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cs.LG2026
Where and What to Reward? Understanding Generalization Between Verifiable and Open-Ended Domains in Reinforcement Learning
Shangjian Yin, Yu Fu, Yue Dong +1
Reinforcement learning with verifiable rewards (RLVR) is widely used to improve the reasoning capability of language models, since model-generated answers can be easily verified by…
cs.LG2026
Reducing the Safety Tax in LLM Safety Alignment with On-Policy Self-Distillation
Yu Fu, Longxuan Yu, Haz Sameen Shahgir +4
Safety alignment often improves robustness to harmful queries at the cost of reasoning ability, a tradeoff known as the safety tax. A common cause is distributional mismatch: super…
cs.LG2025
MACE: A Hybrid LLM Serving System with Colocated SLO-aware Continuous Retraining Alignment
Yufei Li, Yu Fu, Yue Dong +1
Large language models (LLMs) deployed on edge servers are increasingly used in latency-sensitive applications such as personalized assistants, recommendation, and content moderatio…