utilize DeepSeek-R1 you can evaluate it out on their complimentary platform, or get an API key and use it in your code or via AI advancement platforms like Vellum. Fireworks AI also provides an inference endpoint for this design.
The DeepSeek hosted design, costs just $0.55 per million input tokens and $2.19 per million output tokens – making it about 27 times cheaper for inputs and nearly 27.4 times less expensive for outputs than OpenAI’s o1 model.
This API variation supports an optimum context length of 64K, however does not support function calling and JSON outputs. However, contrary to OpenAI’s o1 outputs, you can recover both the “thinking” and the actual response. It’s also very sluggish, however nobody cares about that with these reasoning models, because they open brand-new possibilities where immediate responses aren’t the priority.
Also, this version does not support lots of other specifications like: temperature level 、 top_p 、 presence_penalty 、 frequency_penalty 、 logprobs 、 top_logprobs, making them a bit harder to be used in production.
API example with DeepSeek-R1
The following Python code shows how to utilize the R1 design and access both the CoT process and the final response:
I ‘d suggest you play with it a bit, it’s rather interesting to enjoy it ‘believe’
Small designs can be effective too
The authors also show the thinking patterns of bigger models can be distilled into smaller sized models, resulting in better efficiency.
Using Qwen2.5-32B (Qwen, 2024b) as the base model, direct distillation from DeepSeek-R1 outshines applying simply RL on it. This shows that the thinking patterns discovered by larger base designs are vital for enhancing reasoning abilities for smaller designs. Model distillation is something that is ending up being quite a fascinating approach, shadowing fine-tuning at a big scale.
The results are quite powerful too– A distilled 14B design surpasses advanced open-source QwQ-32B-Preview by a big margin, and the distilled 32B and 70B designs set a brand-new record on the thinking criteria among thick models:
Here’s my take: DeepSeek simply revealed that you can substantially improve LLM thinking with pure RL, no labeled data needed. Even better, they integrated post-training strategies to fix problems and take efficiency to the next level.
Expect a flood of designs like R1 and O1 in the coming weeks-not months.
We believed design scaling struck a wall, however this method is opening brand-new possibilities, implying faster development. To put it in point of view, OpenAI took 6 months from GPT-3.5 to GPT-4.