arXiv Artificial Intelligence

Yes, Q-learning Helps Offline In-Context RL

3 hours ago 14.08.2026 07:00 👁 2 views
Yes, Q-learning Helps Offline In-Context RL

Quick summary

arXiv:2502.17666v5 Announce Type: replace-cross Abstract: Existing offline in-context reinforcement learning (ICRL) methods have predominantly relied on supervised training objectives, which are known to have limitations in offline RL settings. In this study, we explore the integration of RL objectives within an offline ICRL framework. Through experiments on more than 150 GridWorld and MuJoCo environment-derived datasets, we demonstrate that optimizing RL objectives directly improves performance by approximately 30% on average compared to widely adopted Algorithm Distillation (AD), across vari

Key takeaways

  • arXiv:2502.17666v5 Announce Type: replace-cross Abstract: Existing offline in-context reinforcement learning (ICRL) methods have predominantly relied on supervised training objectives, which are known to have limitations in offline RL settings.
  • In this study, we explore the integration of RL objectives within an offline ICRL framework.
  • Through experiments on more than 150 GridWorld and MuJoCo environment-derived datasets, we demonstrate that optimizing RL objectives directly improves performance by approximately 30% on average compared to widely adopted Algorithm Distillation (AD), across vari

Why it matters

“Yes, Q-learning Helps Offline In-Context RL” highlights the need for repeatable measurement rather than a single impressive demonstration. Independent validation across datasets and clearly stated limitations determine whether a result can guide product decisions.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗