# Dongxi NLP explains RL terms like policy, advantage, and GAE through one example

> Original title: 干的像沙漠般的一篇，关于 RL Terms.

- Source: Dongxi NLP
- Published: 2026-10-28T10:31:27.000Z
- Bad Influence: https://badinfluence.news/items/xf1qekg3kkzbjjgzmntwl857y
- Original: https://x.com/dongxi_nlp/status/2115390983818944513

## Summary

Dongxi NLP says reinforcement learning jargon is hard to follow and presents a single worked example to explain terms including policy, state, action, reward, return, value, advantage, baseline, TD error, GAE, clipping, and KL for LLM readers. The post calls its own writing dry and says RL is hard to make lively.

## Post

干的像沙漠般的一篇，关于 RL Terms.

RL 术语很多，且不好理解，例如: 
policy, state, action 
rewardreturn, value, advantage
baseline, TD error, GAE
clipping, KL

这篇用一个例子把 RL 的术语写给 LLM 读者 （依然太干了，RL 真的很难写得生动有趣）

[Video](https://badinfluence.news/api/img-proxy?u=https%3A%2F%2Fpbs.twimg.com%2Famplify_video_thumb%2F2115389807945814017%2Fimg%2FDxQcX5jP-oHb6RZI.jpg&mode=thumb&exp=1793404800&sig=65d61b7ba8853ff4)

## Quoted post @dongxi_nlp

> https://x.com/i/article/2115385031132635138

https://x.com/dongxi_nlp/status/2115389565628305715