Tutorials on Direct Preference Optimization

Learn about Direct Preference Optimization from fellow newline community members!

  • React
  • Angular
  • Vue
  • Svelte
  • NextJS
  • Redux
  • Apollo
  • Storybook
  • D3
  • Testing Library
  • JavaScript
  • TypeScript
  • Node.js
  • Deno
  • Rust
  • Python
  • GraphQL
  • React
  • Angular
  • Vue
  • Svelte
  • NextJS
  • Redux
  • Apollo
  • Storybook
  • D3
  • Testing Library
  • JavaScript
  • TypeScript
  • Node.js
  • Deno
  • Rust
  • Python
  • GraphQL

Reinforcement Learning Examples Behind Modern LLMs: RLHF, RLAIF, and CodeRL in Practice

Supervised fine-tuning (SFT) trains a model on curated prompt-and-answer examples. It stops short once the model leaves the demo and meets real users. SFT gives the model examples to copy. It does not teach the model to rank competing answers, refuse unsafe requests, or handle prompts with multiple…
Thumbnail Image of Tutorial Reinforcement Learning Examples Behind Modern LLMs: RLHF, RLAIF, and CodeRL in Practice

Reinforcement Learning Applications for LLM Agents: RFT, DPO, and SFT Compared

Three methods, three data situations. Supervised fine‑tuning (SFT) copies labeled examples. Direct preference optimization (DPO) aligns to ranked “better vs worse” pairs. Reinforcement fine‑tuning (RFT) runs a reward loop that scores outputs and pushes toward the good ones. Most reinforcement…
Thumbnail Image of Tutorial Reinforcement Learning Applications for LLM Agents: RFT, DPO, and SFT Compared

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