Zhong et al. (2025a) introduced the Reinforced Token Optimization (RTO) framework, proving that modeling Reinforcement Learning from Human Feedback (RLHF) as a token-wise Markov Decision Process (MDP) is significantly more sample-efficient than the traditional contextual bandit formulation.

Authors

• Person: Not available Organization: arXiv
  A Survey on the Theory and Mechanism of Large Language Models

Sources

• A Survey on the Theory and Mechanism of Large Language Models arxiv.org arXiv via serper

Referenced by nodes (1)

• Reinforcement learning from human feedback (RLHF) concept