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Differentiable Information Enhanced Model-Based Reinforcement Learning
Differentiable environments have heralded new possibilities for learning control policies by offering rich differentiable information …
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Sequence to Sequence Reward Modeling: Improving RLHF by Language Feedback
Aligning the behavior of Large language models (LLMs) with human intentions and values remains a critical challenge. Reinforcement …
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Amulet: ReAlignment During Test Time for Personalized Preference Adaptation of LLMs
How to align large language models (LLMs) with user preferences from a static general dataset has been frequently studied. However, …
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Stream Aligner: Efficient Sentence-Level Alignment via Distribution Induction
The rapid advancement of large language models (LLMs) has led to significant improvements in their capabilities, but also to increased …
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JARVIS-1: Open-World Multi-task Agents with Memory-Augmented Multimodal Language Models
Achieving human-like planning and control with multimodal observations in an open world is a key milestone for more functional …
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Aligner: Efficient Alignment by Learning to Correct
With the rapid development of large language models (LLMs) and ever-evolving practical requirements, finding an efficient and effective …
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Panacea: Pareto Alignment via Preference Adaptation for LLMs
Current methods for large language model alignment typically use scalar human preference labels. However, this convention tends to …
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Neural Attention Field: Emerging Point Relevance in 3D Scenes for One-Shot Dexterous Grasping
One-shot transfer of dexterous grasps to novel scenes with object and context variations has been a challenging problem. While …
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Off-agent trust region policy optimization
Leveraging the experiences of other agents offers a powerful mechanism to enhance policy optimization in multi-agent reinforcement …
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