reviews: 
Comments: The paper presents an imitation learning / offline RL method that combines recent insights into the stable training of neural networks with cross-entropy losses and KL divergence minimization. The resulting agent predicts action sequences and achieves strong performance on the common D4RL benchmarking dataset.
Strengths: The paper is mostly cleanly written and presents its insights well (with one notable exception below).

The experimental section is thorough, although I would encourage the authors to add some ablations.
Weaknesses: Overall, the presentation of that paper leaves me unsure how the components of the loss are actually computed. The cross entropy formulation requires the actions to be binned, e.g. with the two-hot or HL-Gauss methods presented in Farebrother et al. However, in those formulations, the output of the action network would be a categorical. In this paper, the network outputs a Gaussian, which would not allow one to compute a cross entropy loss.

Another interpretation is that the authors don't use a categorical transformation, in which case their objective is not a cross entropy objective. It is unclear whether $y(a_t)$ is the probability of action $a_t$ (which would be necessary for the cross entropy objective) or simply the normalized action itself? In this case, how do the authors deal with the fact that $a_t$ is multidimensional? Their objective is then also mostly an MLE objective weighted by the size of the action, which has no clear probabilistic interpretation.The introduction of cross entropy which does not mention probabilities at all makes me think this might be the case? 

From a theoretical angle, combining both a cross entropy loss and a KL minimization is odd, as both losses are mathematically equivalent, except for an additive constant which solely depends on the target. This means that adding a KL loss is simply equivalent to multiplying the cross entropy loss by 2. Writing out the two loss components together, this becomes somewhat obvious. $log (y(a_t))$ does not depend on the learned function, and the second part is equivalent to the (unweighted) cross entropy loss. When rearranging both losses, the added KL is equivalent to adding an offset of $\lambda$ to the return weighing factor.

While the paper talks about MLE, KL minimization (and therefore also cross-entropy) is closely related to MLE [1]. This makes the juxtaposition of the two somewhat confusing here. I assume the authors want to talk about the well-documented phenomenon that squared errors, corresponding to MLE under the assumption of a constant variance Gaussian, is an unsuitable loss for many neural network training setups. This should be clarified.

Reward weighted imitation learning has been proposed in the literature before, this should be acknowledged and differences discussed [2].

Similarly, predicting action sequences is well established in imitation learning, compare for example [3,4,5]. Again, this should be discussed.

3 seeds are well known to not result in statistically reliable results. Please follow established common practice here and report on a sufficiently large number of seeds. In addition, please report uncertainty estimates such as confidence or tolerance intervals. (compare [6] for advice)

All results seem to be reported on expert datasets. Given that the method is presented as an offline RL and not an IL method, it would be important to highlight if the learned policies can outperform the expert. I am unsure that the method would be able to exhibit relevant phenomena for strong offline RL such as trajectory stitching, given it's close reliance on IL objectives.

References:
[1] https://jaketae.github.io/study/kl-mle/
[2] Using reward-weighted imitation for robot reinforcement learning, J Peters, J Kober, 2009 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning
[3] Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware, Tony Z. Zhao, Vikash Kumar, Sergey Levine, Chelsea Finn, CORL 2023
[4] Diffusion Policy: Visuomotor Policy Learning via Action Diffusion, Cheng Chi, Zhenjia Xu, Siyuan Feng, Eric Cousineau, Yilun Du, Benjamin Burchfiel, Russ Tedrake, Shuran Song, IJRR 2023
[5]  RoboAgent - Towards Sample Efficient Robot Manipulation with Semantic Augmentations and Action Chunking,  Homanga Bharadhwaj, Jay Vakil, Mohit Sharma, Abhinav Gupta, Shubham Tulsiani, Vikash Kumar, ICRAA 2024
[6] https://arxiv.org/pdf/2304.01315
Recommendation: 3
Reviewer confidence: 4
Soundness/correctness: 1
Clarity: 3