In the rapidly evolving field of AI, one major challenge is getting large language models to explain how they arrive at solutions, rather than just spitting out an end result. Without any built‑in reasoning process, models tend to provide final answers—right or wrong—without revealing the logic behind them. That’s a big limitation for users who want to trust and verify a model’s results, especially in high‑stakes scenarios like coding, math, or policy decisions. DeepSeek-R1 aims to address this gap by focusing on chain-of-thought reasoning. It aims to produce AI systems that can:

  1. Show a step‑by‑step rationale behind each conclusion.

  2. Improve their accuracy through reinforcement learning, which rewards careful, correct reasoning rather than just guesswork.

  3. Offer more transparent, user‑friendly outputs—so that the underlying logic isn’t an opaque-box testing.

In other words, DeepSeek-R1 is designed to solve the core problem of opaque AI reasoning—making these models better at thinking out loud, self-checking, and adapting to new tasks in a trustworthy way.

Imagine trying to solve a paradox like the classic chicken or the egg dilemma. At first glance, it seems like a simple question, but unraveling it requires thinking several steps ahead—questioning assumptions, considering cause and effect, and even challenging the obvious. That’s exactly what reasoning in large language models is about. It’s not just predicting the next word; it’s constructing a logical chain of thought that mirrors the way we work through complex puzzles and paradoxes.

In GenAI, reasoning is the process of structuring raw data into coherent, thoughtful problem‑solving. Consider planning a road trip: a basic model might tell you the next turn, but a model that truly reasons maps out the entire route. It anticipates detours, weighs alternative paths, and adapts as conditions change. This holistic approach lets the AI tackle everything from intricate mathematical problems to creative storytelling with a consistency that feels almost human.

DeepSeek‑V3 vs. DeepSeek‑R1‑Zero

Before we see how DeepSeek-R1 improves the chain-of-thought, let’s look at two stepping stones: the previously discussed DeepSeek-V3 and the intermediate model R1-Zero.

As we saw DeepSeek‑V3 is an impressive language model, mostly trained via supervised fine-tuning (SFT) on a wide range of curated examples—code, essays, Q&A, and more. Although it produces neat final answers, it generally won’t:

  • Show its intermediate logic (or “chain of thought”) unless explicitly prompted.

  • Self-check solutions or correct mistakes spontaneously.

The reasoning model aims to tackle that gap using reinforcement learning (RL). The model is rewarded for correct solutions and for presenting a clearly structured reasoning process. As a result, it becomes better at thinking out loud, verifying each step rather than simply giving an end result with no explanation. R1‑Zero began as an attempt to see what happens if we train a base model (DeepSeek‑V3’s foundation) strictly via reinforcement learning without a large, curated dataset for the chain-of-thought. Instead, it uses a reward function that checks the correctness of the final answer and the format obedience.

What is Group Relative Policy Optimization (GRPO)?

R1‑Zero’s training is driven by a specialized RL algorithm called Group Relative Policy Optimization (GRPO). Let’s clarify how that works in simpler terms before we jump into the next steps of the pipeline:

  1. Generate a batch of solutions: The model’s older checkpoint (or old policy) comes up with multiple possible solutions to a problem.

  2. Score each solution: Each solution is given a reward or penalty, depending on correctness (for instance, math solutions that match the answer key or code solutions that pass tests) and formatting (like having chain-of-thought between <think> tags).

  3. Compare against the average: We compute the group’s average reward. Any solution that’s above this average gets a positive push (the model learns from it), while solutions below average get pushed away.

  4. Update the model: Over many iterations, the model competes with its own previous snapshots, continually nudging itself toward better, more reasoned answers.

Unlike some other RL methods (e.g., those needing a large critic model), GRPO estimates a baseline reward by looking at that group’s mean performance. This approach reduces computational overhead and still provides a clear incentive for solutions that outscore the average. Over time, the model self-evolves, discovering that writing out thorough steps can lead to higher rewards—and, thus, better solutions.

However, there are some drawbacks as well. R1-Zero’s outputs were sometimes messy. Without curated chain-of-thought examples, it sometimes spews stream-of-consciousness text, mixes multiple languages, or wanders off-topic. This is intriguing from a research angle but not ideal for user-facing tasks. Hence, DeepSeek tried to do something better, which resulted in DeepSeek-R1.

How has DeepSeek-R1 improved on R1-Zero?

While R1-Zero proved that purely RL-trained models can discover thorough reasoning patterns, it sometimes produced messy, stream-of-consciousness answers. So, the DeepSeek team designed a four-stage pipeline for DeepSeek-R1 that preserves the benefits of R1-Zero’s think-out-loud capabilities but removes its worst quirks.

They start with a cold-start datasetA small, hand‑curated set of step‑by‑step examples used to give the model an initial sense of how to structure its reasoning. of neatly formatted chain-of-thought examples, do reasoning-oriented RL to encourage correctness and structure, filter the best outputs to build an expanded supervised dataset, and finally apply RL for all scenarios to produce a well-rounded, alignment-friendly model.

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