The State Of LLMs 2025: Progress, Problems, and Predictions

2025-07-19 · Source: Ahead of AI · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Advanced, extended

Summary

The year 2025 saw significant advancements in large language models (LLMs), particularly with the emergence of reasoning models, Reinforcement Learning with Verifiable Rewards (RLVR), and the GRPO algorithm. DeepSeek's R1 paper, released in January 2025, demonstrated that reasoning-like behavior could be developed using reinforcement learning, with the open-weight R1 model performing comparably to proprietary models. This also led to revised estimates for training costs, suggesting a 671B parameter DeepSeek V3 model could cost around $5 million, with an additional $294,000 for R1 training. RLVR, coupled with GRPO, became a dominant post-training method, enabling LLMs to learn complex problem-solving by using deterministic correctness labels. Other key trends included the convergence of open-weight LLMs on Mixture-of-Experts (MoE) layers and efficiency-tweaked attention mechanisms, increased focus on inference-time scaling for complex tasks, and the integration of tool use to mitigate hallucinations. The concept of "benchmaxxing" also became prevalent, where benchmark scores were often optimized to the detriment of real-world performance, highlighting the need for more robust evaluation methods.

Key takeaway

For CTOs and VPs of Engineering evaluating LLM development strategies, the shift towards reasoning models and RLVR/GRPO in 2025 indicates that pure scaling is no longer the sole path to advanced capabilities. You should prioritize integrating post-training methods like RLVR and explore inference-time scaling techniques to achieve high-accuracy performance on specialized tasks, rather than relying solely on benchmark scores. Additionally, consider developing in-house LLMs with proprietary data to gain a competitive edge as the technology commoditizes.

Key insights

2025 LLM progress was driven by reasoning models, RLVR, GRPO, and inference-time scaling, alongside architectural efficiencies.

Principles

• Reasoning models explain answers, improving accuracy.
• RLVR uses deterministic correctness labels for post-training.
• Inference scaling enhances LLM performance on complex tasks.

Method

DeepSeek R1 introduced Reinforcement Learning with Verifiable Rewards (RLVR) and the GRPO algorithm for post-training LLMs, using deterministic correctness labels (e.g., for math and code) to enable complex problem-solving and improve reasoning capabilities.

In practice

• Adopt RLVR and GRPO for post-training LLMs.
• Implement inference-time scaling for high-accuracy tasks.
• Integrate tool use to reduce LLM hallucinations.

Topics

• RLVR
• GRPO Algorithm
• LLM Architectures
• Inference Scaling
• LLM Evaluation

Code references

• inclusionAI/LLaDA2.0
• rasbt/LLMs-from-scratch

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Editorial summary, takeaway, and curation by AIssential. Original article published by Ahead of AI.