Author

Guoyin Wang

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Preprint Jul 2026

ARMOR: Stabilizing On-Policy LLM RL with Off-Policy Anchor Samples

This work proposes ARMOR (Anchor Rollout and Mixed Optimization for RL), a framework that shifts the paradigm from passive penalty to active sample stabilization, enabling sustained performance improvements over extended training horizons.

Kexin Huang, Junkang Wu, Jinda Lu et al. · 0 citations

Jun 2026

Experience Augmented Policy Optimization for LLM Reasoning

This work proposes Experience-Augmented Policy Optimization (EAPO), which leverages a prior RL-optimized policy as an action-level experience prior and selectively injects experience at critical decision points during rollout to ensure stable and unbiased learning from experience-augmented rollouts.

Jinda Lu, Kexin Huang, Junkang Wu et al. · arXiv.org · 2 citations