Reinforcement Learning as a fine-tuning paradigm

Reinforcement Learning should be better seen as a “fine-tuning” paradigm that can add capabilities to general-purpose foundation models, rather than a paradigm that can bootstrap intelligence from scratch.

Deep reinforcement learning for engineering design through

5: GPT-3 Gets Better with RL, Hugging Face & Stable-baselines3, Meet Evolution Gym, Offline RL's Tailwinds, by Enes Bilgin, RL Agent

Fine-tuning 20B LLMs with RLHF on a 24GB consumer GPU

RLHF & DPO: Simplifying and Enhancing Fine-Tuning for Language Models

Prompting: Better Ways of Using Language Models for NLP Tasks

Fine-tuning 20B LLMs with RLHF on a 24GB consumer GPU

Google's Universal Pretraining Framework Unifies Language Learning

5: GPT-3 Gets Better with RL, Hugging Face & Stable-baselines3, Meet Evolution Gym, Offline RL's Tailwinds

paper-attachments.dropbox.com/s_03D8A88577B9611816

Multi-agent deep reinforcement learning: a survey

Prompting: Better Ways of Using Language Models for NLP Tasks

The AiEdge+: How to fine-tune Large Language Models with Intermediary models

5: GPT-3 Gets Better with RL, Hugging Face & Stable-baselines3, Meet Evolution Gym, Offline RL's Tailwinds

Machine Learning Paradigms - Introduction to Machine Learning

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