Summary of Gdpo: Learning to Directly Align Language Models with Diversity Using Gflownets, by Oh Joon Kwon et al.
GDPO: Learning to Directly Align Language Models with Diversity Using GFlowNetsby Oh Joon Kwon, Daiki…
Summary of Popalign: Diversifying Contrasting Patterns For a More Comprehensive Alignment, by Zekun Moore Wang et al.
PopAlign: Diversifying Contrasting Patterns for a More Comprehensive Alignmentby Zekun Moore Wang, Shawn Wang, Kang…
Summary of Increasing the Difficulty Of Automatically Generated Questions Via Reinforcement Learning with Synthetic Preference, by William Thorne et al.
Increasing the Difficulty of Automatically Generated Questions via Reinforcement Learning with Synthetic Preferenceby William Thorne,…
Summary of Codepmp: Scalable Preference Model Pretraining For Large Language Model Reasoning, by Huimu Yu et al.
CodePMP: Scalable Preference Model Pretraining for Large Language Model Reasoningby Huimu Yu, Xing Wu, Weidong…
Summary of Seeing Eye to Ai: Human Alignment Via Gaze-based Response Rewards For Large Language Models, by Angela Lopez-cardona and Carlos Segura and Alexandros Karatzoglou and Sergi Abadal and Ioannis Arapakis
Seeing Eye to AI: Human Alignment via Gaze-Based Response Rewards for Large Language Modelsby Angela…
Summary of The Phenomenology Of Machine: a Comprehensive Analysis Of the Sentience Of the Openai-o1 Model Integrating Functionalism, Consciousness Theories, Active Inference, and Ai Architectures, by Victoria Violet Hoyle
The Phenomenology of Machine: A Comprehensive Analysis of the Sentience of the OpenAI-o1 Model Integrating…
Summary of Elephant in the Room: Unveiling the Impact Of Reward Model Quality in Alignment, by Yan Liu et al.
Elephant in the Room: Unveiling the Impact of Reward Model Quality in Alignmentby Yan Liu,…
Summary of Just Say What You Want: Only-prompting Self-rewarding Online Preference Optimization, by Ruijie Xu et al.
Just Say What You Want: Only-prompting Self-rewarding Online Preference Optimizationby Ruijie Xu, Zhihan Liu, Yongfei…
Summary of Sequence to Sequence Reward Modeling: Improving Rlhf by Language Feedback, By Jiayi Zhou et al.
Sequence to Sequence Reward Modeling: Improving RLHF by Language Feedbackby Jiayi Zhou, Jiaming Ji, Juntao…
Summary of Minor Sft Loss For Llm Fine-tune to Increase Performance and Reduce Model Deviation, by Shiming Xie et al.
Minor SFT loss for LLM fine-tune to increase performance and reduce model deviationby Shiming Xie,…