5 citations · 6 across the 30 of their papers we have counts for
4 papers · 1 filter
GameSmith: Scaling Zero-Sum Self-Play with Synthesised Games
Weixian Waylon Li, Jiaxin Zhang, Han Zhou +4
Post-training large language models (LLMs) on two-player zero-sum games provides a verifiable reward, and the gains have been shown to transfer to general reasoning. Existing pipel…
Progressive Agent Skill Generation via Reinforcement Learning
Junhao Shen, Zhanqiu Zhang, Chengzhi Piao +2
Recent large language model agents often use external skills as modular procedural units that condition inference and improve complex task solving. Thus, automatically generating h…
Many Voices, One Reward: Multi-Role Rubric Generation for LLM Judging and Reward Modeling
Dazhi Fu, Jiuding Yang, Yiwen Guo +1
Reliable reward and preference signals are critical for evaluating and optimizing large language models on open-ended tasks. Rubric-based judges offer a transparent way to decompos…
Representation-Aware Advantage Estimation: Your Reward Model Provides More Than A Scalar Output
Guozheng Li, Xiyan Fu, Yiwen Guo
Current reinforcement learning from human feedback (RLHF) methods primarily rely on scalar rewards from a trained reward model (RM). While effective, scalar rewards are often noisy…