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# REINFORCE and Actor-critic Methods in RL

**[Daily Dose of Data Science \| Avi Chawla \| Substack](https://daily.dev/sources/dailydoseofds)** · 2 min read · 0 upvotes · 0 comments

## Summary

Part 7 of a reinforcement learning course covering policy gradient methods from scratch. Topics include the REINFORCE algorithm, the log-derivative trick, variance problems, the advantage function, actor-critic architecture, generalized advantage estimation (GAE), and how LLMs are fine-tuned with RL techniques like RLHF, PPO, and GRPO. A hands-on comparison of REINFORCE vs. actor-critic on CartPole is included to illustrate variance differences in training curves.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://blog.dailydoseofds.com/p/reinforce-and-actor-critic-methods>

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Tags: [#python](https://daily.dev/tags/python), [#reinforcement-learning](https://daily.dev/tags/reinforcement-learning)

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