SDGD uses cost-conditioned classifier-free guidance plus reward guidance with feasible trajectory relabeling to generate safe high-reward trajectories that adapt to changing safety budgets in offline RL.
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Auto-CoT automatically generates and filters reasoning-enhanced demonstrations to improve in-context learning accuracy on complex reasoning tasks.
Non-linear transformers enable cross-domain generalization in in-context RL by representing value functions from different domains with shared weights inside a shared RKHS.
citing papers explorer
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Decoupled Guidance Diffusion for Adaptive Offline Safe Reinforcement Learning
SDGD uses cost-conditioned classifier-free guidance plus reward guidance with feasible trajectory relabeling to generate safe high-reward trajectories that adapt to changing safety budgets in offline RL.
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ACIL: Auto Chain of Thoughts for In-Context Learning
Auto-CoT automatically generates and filters reasoning-enhanced demonstrations to improve in-context learning accuracy on complex reasoning tasks.
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One for All: A Non-Linear Transformer can Enable Cross-Domain Generalization for In-Context Reinforcement Learning
Non-linear transformers enable cross-domain generalization in in-context RL by representing value functions from different domains with shared weights inside a shared RKHS.