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Found 6 papers, 5 papers with code
RLVF: Learning from Verbal Feedback without Overgeneralization
The diversity of contexts in which large language models (LLMs) are deployed requires the ability to modify or customize default model behaviors to incorporate nuanced requirements and preferences.
DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature
In this paper, we identify a property of the structure of an LLM's probability function that is useful for such detection.
Mismatched No More: Joint Model-Policy Optimization for Model-Based RL
Many model-based reinforcement learning (RL) methods follow a similar template: fit a model to previously observed data, and then use data from that model for RL or planning.
Model-based Reinforcement Learning
Reinforcement Learning (RL)
What Can I Do Here? Learning New Skills by Imagining Visual Affordances
In effect, prior data is used to learn what kinds of outcomes may be possible, such that when the robot encounters an unfamiliar setting, it can sample potential outcomes from its model, attempt to reach them, and thereby update both its skills and its outcome model.
DisCo RL: Distribution-Conditioned Reinforcement Learning for General-Purpose Policies
Contextual policies provide this capability in principle, but the representation of the context determines the degree of generalization and expressivity.
Contextual Imagined Goals for Self-Supervised Robotic Learning
When the robot's environment and available objects vary, as they do in most open-world settings, the robot must propose to itself only those goals that it can accomplish in its present setting with the objects that are at hand.
