#RLHF

2026

• 04-14 EN

  Interpretable Preferences via Multi-Objective Reward Modeling and Mixture-of-Experts — Deep Technical Review

• 04-14 中

  ArmoRM：用“多目标奖励建模 + 混合专家门控”做可解释偏好学习——深度阅读笔记

• 04-07 EN

  ORPO: Monolithic Preference Optimization without Reference Model — In-Depth Technical Review

• 04-07 中

  ORPO：不用参考模型的一体化偏好优化 — 深度阅读笔记

• 03-31 EN

  Constitutional AI: Harmlessness from AI Feedback — In-Depth Technical Review

• 03-24 EN

  Proximal Policy Optimization Algorithms — In-Depth Technical Review

• 03-24 中

  近端策略优化算法（PPO）— 深度阅读笔记

• 03-12 EN

  PaRO: Smarter Partitioning for Distributed Training — Beyond ZeRO's One-Size-Fits-All

• 03-10 EN

  InstructGPT: The RLHF Recipe That Turned GPT-3 Into a Helpful Assistant

• 02-17 EN

  Direct Preference Optimization: Your Language Model Is Secretly a Reward Model — Technical Review