Source URL: https://simonwillison.net/2025/Apr/6/andriy-burkov/#atom-everything
Source: Simon Willison’s Weblog
Title: Quoting Andriy Burkov
Feedly Summary: […] The disappointing releases of both GPT-4.5 and Llama 4 have shown that if you don’t train a model to reason with reinforcement learning, increasing its size no longer provides benefits.
Reinforcement learning is limited only to domains where a reward can be assigned to the generation result. Until recently, these domains were math, logic, and code. Recently, these domains have also included factual question answering, where, to find an answer, the model must learn to execute several searches. This is how these “deep search" models have likely been trained.
If your business idea isn’t in these domains, now is the time to start building your business-specific dataset. The potential increase in generalist models’ skills will no longer be a threat.
— Andriy Burkov
Tags: generative-ai, llama, openai, ai, llms
AI Summary and Description: Yes
Summary: The text highlights the recent challenges faced in the performance of large language models (LLMs) like GPT-4.5 and Llama 4, emphasizing the importance of reinforcement learning in training for tasks beyond traditional domains. It encourages businesses to focus on developing tailored datasets to enhance model capabilities based on specific requirements.
Detailed Description:
The provided text discusses the limitations of recent advancements in generative AI, specifically pointing out the disappointing performance of models such as GPT-4.5 and Llama 4. Here’s an elaboration on the key points:
– **Challenges in Model Performance**: The text indicates that simply increasing the size of a model is no longer sufficient to enhance its performance. This is particularly relevant for AI practitioners, as it suggests that alternative training methodologies may be necessary to achieve significant improvements.
– **Role of Reinforcement Learning (RL)**:
– Reinforcement learning is highlighted as crucial for training models that exhibit reasoning capabilities.
– The limitation of RL to domains where a reward can be assigned underscores the need for specialized training approaches in non-traditional areas.
– **Emerging Domains for Deep Search Models**:
– Traditionally, RL has been beneficial in mathematical and logical reasoning tasks, but it is now extending to factual question answering, showcasing a shift in the landscape of model training.
– This adaptation to “deep search” models illustrates the growing complexity of tasks that AI is expected to perform.
– **Business Implications**:
– The text advises businesses that are not operating in the traditional RL-friendly domains to start creating their own datasets tailored to specific business needs.
– There is an implication that as generalist models’ skills become more well-defined, they are less likely to threaten specialized, business-focused applications.
– **Strategic Outlook for AI Development**:
– Companies are encouraged to pivot their strategies towards dataset generation to stay competitive as models evolve and become more efficient in handling specific tasks.
Overall, this commentary on the state of generative AI models serves as a call to action for companies dealing with AI to rethink their approach to model training and the data they utilize, providing a strategic insight for AI and infrastructure security professionals who may need to adapt their methodologies accordingly.