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Found 6 papers, 5 papers with code
The Generalization Gap in Offline Reinforcement Learning
Our experiments reveal that existing offline learning algorithms struggle to match the performance of online RL on both train and test environments.
Understanding the Effects of RLHF on LLM Generalisation and Diversity
OOD generalisation is crucial given the wide range of real-world scenarios in which these models are being used, while output diversity refers to the model's ability to generate varied outputs and is important for a variety of use cases.
Stabilizing Unsupervised Environment Design with a Learned Adversary
As a result, we make it possible for PAIRED to match or exceed state-of-the-art methods, producing robust agents in several established challenging procedurally-generated environments, including a partially-observed maze navigation task and a continuous-control car racing environment.
Bottom Up Top Down Detection Transformers for Language Grounding in Images and Point Clouds
We propose a language grounding model that attends on the referential utterance and on the object proposal pool computed from a pre-trained detector to decode referenced objects with a detection head, without selecting them from the pool.
Language Modulated Detection and Detection Modulated Language Grounding in 2D and 3D Scenes
Object detectors are typically trained on a fixed vocabulary of objects and attributes that is often too restrictive for open-domain language grounding, where the language utterance may refer to visual entities in various levels of abstraction, such as a cat, the leg of a cat, or the stain on the front leg of the chair.
CoCoNets: Continuous Contrastive 3D Scene Representations
This paper explores self-supervised learning of amodal 3D feature representations from RGB and RGB-D posed images and videos, agnostic to object and scene semantic content, and evaluates the resulting scene representations in the downstream tasks of visual correspondence, object tracking, and object detection.
