The State of Reinforcement Learning for LLM Reasoning

Sebastian Raschka's comprehensive overview of RL-for-reasoning training explains why GPT-4.5 and Llama 4 received muted reactions — they lack explicit reasoning training — while models like o3 (10× more training compute than o1) and Claude's extended thinking demonstrate that post-training via RL still yields significant gains where raw scale does not. The article argues that reasoning-focused post-training is becoming standard practice in LLM pipelines, making it essential reading for developers integrating frontier models into their tools.