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The icythat defined V (x)= sup V (x), 8x 2 X.

Building upon Miller's framework of explanations derived from research in multiple

We thank the reviewers for their comments. We then address reviewer's comments individually (due to space limits please zoom in the tiny figures). For [18] we used Alg. 2 We thank the reviewer for the additional reference, which we will add to the paper. Gradient Descent) applied in parallel to multiple starting points. We thank R2 for the reference "Entropic regularization of continuous optimal transport problems".

Convex Optimizationwithout Projection Steps.arXivmath.OC, August 2011.

Training an agent to achieve particular goals or perform desired behaviors is often accomplished through reinforcement learning, especially in the absence of expert demonstrations. However, supporting novel goals or behaviors through reinforcement learning requires the ad-hoc design of appropriate reward functions, which quickly becomes intractable. Toaddress thischallenge, wepropose Text-AwareDiffusion forPolicyLearning (TADPoLe), which uses apretrained, frozen text-conditioned diffusion model to compute dense zero-shot reward signals for text-aligned policy learning.

Please note that an "interaction" in this paper is a statistical interaction that describes a non-additive

A unique benefit to this approach is that each transition's TD error can be

Recently, there has been a renewed interest in using linear RNNs for efficient sequence modeling.

Weproveanewlower bound onthe competitive ratio of any online algorithm in the setting where the costs aremstrongly convex and the movement costs are the squared`2 norm. This lower bound shows that no algorithm can achieveacompetitiveratio that iso(m 1/2) asmtendstozero.