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# Google AI Proposes PERL: A Parameter Efficient Reinforcement Learning Technique that can Train a Reward Model and RL Tune a Language Model Policy with LoRA

**[Machine Learning News](https://daily.dev/sources/mlnews)** · 4 min read · 0 upvotes · 0 comments

## Summary

Google introduces a revolutionary methodology called Parameter-Efficient Reinforcement Learning (PERL) that uses the LoRA technique to refine models more efficiently, reducing computational and memory requirements. PERL achieves similar outcomes as traditional RLHF methods but with significantly improved parameter efficiency.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.marktechpost.com/2024/03/22/google-ai-proposes-perl-a-parameter-efficient-reinforcement-learning-technique-that-can-train-a-reward-model-and-rl-tune-a-language-model-policy-with-lora/>

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

[View this post on daily.dev](https://daily.dev/posts/google-ai-proposes-perl-a-parameter-efficient-reinforcement-learning-technique-that-can-train-a-rew-frsirw4h0)

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