RLHF Loops

Design and execute Reinforcement Learning from Human Feedback (RLHF) pipelines visually.

RLHF

Human-in-the-Loop

What is RLHF?

Reinforcement Learning from Human Feedback (RLHF) is a technique used to align large language models with human preferences. Instead of just predicting the next word, the model is penalized or rewarded based on human ratings of its responses.

Visual RLHF in Studio

Traditionally, setting up an RLHF pipeline requires complex Python scripts, a separate reward model, and PPO (Proximal Policy Optimization) integration. Langtrain Studio simplifies this:

•Reward Modeling: Train a reward model visually by providing pair-wise comparisons (Response A vs Response B).
•PPO Nodes: Drag a PPO node onto the canvas and connect your base model and reward model.
•Human-in-the-Loop: Seamlessly route low-confidence generations to a web interface where human annotators can rank the outputs.

DPO (Direct Preference Optimization)

In addition to PPO-based RLHF, Studio also supports DPO (Direct Preference Optimization). DPO eliminates the need for a separate reward model by directly updating the base model using preference data.

To use DPO in Studio, simply connect your Preference Dataset node directly to the DPO Optimization node.