arXiv Artificial Intelligence

Temporal Self-Imitation Learning

1 hour ago 30.09.2026 07:00 👁 1 views
Temporal Self-Imitation Learning

Quick summary

arXiv:2606.19752v3 Announce Type: replace-cross Abstract: Long-horizon policies trained with reinforcement learning can still achieve high return through inefficient interactions, while rare efficient behaviors discovered during training may be forgotten. We argue that temporal efficiency itself provides a source of self-supervision for reinforcement learning. We introduce Temporal Self-Imitation Learning (TSIL), a reinforcement learning framework that mines temporally efficient successful trajectories generated during learning and converts them into reusable supervision for future policy impr

Key takeaways

  • arXiv:2606.19752v3 Announce Type: replace-cross Abstract: Long-horizon policies trained with reinforcement learning can still achieve high return through inefficient interactions, while rare efficient behaviors discovered during training may be forgotten.
  • We argue that temporal efficiency itself provides a source of self-supervision for reinforcement learning.
  • We introduce Temporal Self-Imitation Learning (TSIL), a reinforcement learning framework that mines temporally efficient successful trajectories generated during learning and converts them into reusable supervision for future policy impr

Why it matters

“Temporal Self-Imitation Learning” may affect what data AI products can use and where accountability sits. Product teams should watch compliance duties, rights holders should watch enforcement, and users should watch transparency and appeal mechanisms.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗