Overview

Preference Model builds RL environments that automate ML research and engineering. What it does: creates reinforcement learning environments that allow researchers and engineers to test and automate tasks in ML workflows. How it works: users interact with programmable environments where an RL agent can perform actions to progress ML experiments, with defined rewards, observations, and interfaces that map to common ML tasks like model training, hyperparameter tuning, or data processing. These environments can be run to automate repetitive research tasks and evaluate ideas at scale. How it differs from competitors: instead of offering general RL tools alone, it targets the automation of ML research and engineering processes, packaging ML tasks into reusable, standardized environments that streamline experimentation and comparison. Goal: to speed up ML research and engineering by providing ready-to-use, reusable RL environments that automate routine experiments and evaluations.

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