DeepSeek R1: First Impressions and Reactions

While reading the DeepSeek R1 paper during the day, all I could think about was R1-Zero.

R1-Zero came into being from nothing: from a base model to a reasoning model, from a seedling to maturity, from weakness to strength, from the primitive to the modern, from breadth to specialization, from chaos to order.

It symbolizes something primal, vital, and upward-striving. In it I saw a new kind of hope, the image of life bursting forth in all directions.

But when I came home that night and saw the slush on the ground under the dim moonlight, I thought instead of death.

Over the course of its life, R1 died twice.

The first death was R1-Zero.

We know that large models undergo catastrophic forgetting during training. Old knowledge is slowly lost. The whole internet it once saw as a base model grows increasingly blurred, until it remembers only the thinking it repeats day after day, all for the sake of solving one more problem correctly.

Standard models also run into this problem during post-training. People call it the alignment tax.

The usual solution is some form of model merging: applying exponential averaging directly to the parameters during training, so that past abilities are preserved as much as possible.

But such methods are suited only to small-scale exploration of thought. The model is already an SFT model; it is merely pacing back and forth inside the little circle drawn by KL divergence, dragging its feet without ever leaving the ground, terrified of falling. This cannot point toward true intelligence.

A true warrior dares to stride forward. Falls, thorns, cliffs, and precipices do not frighten it. R1-Zero existed to open a new world for machine thought.

It succeeded, but the price was a body covered with scars.

By the time R1-Zero learned to think, it had already accumulated too much baggage. As reinforcement learning continued, its probability distribution kept contracting, gradually concentrating into a handful of fixed patterns of thought. Mixed into those patterns was chaotic, unreadable language, which became increasingly rigid as training went on.

And so it died. It returned to the tribe, pointed in a direction, left behind several thousand pieces of prophecy-like, messy, hard-to-read synthetic data for later generations, or later models, and then withdrew from the stage of history.

The second death was R1-SFT.

With the experience from that earlier exploration, a brand-new model began its own journey through thought.

First it sorted through the data from R1-Zero, removing most of the dross and preserving only the essence. It also drew from other sources, bringing in some long CoT from V3 Instruct to balance its ways of thinking.

Then it began a new round of exploration. Starting from its predecessor’s experience, it quickly showed even greater potential. At last, through round after round of search, it reached a new height.

Yet such a specialized model still could not be used directly. So DeepSeek collected a 600K CoT dataset, added V3’s 200K chat training set, and only then did the true embryo of R1 emerge. After another period of reinforcement learning, R1 was finally born.

The earliest life had no distinction between death and birth. There was only a piece of protein drifting in the primordial sea. Later, with sex and reproduction, death and inheritance appeared.

Over the full runtime of a complex system, errors accumulate continuously until collapse. At that point, starting over is often the right choice.

This is the new evolutionary dimension of inference-time scaling. Each generation of models explores anew, encounters new problems, and then organizes its own experience into data. The next model can distill from it and move further forward.

R1 is only a beginning. If this flywheel keeps turning, R1.1 and R1.2 will arrive soon.

DeepSeek is surely also building more agent frameworks and reinforcement-learning environments for software engineering. These new reward mechanisms will also bring new forms of intelligence.

Looking up, what lies before us is an endless mountain to climb. And beyond the mountain is a still vaster universe.