The State of LLMs in 2025: Advances, Challenges, and Future Predictions

As 2025 draws to a close now i’m thinking about the groundbreaking developments in Large Language Models (LLMs) and what the future looks like. This year has been marked by significant advancements in reasoning capabilities, Reinforcement Learning with Verifiable Rewards (RLVR), and the practical applications of LLMs, which makes a lot of things easier these days.

The year 2025 will indeed be remembered as the year of reasoning in LLMs, thanks to the work of DeepSeek’s R1 paper released in January. They introduced Reinforcement Learning with Verifiable Rewards (RLVR) and the GRPO algorithm, which significantly advanced the field and allowed LLMs to explain their answers, leading to improved accuracy. I noticed the DeepSeek R1 model was notable for its performance and the relatively low cost of training estimated at around $5 million, which challenged previous assumptions about the expenses involved in developing state‑of‑the‑art models.

LLM Focus Points

Over the years, the focus of LLM development has shifted significantly. In 2022, Reinforcement Learning with Human Feedback (RLHF) and Proximal Policy Optimization (PPO) took center stage. In 2023, Low-Rank Adaptation (LoRA) and supervised fine-tuning (SFT) were the key. By 2024, the emphasis was on mid-training techniques, such as synthetic data and domain-specific data. But by 2025, RLVR and GRPO became the focal points, enabling LLMs to develop more complex, reasoning-like behavior.

GRPO, the Research Darling of the Year

GRPO introduced in the DeepSeek R1 paper became a highlight of academic research in 2025. This algorithm, along with RLVR, offered a more efficient and cost-effective way to improve LLMs. I’ve seen researchers make numerous improvements to GRPO, which were adopted in state-of-the-art models like Olmo 3 and DeepSeek V3.2. These advancements have had a significant impact on the practical applications of LLMs.

LLM Architectures: A Fork in the Road?

While the decoder-style transformer remains the standard for state-of-the-art models there has been a shift towards mixture-of-experts (MoE) layers and efficiency-tweaked attention mechanisms. However, models like Qwen3-Next and Kimi Linear have introduced innovative architectures that scale linearly with sequence length. It seems the transformer architecture is expected to remain dominant for the foreseeable future, with continued improvements in efficiency and performance.

Inference-Scaling and Tool Use

Inference scaling and tool use have been crucial in improving LLM performance. Techniques like inference-time scaling—spending more time and computational resources during the answer generation phase—have shown significant promise. Additionally, integrating tool use into LLMs has helped reduce hallucinations and improve accuracy. For example, models can now call search-engine or calculator APIs to verify information and solve complex problems.

Benchmaxxing

The trend of benchmaxxing, or focusing on benchmark performance, has been prominent in 2025. While benchmarks are useful for evaluating LLMs they are not always indicative of real-world performance. Models like Llama 4 scored well on benchmarks but did not necessarily perform better in practical applications. This highlights the need for more comprehensive evaluation methods that consider the diverse tasks LLMs are used for.

AI for Coding, Writing, and Research

LLMs have had a significant impact on professions like coding, writing, and research. They serve as powerful tools that enhance productivity and reduce friction in day-to-day tasks. For instance, LLMs can spot issues in code, suggest improvements, and automate mundane tasks. However, they are not a replacement for human expertise and judgment. The key is to use LLMs as partners that augment human capabilities rather than replace them.

The Edge: Private Data

The use of private data in LLM development presents both challenges and opportunities. Proprietary data can give companies a competitive edge yet many are reluctant to share it with LLM developers. This has led to a growing trend of in-house model development that leverages private data, making LLM creation increasingly commoditized.

Building LLMs and Reasoning Models From Scratch

Sebastian Raschka shares his experiences in building LLMs and reasoning models from scratch. His work on the book “Build A Large Language Model (From Scratch)” and the upcoming “Build A Reasoning Model (From Scratch)” highlights the importance of understanding the fundamentals of LLM development. These resources have been valuable for educators, students, and engineers looking to implement custom LLMs in production.

Surprises in 2025 and Predictions for 2026

Looking back at 2025, several developments were surprising such as the rapid achievement of gold-level performance in major math competitions by reasoning models. Looking ahead to 2026, we can expect further advancements in RLVR, broader adoption of LLMs with local tool use, and better long-context handling. Improvements in tooling and inference-time scaling are also poised to drive much of the progress in LLM performance.

In conclusion, 2025 has demonstrated that LLMs are maturing into powerful reasoning partners. The innovations around RLVR and GRPO have made these models more efficient and cost-effective, while tool integration and scaling strategies are setting the stage for an even brighter 2026. It’s really going to be interesting to see where these advancements take us in the future.