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Collections including paper arxiv:2406.09760

Self-alignment with DPO Implicit Rewards

Bootstrapping Language Models with DPO Implicit Rewards

Paper • 2406.09760 • Published Jun 14 • 37
sail/Llama-3-Base-8B-DICE-Iter1

Text Generation • Updated 12 days ago • 3
sail/Llama-3-Base-8B-DICE-Iter2

Text Generation • Updated 12 days ago • 2
sail/Zephyr-7B-DICE-Iter1

Text Generation • Updated 12 days ago • 1

Bootstrapping Language Models with DPO Implicit Rewards

Paper • 2406.09760 • Published Jun 14 • 37
DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence

Paper • 2406.11931 • Published Jun 17 • 54
Prism: A Framework for Decoupling and Assessing the Capabilities of VLMs

Paper • 2406.14544 • Published Jun 20 • 34
Instruction Pre-Training: Language Models are Supervised Multitask Learners

Paper • 2406.14491 • Published Jun 20 • 76

Bootstrapping Language Models with DPO Implicit Rewards

Paper • 2406.09760 • Published Jun 14 • 37
BPO: Supercharging Online Preference Learning by Adhering to the Proximity of Behavior LLM

Paper • 2406.12168 • Published Jun 18 • 7
WPO: Enhancing RLHF with Weighted Preference Optimization

Paper • 2406.11827 • Published Jun 17 • 14
Step-DPO: Step-wise Preference Optimization for Long-chain Reasoning of LLMs

Paper • 2406.18629 • Published 26 days ago • 37

DataComp-LM: In search of the next generation of training sets for language models

Paper • 2406.11794 • Published Jun 17 • 46
2BP: 2-Stage Backpropagation

Paper • 2405.18047 • Published May 28 • 21
Bootstrapping Language Models with DPO Implicit Rewards

Paper • 2406.09760 • Published Jun 14 • 37

WPO: Enhancing RLHF with Weighted Preference Optimization

Paper • 2406.11827 • Published Jun 17 • 14
Self-Improving Robust Preference Optimization

Paper • 2406.01660 • Published Jun 3 • 18
Bootstrapping Language Models with DPO Implicit Rewards

Paper • 2406.09760 • Published Jun 14 • 37
BPO: Supercharging Online Preference Learning by Adhering to the Proximity of Behavior LLM

Paper • 2406.12168 • Published Jun 18 • 7

about 20 hours ago

Understanding the performance gap between online and offline alignment algorithms

Paper • 2405.08448 • Published May 14 • 14
Self-Exploring Language Models: Active Preference Elicitation for Online Alignment

Paper • 2405.19332 • Published May 29 • 14
Offline Regularised Reinforcement Learning for Large Language Models Alignment

Paper • 2405.19107 • Published May 29 • 12
Show, Don't Tell: Aligning Language Models with Demonstrated Feedback

Paper • 2406.00888 • Published Jun 2 • 29

Learning from feedback dir

Suppressing Pink Elephants with Direct Principle Feedback

Paper • 2402.07896 • Published Feb 12 • 9
Policy Improvement using Language Feedback Models

Paper • 2402.07876 • Published Feb 12 • 5
Direct Language Model Alignment from Online AI Feedback

Paper • 2402.04792 • Published Feb 7 • 25
Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models

Paper • 2401.01335 • Published Jan 2 • 62

Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Paper • 2402.19427 • Published Feb 29 • 50
Simple linear attention language models balance the recall-throughput tradeoff

Paper • 2402.18668 • Published Feb 28 • 18
ChunkAttention: Efficient Self-Attention with Prefix-Aware KV Cache and Two-Phase Partition

Paper • 2402.15220 • Published Feb 23 • 18
Linear Transformers are Versatile In-Context Learners

Paper • 2402.14180 • Published Feb 21 • 6

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