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cs.AI2026
DASH: Divergence-Adaptive Supervision Horizons for On-Policy Self-Distillation of Reasoning Models
ZhiYan Hou, Xinyu Tang, Hongyan An +9
Reinforcement learning with verifiable rewards (RLVR) improves the reasoning capabilities of large language models using automatically verifiable outcome signals, but these signals…
cs.AI2026
Don't Mix Rewards, Mix Policies: Policy Decomposition and Optimization for Multi-Reward RL
Ruiming Liang, Yi Zhong, Yizhen Yuan +6
Modern large language models (LLMs) are expected not just to answer correctly, but to adapt their behavior to different human values and use cases. As a result, multi-reward reinfo…
cs.AI2026
Rethinking Representativeness and Diversity in Dynamic Data Selection
Yuzhe Zhou, Zhenglin Hua, Haiyun Guo +1
Dynamic data selection accelerates training by sampling a changing subset of the dataset while preserving accuracy. We rethink two core notions underlying sample evaluation: repres…