The Emergence of DeepSeek-R1: A New Chapter in AI

The DeepSeek-R1 model is poised to change the AI landscape. By applying reinforcement learning (RL) starting from a checkpoint that has already been fine-tuned on 600,000 reasoning-related Chain-of-Thought (CoT) examples (plus 200,000 additional CoT samples unrelated to direct reasoning), it demonstrates a key insight:

• Modern AI breakthroughs arise when a model’s filtered CoT outputs become its own training data.

• This “train yourself how to think” method relies on success criteria such as linguistic consistency, accuracy, and a refined chain-of-thought format.

• The model is graded on these outputs and optimized through RL—so the longer and more iteratively it “thinks,” the more intelligent it becomes.

Why Chain-of-Thought Matters

Chain-of-Thought (CoT) approaches have gained attention because they allow models to explicitly show their step-by-step reasoning. Until now, these internal reasoning steps were often hidden, making AI a “black box.” DeepSeek-R1 upends that convention. It aggregates large-scale CoT samples and uses them to self-reflect and refine its reasoning. From this iterative process, intelligence emerges in new and often surprising ways.

Interestingly, this approach even reveals quirks such as switching between languages while reasoning—an unexpected yet potentially enlightening side effect of fine-tuning on diverse data. The bigger point is that capturing and monitoring these reasoning tokens has immense value. By collecting CoT samples and continuously training on them, models can become more accurate and adaptable.

CoT + GRPO + RL = Breakthrough

DeepSeek-R1 uses Chain-of-Thought to explain its reasoning step by step, combined with GRPO (a policy optimization technique similar to PPO) and reinforcement learning. This enables a new breakthrough in efficient test-time training for large language models (LLMs). The workflow looks like this:

1. CoT: The model is prompted to produce step-by-step reasoning. Try to find the best chain of thought to get the answer.

2. Rewarding Good Reasoning: It receives rewards for coherence, accuracy, and proper formatting of its thought process. Rule based rewards system ensures policy optimization to produce the best output.

3. Refining via RL: Through iterative feedback, the model learns to improve not just its final answers but the process (sample, assign rewards, compare and update) by which it arrives at them.

In other words, it’s the trail-and-error cognitive thought process that counts. When a model is encouraged to “show its work” and then reflect on it—while receiving feedback on correctness and clarity—intelligence begins to emerge organically.

Not a Brand-New Concept, but a Game Changer Nonetheless

Reinforcement learning is not new. It’s the engine behind AlphaGo and is heavily used in robotics. What sets DeepSeek-R1 apart is the sheer scale of 800,000 CoT samples used during test-time data generation. Much of this data is synthetic—the model is effectively generating its own training examples and learning from them. This same tactic can be applied to other models, likely improving their performance across a range of benchmarks.

How Intelligence Emerges

The core idea is that intelligence springs from a backpropagated system of policies and rewards:

• Each reasoning step refines the model’s ability to predict and adapt.

• By approximating conditional probabilities, the model navigates complex vector spaces—like light refracting through a crystal—continuously tweaking its parameters to minimize loss.

• Rather than searching for a “one-shot” solution, the model discovers an optimal path through reflective iterations: attributing blame for errors and assigning credit when success is achieved. Over time, these adjustments yield that “aha!” moment when the best combination of steps emerges.

Universal Concepts: Blame, Policy, Reward, Credit

Interestingly, the same feedback loops we use to refine a cooking recipe or to learn how to drive also underpin AI training. In a sense, when a neural network is exposed to step-by-step reasoning, it can apply these loops in both deterministic tasks (e.g., math problem-solving) and more open-ended ones (e.g., creative writing). This is where deep reinforcement learning offers a new paradigm:

• Inference and real-time experimentation generate specialized reasoning data

• This data enables greater general intelligence, as the network continuously iterates on what works and discards what doesn’t.

Scalable, Reproducible, and Powerful

DeepSeek-R1’s approach is reproducible and can be distilled to smaller models, yielding substantial gains even on a fraction of the original scale. In fact, domain-specific models trained on CoT examples are already showing promise for specialized tasks. Open-source models stand to benefit tremendously, as more researchers realize that with enough reasoning data samples, you can fine-tune your own OSS model to a high level of performance.

A New Era of Reasoning-Focused AI

Ultimately, the takeaway is that inference and data are now the prime catalysts for the next wave of AI intelligence. For deterministic fields like math, a 7B-parameter model can achieve surprisingly strong results with only a modest dataset (e.g., 8,000 math-focused CoT samples). By systematically gathering and training on these chain-of-thought outputs, and rewarding the model for clarity and correctness; we usher in an era where self-reflective AI training is both accessible and transformative.

DeepSeek-R1 is now available on chat.response.cx with inference powered by Groq.