2025 LLM Year in Review
Summary
A review of 2025's LLM developments highlighting key paradigm shifts including Reinforcement Learning from Verifiable Rewards (RLVR), novel AI interaction models, and emerging AI application layers.
Review
The 2025 LLM landscape witnessed transformative changes in AI training and interaction methodologies. Notably, Reinforcement Learning from Verifiable Rewards (RLVR) emerged as a critical new training stage, enabling LLMs to develop more sophisticated reasoning strategies by optimizing against automatically verifiable rewards across complex environments like mathematical and coding challenges.
The year also marked a conceptual shift in understanding AI intelligence, moving away from biological analogies toward recognizing LLMs as fundamentally different 'summoned intelligences' with jagged, non-linear capabilities. Developments like Cursor's application layer, Claude Code's local agent model, and 'vibe coding' demonstrated expanding AI interaction paradigms, suggesting that future AI systems will be more contextually adaptive, locally integrated, and democratically accessible across various domains.
Key Points
- RLVR enabled more sophisticated AI reasoning through reward-based optimization
- LLMs demonstrate non-linear, 'jagged' intelligence across different domains
- New application layers are emerging that contextualize and specialize AI capabilities