Reinforcement Learning for Hierarchical Reasoning Rewards: Minimax-Optimal Rates with Transformers
Reinforcement learning (RL) has become a standard tool for post-training language models on reasoning tasks, where the policy is updated by reward feedback while exploring the space of responses. Despite its empirical success, theoretical understanding of RL post-training remains limited, in particular of why on-policy...
Naoki Nishikawa, Taiji Suzuki
· 0 citations