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DeepSeek R1, the brand-new entrant to the Large Language Model wars has created quite a splash over the last couple of weeks. Its entryway into an area dominated by the Big Corps, while pursuing asymmetric and unique strategies has actually been a refreshing eye-opener.

GPT AI enhancement was starting to show signs of decreasing, and has been observed to be reaching a point of decreasing returns as it runs out of data and compute needed to train, tweak increasingly big models. This has turned the focus towards building "reasoning" designs that are post-trained through support learning, methods such as inference-time and test-time scaling and search algorithms to make the designs appear to think and reason better. OpenAI's o1-series designs were the very first to attain this successfully with its inference-time scaling and Chain-of-Thought reasoning.

Intelligence as an emerging home of Reinforcement Learning (RL)

Reinforcement Learning (RL) has actually been effectively utilized in the past by Google's DeepMind team to build highly smart and customized systems where intelligence is observed as an emerging residential or commercial property through rewards-based training technique that yielded accomplishments like AlphaGo (see my post on it here - AlphaGo: a journey to device instinct).

DeepMind went on to build a series of Alpha * jobs that attained lots of notable accomplishments utilizing RL:

AlphaGo, beat the world champion Lee Seedol in the game of Go
AlphaZero, a system that discovered to play video games such as Chess, Shogi and Go without human input
AlphaStar, attained high efficiency in the complex real-time strategy game StarCraft II.
AlphaFold, a tool for anticipating protein structures which significantly advanced computational biology.
AlphaCode, a model created to produce computer programs, carrying out competitively in coding obstacles.
AlphaDev, a system developed to find novel algorithms, notably optimizing sorting algorithms beyond human-derived techniques.
All of these systems attained mastery in its own location through self-training/self-play and by optimizing and maximizing the cumulative benefit over time by engaging with its environment where intelligence was observed as an emerging property of the system.

RL imitates the procedure through which a child would discover to walk, through trial, mistake and very first concepts.

R1 model training pipeline

At a technical level, DeepSeek-R1 leverages a mix of Reinforcement Learning (RL) and Supervised Fine-Tuning (SFT) for its training pipeline:

Using RL and DeepSeek-v3, an interim reasoning model was developed, called DeepSeek-R1-Zero, simply based on RL without relying on SFT, which showed remarkable reasoning abilities that matched the performance of OpenAI's o1 in certain criteria such as AIME 2024.

The design was however impacted by poor readability and language-mixing and is just an interim-reasoning design constructed on RL principles and self-evolution.

DeepSeek-R1-Zero was then utilized to produce SFT information, which was integrated with supervised information from DeepSeek-v3 to re-train the DeepSeek-v3-Base design.

The new DeepSeek-v3-Base design then went through extra RL with triggers and scenarios to come up with the DeepSeek-R1 model.

The R1-model was then utilized to distill a number of smaller open source models such as Llama-8b, Qwen-7b, 14b which outshined larger designs by a large margin, efficiently making the smaller sized models more available and usable.

Key contributions of DeepSeek-R1

1. RL without the need for SFT for emerging reasoning capabilities
R1 was the very first open research project to confirm the efficacy of RL straight on the base model without counting on SFT as a first action, which led to the model developing innovative reasoning capabilities purely through self-reflection and self-verification.

Although, it did deteriorate in its language capabilities throughout the process, its Chain-of-Thought (CoT) capabilities for solving intricate issues was later on utilized for additional RL on the DeepSeek-v3-Base model which ended up being R1. This is a considerable contribution back to the research community.

The below analysis of DeepSeek-R1-Zero and OpenAI o1-0912 shows that it is feasible to attain robust thinking abilities simply through RL alone, which can be additional augmented with other methods to deliver even better thinking performance.

Its quite intriguing, that the application of RL triggers apparently human capabilities of "reflection", and reaching "aha" minutes, causing it to pause, contemplate and focus on a specific aspect of the problem, resulting in emerging abilities to problem-solve as humans do.

1. Model distillation
DeepSeek-R1 likewise showed that larger models can be distilled into smaller sized designs which makes advanced capabilities available to resource-constrained environments, such as your laptop computer. While its not possible to run a 671b design on a stock laptop, you can still run a distilled 14b design that is distilled from the bigger model which still performs better than the majority of publicly available designs out there. This allows intelligence to be brought more detailed to the edge, to permit faster reasoning at the point of experience (such as on a smart device, or on a Raspberry Pi), which paves way for more use cases and possibilities for innovation.

Distilled designs are extremely different to R1, which is a huge model with an entirely various model architecture than the distilled variants, therefore are not straight similar in terms of ability, but are instead built to be more smaller and efficient for more constrained environments. This method of having the ability to distill a larger design's capabilities down to a smaller design for portability, availability, speed, and expense will cause a great deal of possibilities for using expert system in places where it would have otherwise not been possible. This is another crucial contribution of this technology from DeepSeek, which I believe has even more potential for democratization and availability of AI.

Why is this minute so considerable?

DeepSeek-R1 was a pivotal contribution in lots of ways.

1. The contributions to the state-of-the-art and the open research helps move the field forward where everyone benefits, not simply a couple of extremely funded AI labs developing the next billion dollar design.
2. Open-sourcing and making the design freely available follows an asymmetric method to the prevailing closed nature of much of the model-sphere of the larger players. DeepSeek must be applauded for making their contributions free and classihub.in open.
3. It reminds us that its not simply a one-horse race, and it incentivizes competition, which has already resulted in OpenAI o3-mini a cost-efficient thinking design which now shows the Chain-of-Thought reasoning. Competition is a good idea.
4. We stand at the cusp of a surge of small-models that are hyper-specialized, and optimized for a specific usage case that can be trained and deployed inexpensively for fixing problems at the edge. It raises a great deal of exciting possibilities and is why DeepSeek-R1 is one of the most turning points of tech history.
Truly exciting times. What will you build?