Direct Preference Optimization: Your Language Model is Secretly a Reward Model
Paper
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2305.18290
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Published
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37
Papers - Fine-tuning - DPO
Refer to additional papers: https://link.springer.com/article/10.1007/s10994-014-5458-8 and https://link.springer.com/article/10.1007/BF00992696
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ICDPO: Effectively Borrowing Alignment Capability of Others via In-context Direct Preference Optimization
Paper • 2402.09320 • Published • 6 -
sDPO: Don't Use Your Data All at Once
Paper • 2403.19270 • Published • 31 -
Dueling RL: Reinforcement Learning with Trajectory Preferences
Paper • 2111.04850 • Published • 2 -
Learning Trajectory Preferences for Manipulators via Iterative Improvement
Paper • 1306.6294 • Published • 2 -
Deep reinforcement learning from human preferences
Paper • 1706.03741 • Published • 3 -
Learning Dynamic Robot-to-Human Object Handover from Human Feedback
Paper • 1603.06390 • Published • 2 -
Fine-Tuning Language Models from Human Preferences
Paper • 1909.08593 • Published • 2 -
Leverage the Average: an Analysis of KL Regularization in RL
Paper • 2003.14089 • Published • 2 -
Direct Preference Optimization of Video Large Multimodal Models from Language Model Reward
Paper • 2404.01258 • Published • 10 -
AutoWebGLM: Bootstrap And Reinforce A Large Language Model-based Web Navigating Agent
Paper • 2404.03648 • Published • 22 -
JetMoE: Reaching Llama2 Performance with 0.1M Dollars
Paper • 2404.07413 • Published • 32 -
Tango 2: Aligning Diffusion-based Text-to-Audio Generations through Direct Preference Optimization
Paper • 2404.09956 • Published • 10 -
Learn Your Reference Model for Real Good Alignment
Paper • 2404.09656 • Published • 79 -
OpenBezoar: Small, Cost-Effective and Open Models Trained on Mixes of Instruction Data
Paper • 2404.12195 • Published • 11 -
Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone
Paper • 2404.14219 • Published • 228 -
FLAME: Factuality-Aware Alignment for Large Language Models
Paper • 2405.01525 • Published • 16