#RLHF

2026

• 06-23 EN

  Critique-GRPO: Advancing LLM Reasoning with Natural Language and Numerical Feedback

• 06-23 中

  Critique-GRPO：用自然语言批评反馈突破强化学习训练瓶颈

• 06-16 EN

  Back to Basics: Revisiting REINFORCE Style Optimization for RLHF (RLOO)

• 06-16 中

  回归基础：用 RLOO 重新思考 RLHF 中的策略梯度优化

• 06-13 EN

  ForeMoE: Micro-step-level MoE Load Balancing for RL Post-training via Routing Foresight

• 06-13 中

  ForeMoE：利用路由预见性实现 RL 后训练中 MoE 微步级负载均衡

• 06-02 EN

  REINFORCE++: Stabilizing Critic-Free Policy Optimization with Global Advantage Normalization

• 06-02 中

  REINFORCE++：用全局优势归一化稳定免批评家策略优化

• 05-26 EN

  SimPO: Simple Preference Optimization with a Reference-Free Reward

• 05-26 中

  SimPO：无需参考模型的简洁偏好优化

• 05-19 EN

  KTO: Model Alignment as Prospect Theoretic Optimization — Technical Blog Review

• 05-19 中

  KTO：把模型对齐看成「前景理论」优化 —— 阅读笔记

• 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