Trace-based worlds and predictive representations simplify agent learning

Trace2Env reports improved long-horizon consistency and action validity across nine environments without reconstructing executable simulators, while MR.Q achieves multitask RL gains using predictive representations and value functions without explicit planning. These approaches challenge simulator- and world-model-heavy recipes, but their scalability, trace coverage, and cross-domain generalization remain open.