2 papers
cs.LG2026
Quality Determines Direction, Length Shapes Magnitude: Length Control for Open-Ended Reinforcement Learning
Zijun Weng, Zhongan Bi, Xuanang Gao +5
Reinforcement learning (RL) changes not only what language models say, but also how much they say, often increasing response length at the cost of token efficiency. Controlling thi…
cs.CL2026
Prompt-Level Reward Specifications for Open-Ended Post-Training
Zijun Weng, Xiaohui Hu, Shuangyong Song +3
Open-ended post-training benefits from rewards that make prompt-specific success conditions explicit, rather than relying only on post-hoc scalar scores. In instruction following,…