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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.LG2026
Beyond Binary: Turning Partial Success into Dense Verifiable Rewards for Reinforcement Learning in Code Generation
Longwen Wang, Yirui Liu, Xuan'er Wu +6
Effective reward design is a central challenge in Reinforcement Learning (RL) for code generation. Mainstream test-suite-level outcome rewards enforce functional correctness but in…