The LLM Triad: Tune, Prompt, Reward - Gradient Flow

As language models become increasingly common, it becomes crucial to employ a broad set of strategies and tools in order to fully unlock their potential. Foremost among these strategies is prompt engineering, which involves the careful selection and arrangement of words within a prompt or query in order to guide the model towards producing theContinue reading "The LLM Triad: Tune, Prompt, Reward"

Some Core Principles of Large Language Model (LLM) Tuning, by Subrata Goswami

Understanding RLHF for LLMs

Gradient Flow

Gradient Flow

Beyond Training Objectives: Interpreting Reward Model Divergence in Large Language Models

HOW TO USE RLHF TO FINE-TUNE YOUR DATA ON GOOGLE CLOUD PLATFORM USING LLAMA-2, by Nnaemeka Nwankwo

A Comprehensive Guide to fine-tuning LLMs using RLHF (Part-1)

A Comprehensive Guide to fine-tuning LLMs using RLHF (Part-1)

LLM Studies (Part 4) – Reinforcement Learning from Human Feedback (RLHF) – Sherman Wong

Damien Benveniste, PhD on LinkedIn: Fine-tuning an LLM may not be as trivial as we may think! Depending on…