Deepseek R1 accelerates reasoning in language models

A new review shows that while one company was first to push reasoning-enabled language models into the spotlight, another model has kicked research in this area into a higher gear.

Since its release about four months ago, the new model has attracted attention for delivering strong logical reasoning with far fewer training resources than earlier models. Its launch set off a flurry of replication efforts across the industry.

Researchers have now looked at how the new model has shifted the landscape. Their analysis suggests that, while one company set the course, the new model played a major role in speeding up the recent surge of reasoning-focused language models.

One key factor was supervised fine-tuning, where base models are retrained using carefully curated, step-by-step explanations. The analysis found that quality matters more than sheer volume: A few thousand rigorously vetted examples can raise even large models to a high level, while millions of poorly filtered samples yield little improvement.

This challenges the older assumption that deep reasoning always requires massive models. The underlying architecture still sets the upper limits, but reasoning-oriented models can make more efficient use of those resources in some areas.

Reinforcement learning has also become more important for building reasoning skills. Two algorithms stand out: Proximal Policy Optimization and Group Relative Policy Optimization.

Researchers have been testing new approaches to training these models. One effective method is to start with shorter answers and gradually increase their length. Curriculum learning—where tasks get harder step by step—has also shown good results.

Another major trend is bringing reasoning skills into multimodal tasks. Early research has focused on transferring these abilities to image and audio analysis, and so far, reasoning developed in text models often carries over to other areas.

Better reasoning also means new challenges around safety and efficiency. Researchers have been working on ways to prevent unwanted behaviors like “overthinking”.

While reasoning can improve the quality and safety of AI outputs, it can also mean much higher computational demands, rising costs, and sometimes inefficient behavior.

This makes it more important to choose the right tool for the job. For now, there’s no clear consensus on when to use a standard model and when to reach for a reasoning model—except for especially complex logic, science, or coding problems.

Safety is another major concern. Reasoning models may be harder to manipulate thanks to their structured thinking process, but they also come with new risks: If the reasoning logic is manipulated, these systems can still be tricked into producing harmful or problematic outputs—even when safeguards are in place.

The study concludes that the new model has played a key role in speeding up the development of reasoning language models. The authors see these advances as just the beginning, with the next phase focused on expanding reasoning to new applications, improving reliability, and finding even more efficient ways to train these systems.