  The search for a general model that can operate seamlessly across multiple
domains remains a key goal in machine learning research. The prevailing
methodology in Reinforcement Learning (RL) typically limits models to a single
task within a unimodal framework, a limitation that contrasts with the broader
vision of a versatile, multi-domain model. In this paper, we present Jack of
All Trades (JAT), a transformer-based model with a unique design optimized for
handling sequential decision-making tasks and multi-modal data types. The JAT
model demonstrates its robust capabilities and versatility by achieving strong
performance on very different RL benchmarks, along with promising results on
Computer Vision (CV) and Natural Language Processing (NLP) tasks, all using a
single set of weights. The JAT model marks a significant step towards more
general, cross-domain AI model design, and notably, it is the first model of
its kind to be fully open-sourced at https://huggingface.co/jat-project/jat,
including a pioneering general-purpose dataset.
