-
Contrastive Prefence Learning: Learning from Human Feedback without RL
Paper • 2310.13639 • Published • 24 -
RLAIF: Scaling Reinforcement Learning from Human Feedback with AI Feedback
Paper • 2309.00267 • Published • 47 -
A General Theoretical Paradigm to Understand Learning from Human Preferences
Paper • 2310.12036 • Published • 11 -
Deep Reinforcement Learning from Hierarchical Weak Preference Feedback
Paper • 2309.02632 • Published • 1
Collections
Discover the best community collections!
Collections including paper arxiv:2310.13639
-
Pearl: A Production-ready Reinforcement Learning Agent
Paper • 2312.03814 • Published • 14 -
Secrets of RLHF in Large Language Models Part II: Reward Modeling
Paper • 2401.06080 • Published • 24 -
Contrastive Prefence Learning: Learning from Human Feedback without RL
Paper • 2310.13639 • Published • 24 -
StepCoder: Improve Code Generation with Reinforcement Learning from Compiler Feedback
Paper • 2402.01391 • Published • 41
-
S-LoRA: Serving Thousands of Concurrent LoRA Adapters
Paper • 2311.03285 • Published • 28 -
Tailoring Self-Rationalizers with Multi-Reward Distillation
Paper • 2311.02805 • Published • 3 -
Ultra-Long Sequence Distributed Transformer
Paper • 2311.02382 • Published • 2 -
OpenChat: Advancing Open-source Language Models with Mixed-Quality Data
Paper • 2309.11235 • Published • 15
-
CLEX: Continuous Length Extrapolation for Large Language Models
Paper • 2310.16450 • Published • 9 -
Let's Synthesize Step by Step: Iterative Dataset Synthesis with Large Language Models by Extrapolating Errors from Small Models
Paper • 2310.13671 • Published • 18 -
Contrastive Prefence Learning: Learning from Human Feedback without RL
Paper • 2310.13639 • Published • 24 -
Tuna: Instruction Tuning using Feedback from Large Language Models
Paper • 2310.13385 • Published • 10
-
Let's Synthesize Step by Step: Iterative Dataset Synthesis with Large Language Models by Extrapolating Errors from Small Models
Paper • 2310.13671 • Published • 18 -
Contrastive Prefence Learning: Learning from Human Feedback without RL
Paper • 2310.13639 • Published • 24 -
SILC: Improving Vision Language Pretraining with Self-Distillation
Paper • 2310.13355 • Published • 6 -
Ranking LLM-Generated Loop Invariants for Program Verification
Paper • 2310.09342 • Published • 2
-
Moral Foundations of Large Language Models
Paper • 2310.15337 • Published • 1 -
Specific versus General Principles for Constitutional AI
Paper • 2310.13798 • Published • 2 -
Contrastive Prefence Learning: Learning from Human Feedback without RL
Paper • 2310.13639 • Published • 24 -
RLAIF: Scaling Reinforcement Learning from Human Feedback with AI Feedback
Paper • 2309.00267 • Published • 47
-
Contrastive Prefence Learning: Learning from Human Feedback without RL
Paper • 2310.13639 • Published • 24 -
RLAIF: Scaling Reinforcement Learning from Human Feedback with AI Feedback
Paper • 2309.00267 • Published • 47 -
Diffusion Model Alignment Using Direct Preference Optimization
Paper • 2311.12908 • Published • 47 -
RLHF-V: Towards Trustworthy MLLMs via Behavior Alignment from Fine-grained Correctional Human Feedback
Paper • 2312.00849 • Published • 8
-
Tuna: Instruction Tuning using Feedback from Large Language Models
Paper • 2310.13385 • Published • 10 -
Contrastive Prefence Learning: Learning from Human Feedback without RL
Paper • 2310.13639 • Published • 24 -
Teaching Language Models to Self-Improve through Interactive Demonstrations
Paper • 2310.13522 • Published • 11 -
Zephyr: Direct Distillation of LM Alignment
Paper • 2310.16944 • Published • 120