Trajectory First: A Curriculum for Discovering Diverse Policies

Cornelius V. Braun, Sayantan Auddy, Marc Toussaint; 7, 2026.

Presented at the Reinforcement Learning Conference (RLC), Montréal, Quebec, Canada, August 15–17, 2026.

Abstract

Being able to solve a task in diverse ways makes agents more robust to task variations and less prone to local optima. In this context, constrained diversity optimization has become a useful reinforcement learning (RL) framework for training a set of diverse agents in parallel. However, existing constrained-diversity RL methods often under-explore in complex tasks such as robot manipulation, resulting in limited behavioral diversity. We address this with a two-stage curriculum that introduces a spline-based trajectory prior as an inductive bias to produce diverse, high-reward behaviors in an initial stage, and then distills these behaviors into reactive, step-wise policies in a second stage. In our empirical evaluation, we provide novel insights into challenges of diversity-targeted training and show that our curriculum increases the diversity of learned skills while maintaining high task performance.

[abs][pdf]

BibTeX

@article{braun2026trajectory,
    title={Trajectory First: A Curriculum for Discovering Diverse Policies},
    author={Cornelius V. Braun and Sayantan Auddy and Marc Toussaint},
    journal={Reinforcement Learning Journal},
    volume={7},
    pages={},
    year={2026}
}