1 citations · 2 across the 31 of their papers we have counts for
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Revisiting On-policy Adversarial Black-Box Distillation: Calibrating Groupwise Reward Geometry for Effective Advantage Construction
Xiao Cui, Mo Zhu, Yulei Qin +3
Black-box distillation is a practical route for transferring capabilities from API-accessible large language models that expose only text outputs into smaller student models. Recen…
Goal Discovery with Causal Capacity for Efficient Reinforcement Learning
Yan Yu, Yaodong Yang, Zhengbo Lu +3
Causal inference is crucial for humans to explore the world, which can be modeled to enable an agent to efficiently explore the environment in reinforcement learning. Existing rese…
Bias Fitting to Mitigate Length Bias of Reward Model in RLHF
Kangwen Zhao, Jianfeng Cai, Jinhua Zhu +5
Reinforcement Learning from Human Feedback (RLHF) relies on reward models to align large language models with human preferences. However, RLHF often suffers from reward hacking, wh…
Disentangling Length Bias In Preference Learning Via Response-Conditioned Modeling
Jianfeng Cai, Jinhua Zhu, Ruopei Sun +4
Reinforcement Learning from Human Feedback (RLHF) has achieved considerable success in aligning large language models (LLMs) by modeling human preferences with a learnable reward m…