Publications
============

This page lists papers and technical reports associated with **RLinf**. Detailed publication pages (with results and quickstart links) are linked below.

Detailed publication pages
--------------------------

.. toctree::
   :hidden:
   :maxdepth: 2

   rlinf_user
   rlinf_vla
   rlinf_co
   rlinf_system
   pi_rl
   wovr
   wideseek_r1

- :doc:`RLinf-USER <rlinf_user>` — Unified system for real-world online policy learning `arXiv:2602.07837 <https://arxiv.org/abs/2602.07837>`__
- :doc:`RLinf-VLA <rlinf_vla>` — Unified framework for VLA+RL training `arXiv:2510.06710 <https://arxiv.org/abs/2510.06710>`__
- :doc:`RLinf-Co <rlinf_co>` — Reinforcement learning-based sim-real co-training for VLA models `arXiv:2602.12628 <https://arxiv.org/abs/2602.12628>`__
- :doc:`RLinf <rlinf_system>` — Flexible and efficient RL system `arXiv:2509.15965 <https://arxiv.org/abs/2509.15965>`__
- :doc:`πRL <pi_rl>` — Online RL fine-tuning for flow-based VLA models `arXiv:2510.25889 <https://arxiv.org/abs/2510.25889>`__
- :doc:`WoVR <wovr>` — World model-based RL fine-tuning for VLA policies `arXiv:2602.13977 <https://arxiv.org/abs/2602.13977>`__
- :doc:`WideSeek-R1 <wideseek_r1>` — Exploring Width Scaling for Broad Information Seeking via Multi-Agent Reinforcement Learning `arXiv:2602.04634 <https://arxiv.org/abs/2602.04634>`__
