GPLD applies a row-wise Jacobian penalty to DreamerV3's posterior latent distribution, producing higher sample efficiency on DeepMind Control proprioceptive tasks.
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SuperIgor uses iterative co-training of a language model planner and a goal-conditional RL agent to self-generate and refine plans, resulting in stricter instruction adherence and better generalization to unseen instructions.
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Dreaming Smoothly and Sample Efficiently with Gradient Penalized Latent Dynamics
GPLD applies a row-wise Jacobian penalty to DreamerV3's posterior latent distribution, producing higher sample efficiency on DeepMind Control proprioceptive tasks.
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Self-Guided Plan Extraction for Instruction-Following Tasks with Goal-Conditional Reinforcement Learning
SuperIgor uses iterative co-training of a language model planner and a goal-conditional RL agent to self-generate and refine plans, resulting in stricter instruction adherence and better generalization to unseen instructions.