arxiv
PublishedJuly 14, 2026 at 4:00 AM
—neutral
ARMOR: Stabilizing On-Policy LLM RL with Off-Policy Anchor Samples
Publisher summary· verbatim
arXiv:2607.10481v1 Announce Type: cross Abstract: Reinforcement learning (RL) has significantly enhanced the reasoning capabilities of large language models (LLMs), yet the training process remains notoriously fragile. In this work, we investigate a critical source of this instability: over-optimiza
Stay posted· Newsletter
A 5-min weekly brief — top movers, price watch, story of the week.
Discussion
No replies yet. Be first.
Related coverage
More from ARXIV
arxivADS-C: Antidistillation Sampling for Classification19harxivBeyond a Joke: Multi-Angle Reasoning for Detecting and Explaining Harmful Humor in Memes19harxivFrom Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems19harxivA Formally Grounded ODRL Evaluator: Implementation and Comparison19hThe Bubble Brief
WEEKLYRead AI insights every Tuesday — top movers, new releases, story of the week.
Originally published on arxiv ↗