Summary of Reward Difference Optimization For Sample Reweighting in Offline Rlhf, by Shiqi Wang et al.
Reward Difference Optimization For Sample Reweighting In Offline RLHFby Shiqi Wang, Zhengze Zhang, Rui Zhao,…
Summary of A Comparison Of Llm Finetuning Methods & Evaluation Metrics with Travel Chatbot Use Case, by Sonia Meyer et al.
A Comparison of LLM Finetuning Methods & Evaluation Metrics with Travel Chatbot Use Caseby Sonia…
Summary of Model Surgery: Modulating Llm’s Behavior Via Simple Parameter Editing, by Huanqian Wang et al.
Model Surgery: Modulating LLM’s Behavior Via Simple Parameter Editingby Huanqian Wang, Yang Yue, Rui Lu,…
Summary of Predicting Vs. Acting: a Trade-off Between World Modeling & Agent Modeling, by Margaret Li et al.
Predicting vs. Acting: A Trade-off Between World Modeling & Agent Modelingby Margaret Li, Weijia Shi, Artidoro…
Summary of Towards Comprehensive Preference Data Collection For Reward Modeling, by Yulan Hu et al.
Towards Comprehensive Preference Data Collection for Reward Modelingby Yulan Hu, Qingyang Li, Sheng Ouyang, Ge…
Summary of Videoscore: Building Automatic Metrics to Simulate Fine-grained Human Feedback For Video Generation, by Xuan He et al.
VideoScore: Building Automatic Metrics to Simulate Fine-grained Human Feedback for Video Generationby Xuan He, Dongfu…
Summary of Aqulia-med Llm: Pioneering Full-process Open-source Medical Language Models, by Lulu Zhao et al.
Aqulia-Med LLM: Pioneering Full-Process Open-Source Medical Language Modelsby Lulu Zhao, Weihao Zeng, Xiaofeng Shi, Hua…
Summary of Toward Optimal Llm Alignments Using Two-player Games, by Rui Zheng et al.
Toward Optimal LLM Alignments Using Two-Player Gamesby Rui Zheng, Hongyi Guo, Zhihan Liu, Xiaoying Zhang,…
Summary of Regularizing Hidden States Enables Learning Generalizable Reward Model For Llms, by Rui Yang et al.
Regularizing Hidden States Enables Learning Generalizable Reward Model for LLMsby Rui Yang, Ruomeng Ding, Yong…
Summary of Learning Reward and Policy Jointly From Demonstration and Preference Improves Alignment, by Chenliang Li et al.
Learning Reward and Policy Jointly from Demonstration and Preference Improves Alignmentby Chenliang Li, Siliang Zeng,…