Understanding Reasoning from Pretraining to Post-Training

Researchers utilized chess as a controlled testbed to investigate how pretraining choices influence the effectiveness of reinforcement learning (RL) in large language models. By systematically scaling models from 5M to 1B parameters, the study established a joint scaling law where a model's pretraining loss accurately predicts its subsequent RL performance. The findings reveal that extended pretraining not only provides a better starting point but also increases the speed at which a model improves during RL training. Mechanistic analysis showed that while RL amplifies correct moves on simple tasks, it can also surface previously hidden solutions on difficult problems. Furthermore...