Traditional interactive environments limit agents' intelligence growth with fixed

Point processes are widely used statistical tools for modeling event occurrence patterns within continuous spatio-temporal domains.

Socialbiasinmachine learning hasdrawnsignificant attention, withworkranging from demonstrations of bias in a multitude of applications, curating definitions of fairness for different contexts, to developing algorithms to mitigate bias. In natural language processing, gender bias has been shown to exist in context-free word embeddings. Recently, contextual word representations have outperformed word embeddings in several downstream NLP tasks.

This assumption means learning separate models for language-only, vision-only, and vision-language tasks, as opposed to a single "generalist" model that can handle all modalities or subsets of them [Reed et al., 2022]. Yet, existing work suggests that knowledge grounded in multiple modalities can benefit unimodal tasks [Desai and Johnson, 2021, Jin et al., 2022].

In this work, we significantly improve this framework by proposing an extremely compact parameter space for fine-tuning the generator.