New research shows RL may not help a model learn new basic skills (arxiv.org)

Recent research has cast new light on the effectiveness of reinforcement learning (RL) in enhancing reasoning abilities in language models. Despite previous advancements attributed to RL techniques, this study reveals that post-training does not necessarily extend a model's reasoning capabilities beyond what is acquired during pre-training. By establishing a controlled experimental framework, researchers were able to dissect the effects of pre-training, mid-training, and RL-based post-training on model performance. They found that RL significantly boosts capabilities only when pre-training has left sufficient developmental headroom and that tasks targeted during RL training must align with the model's near-competence. Moreover, the study highlights mid-training's critical yet often overlooked role, demonstrating that it can lead to substantial performance improvements with fixed computational resources. It further illustrates that minimal pre-training exposure is necessary for effective contextual generalization once RL is applied. The findings collectively emphasize the intricate interactions between training paradigms and lay the groundwork for refining reasoning language model training strategies, ultimately posing significant implications for AI and machine learning communities focused on developing more robust reasoning capabilities in models.