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Jinseong Jeong

Alumni

Publications

One Flow-Transformer for Imagination and Control
Rabiul Awal
Jinseong Jeong
Ankur Sikarwar
Parisa Kordjamshidi
Andrii Zadaianchuk
Sai Rajeswar
Paul Hongsuck Seo
Aishwarya Agrawal
Diffusion and flow models are effective world models for visual reinforcement learning, but existing agents treat them as black-box simulato… (voir plus)rs, leaving the backbone’s representations unused for control. We introduce DRIFT, an online agent in which a single Flow-Transformer serves as both world model and policy backbone, trained from scratch. We find that denoising features alone are suboptimal for control; DRIFT bridges this gap with a next-latent prediction objective that gives the backbone an explicit dynamics signal. Shortcut flow matching reduces imagination to a single denoising step per frame. Across Atari 100k, Craftium, and Crafter, DRIFT is competitive with both latent-dynamics and diffusion world-model baselines.
2026-07-30
WMW @ Reinforcement Learning Conference (publié)
openreview.net
openreview.net