Best of Reinforcement Learning — December 2025

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   Article

   Sebastian Raschka·15w

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

   A comprehensive 2025 review of large language model developments highlights reinforcement learning with verifiable rewards (RLVR) and the GRPO algorithm as the year's dominant training paradigm, following DeepSeek R1's breakthrough. Key trends include inference-time scaling, tool use integration, and architectural efficiency tweaks like mixture-of-experts and linear attention mechanisms. The analysis addresses benchmarking challenges ("benchmaxxing"), discusses practical LLM usage for coding and writing, and examines the shift toward domain-specific models with proprietary data. Predictions for 2026 emphasize RLVR expansion beyond math/code, increased inference optimization, and the emergence of diffusion models for low-latency tasks.

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2. 2
   Article

   Machine Learning Mastery·19w

   The Roadmap for Mastering Agentic AI in 2026

   A comprehensive learning path for building autonomous AI systems that can plan, reason, and act independently. Covers foundational mathematics and programming, machine learning fundamentals, autonomous agent architectures, specialization areas like robotics and workflow automation, deployment strategies using Docker and cloud platforms, and portfolio development. Includes curated resources from beginner prerequisites through advanced topics like multi-agent systems, transformer-based decision-making, and reinforcement learning with human feedback.

   57

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   Article

   Sebastian Raschka·18w

   From Random Forests to RLVR: A Short History of ML/AI Hello Worlds

   A chronological overview traces the evolution of beginner-friendly ML/AI examples from 2013 to 2025. Starting with Random Forests on Iris datasets and XGBoost on Kaggle competitions, it progresses through neural networks (MLPs, AlexNet), transformer models (DistilBERT, Llama 2 with LoRA), and culminates with reasoning models using RLVR on mathematical datasets. Each milestone reflects when methods became mainstream and accessible, often lagging years behind their initial publication due to tooling maturity and community adoption.

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