By 2026: Large language models await a breakthrough in training and memory

CICC analysts predict significant changes in the large language models (LLM) field by 2026. In 2025, we observed significant progress in this area, particularly in logical inference, programming, agent systems, and multimodality. However, models still face stability challenges and a tendency toward hallucinations.

Next year is expected to bring breakthroughs in several key areas. First, this concerns reinforcement learning, which will enable models to better adapt to complex tasks and make more optimal decisions. Additionally, improvements in model memory are expected, allowing them to process more information and better retain previous experiences. Finally, context optimization will enable models to more effectively leverage available information to solve tasks.

These improvements will yield several important consequences. First, models will better handle tasks requiring long chains of reasoning. Second, there will be a shift from text-based interaction to more natural multimodal approaches that combine different data types, such as text, images, and sound. Ultimately, these improvements will bring us closer to achieving the long-term goal of artificial general intelligence (AGI).

This forecast underscores the dynamic nature of LLM development. Companies and researchers worldwide are actively working to improve these models, and we can expect new breakthroughs in the coming years. This could lead to significant changes across various industries, from automation to healthcare.

For users, this means more intelligent and useful tools capable of solving complex problems and interacting with the world in a more natural way. For the industry, it opens new opportunities for innovation and creating new products and services. Ultimately, LLM development will continue to have a profound impact on our lives.