ARMOR: Stabilizing On-Policy LLM RL with Off-Policy Anchor Samples
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-optimization, where models exploit training heuristics at the expense of generalizable reasoning. While reverse KL regularization is the standard defense against such degradation, our analysis reveals that it is often insufficient in this regime, as it fails to ensure comprehensive coverage of the reference distribution. To address this, we propose ARMOR (Anchor Rollout and Mixed Optimization for RL), a framework that shifts the paradigm from passive ...
| Authors | Kexin Huang, Junkang Wu, Jinda Lu et al. |
|---|---|
| Primary category | cs.LG |
| Published | 11 July 2026 |
| Last updated at source | 21 July 2026 |
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