Papers

• PILAF: Optimal Human Preference Sampling for Reward Modeling (2025)
  Yunzhen Feng et al.
  1.28
• DRAGON: Distributional Rewards Optimize Diffusion Generative Models (2025)
  Yatong Bai et al.
  1.28
• RewardDance: Reward Scaling in Visual Generation (2025)
  Jie Wu et al.
  1.28
• On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes (2023)
  Rishabh Agarwal et al.
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• Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback (2023)
  Stephen Casper et al.
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• Efficient RLHF: Reducing the Memory Usage of PPO (2023)
  Michael Santacroce et al.
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• ICE-GRT: Instruction Context Enhancement by Generative Reinforcement based Transformers (2024)
  Chen Zheng et al.
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• RewardBench: Evaluating Reward Models for Language Modeling (2024)
  Nathan Lambert et al.
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• Measuring memorization in RLHF for code completion (2024)
  Aneesh Pappu et al.
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• Model Surgery: Modulating LLM's Behavior Via Simple Parameter Editing (2024)
  Huanqian Wang et al.
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