Models
Datasets
Spaces
Docs
Enterprise
Pricing
Log In
Sign Up

Collections

Discover the best community collections!

Collections including paper arxiv:2406.16979

Reinforcement Learning (RL / RLHF)

RLHF Workflow: From Reward Modeling to Online RLHF

Paper • 2405.07863 • Published May 13, 2024 • 71
Understanding and Diagnosing Deep Reinforcement Learning

Paper • 2406.16979 • Published Jun 23, 2024 • 10
Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences

Paper • 2404.03715 • Published Apr 4, 2024 • 62
Iterative Nash Policy Optimization: Aligning LLMs with General Preferences via No-Regret Learning

Paper • 2407.00617 • Published Jun 30, 2024 • 7

Understanding and Diagnosing Deep Reinforcement Learning

Paper • 2406.16979 • Published Jun 23, 2024 • 10

reinforcement learning

In deep reinforcement learning, a pruned network is a good network

Paper • 2402.12479 • Published Feb 19, 2024 • 19
Stop Regressing: Training Value Functions via Classification for Scalable Deep RL

Paper • 2403.03950 • Published Mar 6, 2024 • 15
RLHF Workflow: From Reward Modeling to Online RLHF

Paper • 2405.07863 • Published May 13, 2024 • 71
OpenRLHF: An Easy-to-use, Scalable and High-performance RLHF Framework

Paper • 2405.11143 • Published May 20, 2024 • 40

Secrets of RLHF in Large Language Models Part II: Reward Modeling

Paper • 2401.06080 • Published Jan 11, 2024 • 28
Scaling Laws for Reward Model Overoptimization in Direct Alignment Algorithms

Paper • 2406.02900 • Published Jun 5, 2024 • 13
AgentGym: Evolving Large Language Model-based Agents across Diverse Environments

Paper • 2406.04151 • Published Jun 6, 2024 • 24
Understanding and Diagnosing Deep Reinforcement Learning

Paper • 2406.16979 • Published Jun 23, 2024 • 10

Understanding and Diagnosing Deep Reinforcement Learning

Paper • 2406.16979 • Published Jun 23, 2024 • 10

WPO: Enhancing RLHF with Weighted Preference Optimization

Paper • 2406.11827 • Published Jun 17, 2024 • 17
Self-Improving Robust Preference Optimization

Paper • 2406.01660 • Published Jun 3, 2024 • 20
Bootstrapping Language Models with DPO Implicit Rewards

Paper • 2406.09760 • Published Jun 14, 2024 • 41
BPO: Supercharging Online Preference Learning by Adhering to the Proximity of Behavior LLM

Paper • 2406.12168 • Published Jun 18, 2024 • 7

Diffusion World Model

Paper • 2402.03570 • Published Feb 5, 2024 • 8
Iterative Data Smoothing: Mitigating Reward Overfitting and Overoptimization in RLHF

Paper • 2401.16335 • Published Jan 29, 2024 • 1
Towards Efficient and Exact Optimization of Language Model Alignment

Paper • 2402.00856 • Published Feb 1, 2024 • 1
ODIN: Disentangled Reward Mitigates Hacking in RLHF

Paper • 2402.07319 • Published Feb 11, 2024 • 14

Reinforcement Learning (RL / RLHF)

RLHF Workflow: From Reward Modeling to Online RLHF

Paper • 2405.07863 • Published May 13, 2024 • 71
Understanding and Diagnosing Deep Reinforcement Learning

Paper • 2406.16979 • Published Jun 23, 2024 • 10
Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences

Paper • 2404.03715 • Published Apr 4, 2024 • 62
Iterative Nash Policy Optimization: Aligning LLMs with General Preferences via No-Regret Learning

Paper • 2407.00617 • Published Jun 30, 2024 • 7

Understanding and Diagnosing Deep Reinforcement Learning

Paper • 2406.16979 • Published Jun 23, 2024 • 10

Understanding and Diagnosing Deep Reinforcement Learning

Paper • 2406.16979 • Published Jun 23, 2024 • 10

WPO: Enhancing RLHF with Weighted Preference Optimization

Paper • 2406.11827 • Published Jun 17, 2024 • 17
Self-Improving Robust Preference Optimization

Paper • 2406.01660 • Published Jun 3, 2024 • 20
Bootstrapping Language Models with DPO Implicit Rewards

Paper • 2406.09760 • Published Jun 14, 2024 • 41
BPO: Supercharging Online Preference Learning by Adhering to the Proximity of Behavior LLM

Paper • 2406.12168 • Published Jun 18, 2024 • 7

reinforcement learning

In deep reinforcement learning, a pruned network is a good network

Paper • 2402.12479 • Published Feb 19, 2024 • 19
Stop Regressing: Training Value Functions via Classification for Scalable Deep RL

Paper • 2403.03950 • Published Mar 6, 2024 • 15
RLHF Workflow: From Reward Modeling to Online RLHF

Paper • 2405.07863 • Published May 13, 2024 • 71
OpenRLHF: An Easy-to-use, Scalable and High-performance RLHF Framework

Paper • 2405.11143 • Published May 20, 2024 • 40

Diffusion World Model

Paper • 2402.03570 • Published Feb 5, 2024 • 8
Iterative Data Smoothing: Mitigating Reward Overfitting and Overoptimization in RLHF

Paper • 2401.16335 • Published Jan 29, 2024 • 1
Towards Efficient and Exact Optimization of Language Model Alignment

Paper • 2402.00856 • Published Feb 1, 2024 • 1
ODIN: Disentangled Reward Mitigates Hacking in RLHF

Paper • 2402.07319 • Published Feb 11, 2024 • 14

Secrets of RLHF in Large Language Models Part II: Reward Modeling

Paper • 2401.06080 • Published Jan 11, 2024 • 28
Scaling Laws for Reward Model Overoptimization in Direct Alignment Algorithms

Paper • 2406.02900 • Published Jun 5, 2024 • 13
AgentGym: Evolving Large Language Model-based Agents across Diverse Environments

Paper • 2406.04151 • Published Jun 6, 2024 • 24
Understanding and Diagnosing Deep Reinforcement Learning

Paper • 2406.16979 • Published Jun 23, 2024 • 10

Company

TOS Privacy About Careers

Website

Models Datasets Spaces Pricing Docs