Maximum Likelihood Reinforcement Learning
The episode analyzes the paper "Maximum Likelihood Reinforcement Learning," which addresses how current reinforcement learning focuses on average success while neglecting difficult tasks. The discussion covers the MaxRL framework, which uses improved sampling and reward normalization to achieve better scaling and more precise mathematical targets for reasoning.