Hugging Face outlines how reinforcement learning (RL) training in 2026 has evolved from preference-based rewards to result-oriented methods, emphasizing the growing use of verifiable rewards. The company's blog post details how leading labs are adopting GRPO, a method central to training coding agents, as a foundational approach. This shift reflects a broader trend in the industry, where models are now trained to solve reasoning tasks by executing checks rather than relying on human preferences. The post provides an overview of how these changes are shaping the development of advanced language models. Source: huggingface
The article highlights how companies like OpenAI and DeepSeek-R1 have moved RL from preference-based to result-based training. OpenAI's o1 announcement in September 2024 placed RL at the center of reasoning, stating that performance improves with more reinforcement learning and test-time compute. DeepSeek-R1 demonstrated this approach in January 2025 by showing how reasoning abilities can be incentivized through pure reinforcement learning. These developments mark a significant shift in how models are trained, focusing on executable outcomes rather than subjective preferences. Source: huggingface
The blog post also references the broader context of how RL has evolved since its initial use in RLHF for InstructGPT. The article explains how the field progressed from sampling fresh answers to learning directly from fixed preference pairs with DPO. It then details how the latest wave of RL focuses on executable rewards, with models like Qwen3 and Xiaomi's MiMo-7B using verifiable checks to improve training outcomes. The post underscores the importance of these changes in shaping the future of model training. Source: huggingface*
