3 papers
cs.LG2026
Considering Context: When World Models Need Context Encoders
Oleg Smirnov, Sofiane Ennadir, John Pertoft +2
Methods for generalization in model-based reinforcement learning typically assume that an agent cannot recover the latent context governing the environment dynamics from its own ex…
cs.LG2025
Prompt-Tuning Bandits: Enabling Few-Shot Generalization for Efficient Multi-Task Offline RL
Finn Rietz, Oleg Smirnov, Sara Karimi +1
Prompting has emerged as the dominant paradigm for adapting large, pre-trained transformer-based models to downstream tasks. The Prompting Decision Transformer (PDT) enables large-…
cs.LG2025
Prompt Tuning Decision Transformers with Structured and Scalable Bandits
Finn Rietz, Oleg Smirnov, Sara Karimi +1
Prompt tuning has emerged as a key technique for adapting large pre-trained Decision Transformers (DTs) in offline Reinforcement Learning (RL), particularly in multi-task and few-s…