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A Gallery of Methods Beyond RL — Part I: Sampling Methods | Shengyu Feng

shengyu-feng.github.io · 2,119 words · saved by 2 readers

A tour of methods beyond reinforcement learning.

Reinforcement learning (RL) has become a dominant paradigm for optimizing modern machine learning systems, from AlphaGo to large language models (LLMs). Given an evaluable objective, the recipe is simple: treat the (negative) objective as a reward, sample solutions from a policy, and optimize the expected reward. This naturally raises a question: Is reinforcement learning the only way to optimize an objective? The answer is clearly no. Many methods approach the same problem from very different perspectives. In this series, I want to collect some of these ideas, including some of my own…

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