Summary

The article reflects on the significant advancements in large language models (LLMs) throughout 2025, highlighting key developments such as reasoning models, reinforcement learning with verifiable rewards (RLVR), and the GRPO algorithm. It discusses the evolving landscape of LLM architectures, the importance of inference scaling, and the challenges of benchmarking in the field. The author shares predictions for future trends in LLM development, emphasizing the need for continual learning and domain specialization. Overall, it provides a comprehensive overview of the state of LLMs and their implications for various industries.

Data Problems Addressed